Bacterial Profile and Antimicrobial Resistance in Patients with Pulmonary Infection: A Retrospective Single-Center Study

Bacterial Profile and Antimicrobial Resistance in Patients with Pulmonary Infection: A Retrospective Single-Center Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bacterial Profile and Antimicrobial Resistance in Patients with Pulmonary Infection: A Retrospective Single-Center Study Jargaltulga Ulziijargal, Ekaterina Faermark, Usukhbayar Khenchbish, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9327263/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Background: Pulmonary infections remain a major cause of global morbidity and mortality, particularly in low- and middle-income countries. The rapid emergence of antimicrobial resistance, including multidrug-resistant, extensively drug-resistant, and pandrug-resistant organisms, has significantly limited therapeutic options and increased healthcare burden. This study aimed to investigate the bacterial profile and antimicrobial resistance patterns among patients with suspected pulmonary infections at a tertiary referral hospital in Mongolia. Methods: A retrospective descriptive study was conducted at Mongolia–Japan Hospital in Ulaanbaatar, from January 1, 2023, to December 31, 2024. Data were extracted from the electronic medical record system. Patients who underwent microbiological examination of lower respiratory tract specimens (sputum, bronchial washing, bronchoalveolar lavage) were included. Bacterial identification and antimicrobial susceptibility testing were performed using the VITEK-2 system in accordance with CLSI M100 guidelines. Resistance phenotypes were classified based on CDC and ECDC criteria. Statistical analysis was conducted using SPSS version 26, with chi-square testing applied for group comparisons. A p-value < 0.05 was considered statistically significant. Results: A total of 354 patients were included, with a balanced gender distribution and predominance of older adults. Bacterial pathogens were identified in 78.0% of cases. The most frequently isolated organisms were Klebsiella pneumoniae (36.2%), Acinetobacter baumannii (17.0%), methicillin-resistant Staphylococcus aureus (10.9%), and Pseudomonas aeruginosa (6.2%). Gram-negative bacteria predominated and demonstrated high resistance rates. Among K. pneumoniae isolates, 23% were MDR, 8% XDR, and 4% PDR. A. baumannii exhibited 14.9% MDR, 6.4% XDR, and 31.9% PDR. MDR was observed in 73.3% of MRSA isolates. Resistance rates were significantly higher in the Internal Medicine Department (MDR 39.7%) and ICU (XDR 37.1%) compared to the outpatient department ( p < 0.001 ). MDR was significantly associated with cardiovascular disease, chronic kidney disease, rheumatoid arthritis, diabetes mellitus, and hematological disorders ( p < 0.05 ). Mortality was significantly higher among patients with bacterial growth compared to culture-negative patients (18.8% vs. 2.9%, p < 0.05 ), with pneumonia being the leading cause of death. Conclusions: Gram-negative pathogens, particularly K. pneumoniae and A. baumannii , predominate in pulmonary infections and demonstrate alarming levels of MDR, XDR, and PDR phenotypes. High resistance rates in ICU and internal medicine settings highlight the urgent need for strengthened infection prevention and control measures and robust antimicrobial stewardship programs. Continuous surveillance, regular antibiogram updates, and multicenter prospective studies incorporating molecular resistance detection are essential to optimize empirical therapy and mitigate the growing burden of AMR in Mongolia. low respiratory tract infection multidrug-resistant bacteria Klebsiella pneumoniae Acinetobacter baumannii MRSA Introduction Respiratory tract infections continue to be among the primary causes of morbidity and mortality globally, with lower respiratory infections and bloodstream infections comprising the majority of cases [ 1 ]. As of 2021, lower respiratory tract infections ranked as the fifth leading cause of mortality globally [ 2 ]. Although the mortality rate attributed to this condition has decreased compared to the year 2000, an estimated 2.5 million deaths were still recorded worldwide in 2021 [ 2 ]. Over 85% of these deaths occur in low- and middle-income countries, highlighting significant disparities in access to healthcare services, as well as in diagnostic and therapeutic capacities. According to the 2025 report by the Health Development Center of Mongolia, an analysis of the ten-year average morbidity from 2014 to 2024 indicates that respiratory diseases rank second among the leading causes of outpatient morbidity and very top among the causes of morbidity for hospitalized patients in Mongolia [ 3 ]. During the COVID-19 pandemic, the distribution of respiratory tract infections shifted temporarily; however, subsequent secondary bacterial, viral, and fungal complications following SARS-CoV-2 infection have led to an overall increase in respiratory mortality [ 1 ]. According to the Global Antibiotic Resistance Surveillance Report 2025, eight common bacterial pathogens responsible for bloodstream infections, urinary tract infections, gastrointestinal infections, and urogenital infections—specifically Acinetobacter spp. , Escherichia coli , Klebsiella pneumoniae , Neisseria gonorrhoeae , non-typhoidal Salmonella spp. , Shigella spp. , Staphylococcus aureus , and Streptococcus pneumoniae exhibit elevated levels of antimicrobial resistance [ 4 ]. Five major pathogens - Staphylococcus aureus , Escherichia coli , Streptococcus pneumoniae , Klebsiella pneumoniae , and Pseudomonas aeruginosa - accounted for 54.9% of all deaths attributable to bacterial infections globally [ 1 ]. The organisms discussed herein are the primary etiological agents responsible for pneumonia, sepsis, and healthcare-associated infections. Respiratory infections constitute a substantial burden of respiratory morbidity and represent a major contributor to global mortality related to infectious diseases. This underscores the need for the ongoing refinement of infection control, diagnostic, and prevention strategies. Globally, antimicrobial resistance (AMR) has increased by 40% between 2018 and 2023, with disproportionately higher rates observed in low- and middle-income countries. This burden is particularly pronounced in settings with limited capacity for AMR surveillance, inadequate microbiological diagnostic infrastructure, and insufficient evaluation of targeted antimicrobial therapies. As of 2023, the global prevalence of antibiotic resistance is estimated to be 17.2%, with a range of 3.5% to 39.5%. In contrast, countries in the Western Pacific Region reported a prevalence of 9.1%, ranging from 2.1% to 25.4%. The prevalence of AMR is observed to increase annually at a steady rate of 5% to 15% globally [ 4 ]. This data underscores the growing challenge of AMR, highlighting the urgent need for continued surveillance, research, and intervention strategies. According to estimates from the World Bank, antimicrobial resistance is projected to impose an additional healthcare burden of up to 1 trillion USD by 2050 [ 5 ]. As the prevalence of antimicrobial resistance continues to rise, first-line treatment options are becoming increasingly limited. This situation is resulting in a growing dependence on intravenous antibiotic therapy in place of oral regimens. The escalating reliance on second-line and last-resort antibiotics is placing a significant burden on healthcare systems, increasing treatment costs, and posing a considerable risk of diminished therapeutic effectiveness [ 6 ]. The growing dependency on second-line and last-resort antibiotics has become increasingly noteworthy, as this trend exacerbates the healthcare burden, elevates costs, and presents significant risks associated with diminished therapeutic efficacy. Antimicrobial resistance poses one of the most significant threats to global public health advancement. In 2019, of the 4.95 million deaths recorded worldwide, approximately 1.27 million were directly attributable to AMR [ 7 ]. The increase in antimicrobial resistance has accelerated dramatically in recent years, with projections estimating that by 2050, around 10 million deaths annually will be caused by infections due to resistant pathogens. This anticipated mortality rate is expected to surpass that of malignant neoplasms, highlighting the urgent need for effective strategies to combat AMR [ 7 ]. Multidrug-resistant infections significantly limit therapeutic options, necessitating the combined administration of multiple antimicrobials, which often have heightened adverse effect profiles. This increases the risk of nosocomial transmission and compromises infection control systems [ 8 ]. The efficacy of antimicrobial agents remains essential for advanced medical interventions, including major surgical procedures, solid organ transplantation, and immunosuppressive therapies for oncological treatments [ 9 ]. High-level resistance significantly limits therapeutic options and fosters excessive reliance on broad-spectrum antibiotics, highlighting the urgent need for enhanced hospital infection control measures, refined antibiotic stewardship policies, and improved laboratory surveillance systems. Therefore, a comprehensive investigation of the microbial spectrum and antimicrobial resistance patterns among patients with pulmonary infections is essential for optimizing therapeutic outcomes and guiding evidence-based antimicrobial selection. Methods 1. Study design and setting A retrospective descriptive study was conducted at Mongolia–Japan Hospital, Ulaanbaatar, Mongolia. The study covered the period from January 1, 2023, to December 31, 2024, utilizing data extracted from the hospital’s electronic medical record (EMR) system. The investigation aimed to describe the bacterial profile and antimicrobial resistance patterns among patients with suspected pulmonary infections. 2. Study population and sample size The study population comprised patients who underwent microbiological examination of lower respiratory tract specimens — including sputum, bronchial washing (BW), and bronchoalveolar lavage (BAL). Both hospitalized and ambulatory patients with clinically suspected pulmonary infection were identified through the hospital’s EMR database. Patients with complete clinical and laboratory data who fulfilled the inclusion criteria were enrolled, irrespective of whether bacterial pathogens were isolated. As the study adopted a retrospective descriptive design, no a priori sample size estimation was performed. A total enumeration approach was employed, including all eligible patients during the study period, yielding a final sample of 354 cases. 3. Sample collection and laboratory processing 3.1. Sample collecting Sputum, BW, and BAL samples were collected from patients with suspected lower respiratory tract infections for analysis. For sputum sample collection, patients received the following instructions. The first morning's sputum sample was to be collected for testing. Patients were instructed to remove any dentures and oral appliances, thoroughly rinse their oral cavities with clean water, take a deep breath, and expectorate 2–5 ml of sputum through forceful coughing into a clean, wide-mouth, sterile single-use container. Patients were cautioned to avoid including saliva in the sample. Sputum samples should be delivered to the laboratory within 2 hours of collection. The quality of the sputum samples was assessed using the Q score evaluation criteria (Bartlett's criteria), and patients were instructed to provide repeat samples if the initial specimen did not meet the required standards [ 10 ]. Bronchial wash and lavage fluids were collected by experienced pulmonologists using a video bronchoscope under sterile conditions to minimize the risk of contamination with normal microbial flora from the upper respiratory tract. Bronchial washing was obtained by instilling 20–30 ml of sterile normal saline into a segmental or lobar bronchus, followed by immediate aspiration of the fluid. A minimum of 3–5 ml of the aspirated fluid was collected in a sterile container. BAL is performed by wedging the bronchoscope into a segmental bronchus and instilling 100 ml of sterile normal saline, and at least 5–10 ml of subsequent aliquots were submitted for analysis. All samples were transported in a cold box maintained at 2–8°C and delivered to the laboratory within 30–60 minutes following collection [ 10 ]. 3.2. Laboratory processing Upon receipt, specimens were logged and processed within a Biosafety Level 2 cabinet. All specimens were Gram-stained and examined microscopically to evaluate the ratio of polymorphonuclear leukocytes (PMNs) to squamous epithelial cells, as well as to assess bacterial morphology. Sputum specimens exhibiting more than 25 PMNs and fewer than 10 squamous epithelial cells per low-power field (LPF) were classified as acceptable quality and subsequently cultured. Specimens deemed unacceptable were rejected, and repeat specimen collection was recommended. 3.3. Bacterial isolation Quality-assured specimens were cultured and identified using a variety of culture media. Blood agar plates (BAP, CO₂) were utilized for the cultivation of aerobic bacteria. MacConkey agar (MAC, O₂) was employed for the isolation of Gram-negative bacteria. Sabouraud agar supplemented with chloramphenicol was used for fungal isolation, while mannitol salt agar facilitated the cultivation of Staphylococcus species . Additionally, chocolate agar (CHOC, CO₂) and Brain–Heart Infusion broth were also utilized in the culturing process. Bronchial washing and bronchoalveolar lavage samples were enriched in brain–heart infusion broth and incubated for 24 hours before subculture. A sterile disposable 1 µL loop was utilized for specimen transfer, employing a four-quadrant streaking technique on the culture media. All cultures were incubated at 35–37°C in a 5% CO₂ atmosphere for 24 hours. After incubation, colony-forming units (CFU) were quantified. In sputum cultures, bacterial growth of ≥ 10⁵ CFU/mL and in BAL samples of ≥ 10⁴ CFU/mL was deemed clinically significant. Isolates that met these thresholds were further characterized [ 11 , 12 ]. 3.4. Antimicrobial susceptibility testing A bacterial suspension equivalent to the 0.5 McFarland standard was prepared from cultured colonies. Identification of bacterial and fungal species, as well as antimicrobial susceptibility testing, was conducted using VITEK-2 analyzer ID/AST cards. Minimum inhibitory concentration (MIC) values were determined in accordance with CLSI M100 guidelines, and isolates were classified as susceptible, intermediate, or resistant based on established breakpoints. For quality control purposes, standard reference strains, including E. coli ATCC 25922, S. aureus ATCC 25923, and P. aeruginosa ATCC 27853, were employed throughout the testing process [ 13 , 14 ]. Microbial resistance was classified based on the number of antibiotics to which the organisms exhibited resistance, adhering to the classification systems established by the Centers for Disease Control and Prevention (CDC) and the European Centre for Disease Prevention and Control (ECDC). Isolates were categorized as non-resistant or susceptible (NR), multidrug-resistant (MDR), extensively drug-resistant (XDR), or pandrug-resistant (PDR) according to these standardized definitions [ 15 ]. 4. Statistical analysis Collected study data were analyzed using the Statistical Package for the Social Sciences, version 26. Descriptive statistics were summarized in tables and presented as percentages. Group comparisons were conducted using the chi-square test. Initially, the presence and absence of bacterial isolation were compared among patients with pulmonary diseases. Subsequently, the distribution of etiologic agents and antimicrobial resistance patterns among culture-positive groups was analyzed using percentages. A 95% confidence interval was applied, and a p-value of < 0.05 was considered statistically significant. Results The study included participants with a relatively equal gender distribution (male 47.7%, female 52.3%), and the majority were of advanced age. The predominant pathogens identified in this study were Klebsiella pneumoniae (36.2%), Acinetobacter baumannii (17.0%), methicillin-resistant Staphylococcus aureus (MRSA) (10.9%), and Pseudomonas aeruginosa (6.2%), which represented the most common causative agents of lower respiratory tract infections. High levels of multidrug resistance were observed among these isolates. Specifically, 31.9% of Acinetobacter baumannii , 8.0% of Klebsiella pneumoniae , 11.8% of Pseudomonas aeruginosa , and 73.3% of MRSA isolates were classified as multidrug-resistant. Table 1 Demographics n % Sex Male 169 47.7% Female 185 52.3% Age, median (IQR), y 61 (49–70) Height, median (IQR), cm 164 (158–168) Weight, median (IQR), kg 65 (55–78) BMI < 18.5 (underweight) 23 10.0% 18.5–24.9 (normal) 99 42.9% 25.0–29.9 (overweight) 72 31.2% 30 < Obese 37 16.0% Education No formal education 4 1.3% Primary education 5 1.6% Lower secondary education 3 1.0% Upper secondary education 142 45.7% Vocational or technical education 52 16.7% Associate degree / Diploma 105 33.8% Marital status Never married 27 8.6% Married 256 81.8% Cohabiting 7 2.2% Separated 1 0.3% Divorced 2 0.6% Widowed 20 6.4% Family member, median (IQR) 4 (2–5) Household type Dormitory 5 1.8% Traditional Mongolian ger / Yurt 26 9.2% Gated house 44 15.5% Apartment 209 73.6% Shared room / Rented room 0 0.0% Employment status Yes 79 24.7% No 70 21.9% Retired 171 53.4% Working conditions Normal 278 89.4% Physically demanding 24 7.7% Hazardous 9 2.9% Allergy 108 30.5% Smoking 104 29.4% Cumulative smoking, median (IQR), y 35 (20–45) If quit smoking: Total smoked, median (IQR), y 15 (5–40) Bacterial pathogens were identified in 78.0% of the study population, with no notable variation in the distribution of bacterial species across study groups. Acute exacerbation of chronic obstructive pulmonary disease (COPD) and bronchitis were the most frequently diagnosed conditions (57.6%), followed by pneumonia and lung abscess (33.9%). The rate of bacterial growth did not differ significantly among pulmonary disease categories ( p > 0.05 ). No significant association was observed between bacterial growth and most comorbidities. In contrast, haematological disorders were significantly more prevalent among patients with bacterial growth (5.1%) compared with culture-negative patients ( p 0.05 Outpatient care 212 59.9% 73 26.4% 29 37.2% General ward 102 28.8% 35 12.7% 5 6.4% ICU 40 11.3% 168 60.9% 44 56.4% Diagnosis Pneumonia and pulmonary abscess 120 33.9% 99 35.9% 21 26.9% > 0.05 COPD exacerbation and bronchitis 204 57.6% 159 57.6% 45 57.7% > 0.05 Bronchiectasis 39 11.0% 33 12.0% 6 7.7% > 0.05 Pulmonary fibrosis exacerbation 32 9.0% 22 8.0% 10 12.8% > 0.05 Comorbidities Cardiovascular disorders† 251 70.9% 193 69.9% 58 74.4% > 0.05 Chronic kidney disease 43 12.1% 31 11.2% 12 15.4% > 0.05 Liver disorders‡ 39 11.0% 32 11.6% 7 9.0% > 0.05 Diabetes mellitus 33 9.3% 26 9.4% 7 9.0% > 0.05 Rheumatoid arthritis 27 7.6% 18 6.5% 9 11.5% > 0.05 Neurological disorder 23 6.5% 21 7.6% 2 2.6% > 0.05 Hematologic disorder 14 4.0% 14 5.1% 0 0.0% 0.05 Respiratory failure 130 36.7% 96 34.8% 34 43.6% > 0.05 ARDS 38 10.7% 30 10.9% 8 10.3% > 0.05 Sepsis / Septicemia 27 7.6% 23 8.3% 4 5.1% > 0.05 Coma 15 4.2% 14 5.1% 1 1.3% > 0.05 Single organ failure 57 16.1% 47 17.0% 10 12.8% > 0.05 Multiple organ failure 27 7.6% 23 8.3% 4 5.1% > 0.05 Total mortality 22 15.1% 21 18.8% 1 2.9% < 0.05* Death due to lung disease 13 3.7% 13 4.7% 0 0.0% 0.05 COPD 1 0.3% 1 0.4% 0 0.0% > 0.05 Death due to non-pulmonary disease 16 4.5% 15 5.4% 1 1.3% > 0.05 ICU intensive care unit, COPD chronic obstructive pulmonary disease, ARDS acute respiratory distress syndrome † including arterial hypertension, ischemic heart disease, arrythmia and heart failure ‡ including chronic hepatitis and liver cirrhosis statistical markers (*p < 0.05, **p < 0.01, ***p < 0.001) Overall, 48.0% of patients developed at least one complication. Respiratory failure was the most common (36.7%), followed by single-organ failure (16.1%), acute respiratory distress syndrome (ARDS) (10.7%), multiple organ failure (7.6%), sepsis (7.6%), and coma (4.2%). The incidence of complications did not differ significantly between patients with and without bacterial growth ( p > 0.05 ). A total of 22 deaths (15.1%) were recorded. Mortality was significantly higher among patients with bacterial growth than among those without (18.8% vs 2.9%, p < 0.05 ). Pulmonary disease–related mortality was observed exclusively in the bacterial growth–positive group (4.7% vs 0%, p < 0.05 ), with pneumonia being the leading cause of death. Table 3 . Prevalence and distribution of bacterial antibiotic resistance Table 4. Resistance Group Classification N (%) Resistancy group PDR XDR MDR NR n (%) n (%) n (%) n (%) Klebsiella pneumoniae 100 (36.2) 4 (4.0) 8 (8.0) 23 (23.0) 4 (4.0) Acinetobacter baumannii 47 (17.0) 15 (31.9) 3 (6.4) 7 (14.9) 6 (12.8) MRSA 30 (10.9) 0 2 (6.7) 22 (73.3) 2 (6.7) Pseudomonas aeruginosa 17 (6.2) 2 (11.8) 2 (11.8) 3 (17.6) 2 (11.8) Escherichia coli 14 (5.1) 0 1 (7.1) 9 (64.3) 0 Enterococcus faecium 13 (4.7) 0 0 5 (38.5) 1 (7.7) Staphylococcus aureus 10 (3.6) 0 0 2 (20.0) 0 Sphingomonas paucimobilis 8 (2.9) 0 1 (12.5) 1 (12.5) 2 (25.0) Streptococcus pneumoniae 5 (1.8) 0 0 0 3 (60.0) Staphylococcus epidermidis 4 (1.4) 0 0 4 (100) 0 Stenotrophomonas maltophilia 3 (1.1) 1 (33.3) 0 0 2 (66.7) Streptococcus pseudoporcinus 2 (0.7) 0 0 1 (50.0) 1 (50.0) Staphylococcus haemolyticus 2 (0.7) 0 1 (50.0) 1 (50.0) 0 Enterococcus faecalis 2 (0.7) 0 0 0 0 Enterobacter cloacae 2 (0.7) 0 0 0 0 Acinetobacter lwoffii 2 (0.7) 0 0 0 1 (50.0) Raoultella planticola 2 (0.7) 0 0 0 0 Streptococcus gordonii 1 (0.4) 0 0 0 1 (100) Streptococcus salivarius 1 (0.4) 0 0 1 (100) 0 Streptococcus agalactiae 1 (0.4) 0 0 0 0 Staphylococcus pseudintermedius 1 (0.4) 0 1 (100) 0 0 Staphylococcus warneri 1 (0.4) 0 0 1 (100) 0 Viridans streptococci 1 (0.4) 0 0 1 (100) 0 Kocuria kristinae 1 (0.4) 0 0 0 0 Acinetobacter junii 1 (0.4) 0 0 0 0 Pseudomonas stutzeri 1 (0.4) 0 0 0 1 (100) Pseudomonas alcaligenes 1 (0.4) 0 0 0 1 (100) Pantoea spp 1 (0.4) 0 0 0 0 Ralstonia pickettii 1 (0.4) 0 0 0 0 Citrobacter brakii 1 (0.4) 0 0 0 0 Yersinia entercolitica 1 (0.4) 0 0 1 (100) 0 Rothia mucilaginosa 1 (0.4) 0 0 0 1 (100) Aeromonas sobria 1 (0.4) 0 0 0 1 (100) PDR pan drug-resistant, XDR extensively drug-resistant, MDR multidrug-resistant, NR non-resistant Among the identified causative pathogens, gram-negative bacteria demonstrated the highest prevalence, including K. pneumoniae , A. baumannii , P. aeruginosa species, and E. coli . These gram-negative organisms exhibited particularly high rates of MDR, XDR, and PDR. Concerning gram-positive bacteria, including MRSA, S. aureus , E. faecium , and S. epidermidis , the proportion demonstrating multidrug resistance was notably elevated. K. pneumoniae represented the highest proportion of all bacterial isolates at 36.2%, with predominant detection in the outpatient department (45.8%), which demonstrated statistically significant differences when compared to the internal medicine ward and ICU ( p < 0.05 ). Among K. pneumoniae isolates, 23% were classified as MDR, 8% as XDR, and 4% as PDR, indicating a substantial risk within the healthcare environment. A similar pattern was observed for A. baumannii (17% of total isolates), which exhibited a particularly high proportion of pandrug resistance (31.9%), with 14.9% classified as MDR and 6.4% as XDR. These isolates demonstrated elevated resistance to β-lactam antibiotics, cephalosporins, and carbapenems. P. aeruginosa and E. coli also displayed extensive resistance patterns, with 11.8% and 17.6% of P. aeruginosa isolates classified as XDR and MDR, respectively, while 64.3% of E. coli isolates were categorized as multidrug-resistant. High levels of resistance were observed among gram-positive bacteria. Among MRSA isolates, 73.3% were classified as multidrug-resistant, while an additional 6.7% exhibited extensive drug resistance. This indicates a persistent resistance to β-lactam antibiotics and other broad-spectrum antimicrobial agents across multiple classes. Multidrug resistance was particularly prominent among bacterial species, including Enterococcus faecium and Staphylococcus aureus . In comparison, specific bacterial species (such as Streptococcus pneumoniae , Staphylococcus epidermidis , and Stenotrophomonas maltophilia ) were detected in relatively low quantities; however, they exhibited significantly different patterns between non-resistant and multidrug-resistant classifications. Notably, the proportion of pandrug-resistant isolates of S. maltophilia was high, at 33.3%. Although S. maltophilia had a lower prevalence compared to predominant gram-negative bacteria such as Klebsiella pneumoniae (36.2%) and Acinetobacter baumannii (17.0%), the elevated rate of PDR isolates necessitates careful consideration. Table 5 Distribution of Patient Characteristics, Comorbidities, and Complications According to Antibiotic Resistance Categories Characteristics Resistancy group p value PDR EDR MDR NR n % n % n % n % Wards < 0.001*** General ward 9 12.3% 4 5.5% 29 39.7% 3 4.1% ICU 8 22.9% 13 37.1% 9 25.7% 1 2.9% Outpatient care 4 2.4% 1 0.6% 44 26.2% 24 14.3% Diagnosis Pneumonia and pulmonary abscess 12 12.1% 15 15.2% 24 24.2% 9 9.1% < 0.001*** COPD exacerbation and bronchitis 5 3.1% 7 4.4% 44 27.7% 19 11.9% 0.05 Pulmonary fibrosis exacerbation 2 9.1% 1 4.5% 7 31.8% 3 13.6% > 0.05 Comorbidities Cardiovascular disorders† 18 9.3% 16 8.3% 66 34.2% 17 8.8% < 0.01** Chronic kidney disease 9 29.0% 0 0.0% 14 45.2% 0 0.0% 0.05 Diabetes mellitus 5 19.2% 1 3.8% 11 42.3% 1 3.8% < 0.05* Rheumatoid arthritis 0 0.0% 5 27.8% 6 33.3% 3 16.7% 0.05 Hematologic disorder 4 28.6% 2 14.3% 5 35.7% 1 7.1% < 0.05* Presence of complications 17 13.2% 17 13.2% 42 32.6% 13 10.1% < 0.001*** Respiratory failure 8 8.3% 13 13.5% 35 36.5% 10 10.4% < 0.001*** ARDS 5 16.7% 10 33.3% 6 20.0% 2 6.7% < 0.001*** Sepsis / Septicemia 5 21.7% 6 26.1% 7 30.4% 2 8.7% 0.05 Single organ failure 10 21.3% 4 8.5% 21 44.7% 2 4.3% < 0.001*** Multiple organ failure 4 17.4% 1 4.3% 12 52.2% 2 8.7% < 0.05* ICU intensive care unit, COPD chronic obstructive pulmonary disease, ARDS acute respiratory distress syndrome PDR pan drug-resistant, XDR extensively drug-resistant, MDR multidrug-resistant, NR non-resistant † including arterial hypertension, ischemic heart disease, arrythmia and heart failure ‡ including chronic hepatitis and liver cirrhosis statistical markers (*p < 0.05, **p < 0.01, ***p < 0.001) When comparing antibiotic resistance rates across various departments, notably high levels were observed in the internal medicine department (MDR 39.7%) and ICU (XDR 37.1%). Additionally, the ICU exhibited a significant prevalence of both multidrug-resistant and pandrug-resistant isolates. Importantly, the non-resistant rates in these two departments were significantly lower than those in the outpatient department ( p < 0.001 ). Within diagnostic categories, notably high levels of MDR were observed in lung abscess (24.2%), as well as in exacerbations of COPD and bronchitis (27.7%), with statistically significant differences identified ( p < 0.001 ). In contrast, while bronchiectasis (30.3%) and pulmonary fibrosis exacerbation (31.8%) exhibited the highest proportions of MDR, comparisons of antibiotic resistance categories in these conditions did not reveal statistically significant differences. Multidrug-resistance was significantly more prevalent among patients with cardiovascular disease (30.3%), demonstrating a notable association between cardiovascular comorbidity and MDR ( p < 0.01 ). A higher prevalence of MDR was also observed in patients with chronic kidney disease (45.2%) and rheumatoid arthritis (33.3%), with strong statistical evidence of association ( p < 0.001 ). Extensively drug-resistant (EDR) isolates were more frequently identified in patients with rheumatoid arthritis (27.8%). In contrast, no EDR isolates were detected among patients with chronic kidney disease. Furthermore, patients with hematological disorders and diabetes mellitus exhibited significantly higher prevalence rates of both MDR and PDR phenotypes ( p < 0.05 ). Neurological disorders and liver disease were associated with increased proportions of MDR and EDR isolates. In terms of complications, multidrug-resistant infections exhibited the highest rate at 32.6% ( p < 0.001 ). When complications were categorized, patients experiencing respiratory failure demonstrated elevated MDR rates at 36.5%, while those with acute respiratory distress syndrome presented high XDR rates at 33.3% ( p < 0.001 ). Among patients with septic complications, all resistance categories, with the exception of non-resistant, showed increased rates ( p < 0.001 ). Conversely, in patients with complications arising from coma, the MDR rate was highest at 50%. Table 6 Comparative distribution of bacterial isolates in general ward, ICU, and outpatient care General ward ICU Outpatient care p value n % n % n % MRSA 12 16.4% 4 11.4% 14 8.3% > 0.05 Streptococcus gordonii 0 0.0% 0 0.0% 1 0.6% > 0.05 Streptococcus salivarius 0 0.0% 0 0.0% 1 0.6% > 0.05 Streptococcus pneumoniae 0 0.0% 1 2.9% 4 2.4% > 0.05 Streptococcus pseudoporcinus 0 0.0% 0 0.0% 2 1.2% > 0.05 Streptococcus agalactiae 1 1.4% 0 0.0% 0 0.0% > 0.05 Staphylococcus epidermidis 3 4.1% 0 0.0% 1 0.6% > 0.05 Staphylococcus pseudintermedius 0 0.0% 1 2.9% 0 0.0% 0.05 Staphylococcus aureus 6 8.2% 1 2.9% 3 1.8% < 0.05* Staphylococcus warneri 0 0.0% 1 2.9% 0 0.0% 0.05 Enterococcus faecium 1 1.4% 0 0.0% 12 7.1% > 0.05 Enterococcus faecalis 0 0.0% 0 0.0% 2 1.2% > 0.05 Kocuria kristinae 1 1.4% 0 0.0% 0 0.0% > 0.05 Klebsiella pneumoniae 11 15.1% 12 34.3% 77 45.8% 0.05 Escherichiacoli 5 6.8% 3 8.6% 6 3.6% > 0.05 Acinetobacter baumannii 14 19.2% 10 28.6% 23 13.7% > 0.05 Acinetobacter lwoffii 0 0.0% 0 0.0% 2 1.2% > 0.05 Acinetobacter junii 1 1.4% 0 0.0% 0 0.0% > 0.05 Pseudomonas aeruginosa 6 8.2% 2 5.7% 9 5.4% > 0.05 Pseudomonas stutzeri 0 0.0% 0 0.0% 1 0.6% > 0.05 Pseudomonas alcaligenes 0 0.0% 0 0.0% 1 0.6% > 0.05 Pantoea spp 0 0.0% 0 0.0% 1 0.6% > 0.05 Sphingomonas paucimobilis 6 8.2% 0 0.0% 2 1.2% 0.05 Raoultella planticola 2 2.7% 0 0.0% 0 0.0% > 0.05 Ralstonia pickettii 1 1.4% 0 0.0% 0 0.0% > 0.05 Citrobacter brakii 0 0.0% 0 0.0% 1 0.6% > 0.05 Yersinia entercolitica 0 0.0% 0 0.0% 1 0.6% > 0.05 Rothia mucilaginosa 0 0.0% 0 0.0% 1 0.6% > 0.05 Aeromonas sobria 0 0.0% 0 0.0% 1 0.6% > 0.05 statistical markers (*p < 0.05, **p < 0.01, ***p < 0.001) Discussion The most prevalent gram-negative bacteria identified were Klebsiella pneumoniae , Acinetobacter baumannii , Pseudomonas aeruginosa , Escherichia coli . Among the gram-positive bacteria, the most commonly isolated organisms were MRSA , Enterococcus faecium , Staphylococcus aureus , and Streptococcus pneumoniae . Our findings correspond with the principal prevalent pathogens were identified in various studies, including a retrospective analysis at a prominent teaching hospital in China [ 16 ], a retrospective study in Saudi Arabia [ 17 ] focusing on ICU and diverse infection types (including lower respiratory tract infections, bloodstream infections, and urinary tract infections), the World Health Organization (WHO) [ 4 ] and the Global Burden of Disease (GBD) study [ 7 ]. We observed that Gram-negative and some gram-positive bacteria showed high resistance to β-lactam, cephalosporin, and macrolide antibiotics. Acinetobacter baumannii had a pan-drug-resistant rate of 31.9%, MRSA showed 73.3% multidrug resistance, and Klebsiella pneumoniae had a 23% MDR rate—highlighting alarming resistance to broad-spectrum antibiotics. The primary mechanism underlying antibiotic resistance is the production of enzymes such as β-lactamases and carbapenemases by certain bacteria, which hydrolyze the β-lactam ring structure of antibiotics. These enzymes are particularly prevalent in species such as Klebsiella and Acinetobacter [ 18 ]. In MRSA , the mecA gene induces the production of PBP2a, which confers resistance to β-lactam antibiotics and accounts for a significant proportion of MDR isolates. Numerous studies have confirmed that increased synthesis of efflux pumps, inappropriate antibiotic usage, and inadequate infection control practices within healthcare settings contribute to the dissemination of MDR and XDR pathogens, exacerbating the issue of antibiotic resistance [ 18 , 19 ]. This study illustrates that antibiotic resistance patterns in both gram-negative and gram-positive bacteria are influenced by the interplay between antibiotic usage in healthcare settings and the intrinsic resistance mechanisms of these pathogens, leading to heightened levels of MDR, XDR, and PDR bacteria. In addition, Gram-negative pathogens such as Klebsiella pneumoniae (23%), Acinetobacter baumannii (14.9%), Pseudomonas aeruginosa (17.9%), and Escherichia coli (64.3%) exhibited multidrug resistance (MDR). A considerable proportion of these isolates also demonstrated extensively drug-resistant (XDR) and pan-drug-resistant (PDR) phenotypes [ 20 ]. Among Gram-positive bacteria, high rates of MDR were observed in MRSA (73.7%), Staphylococcus aureus (20%), and Enterococcus faecium (38.5%) [ 21 ]. The elevated prevalence of MDR, XDR, and PDR strains is closely associated with the frequent use of antibiotics in both outpatient and hospital settings, particularly in internal medicine wards and intensive care units [ 22 ]. This environment fosters the emergence of resistant strains against broad-spectrum agents such as β-lactams and fluoroquinolones. Furthermore, both intrinsic and acquired mechanisms of antimicrobial resistance in these pathogens have been well documented in numerous studies [ 23 , 24 ]. Inappropriate antibiotic use and insufficient regulation of prescribing practices remain fundamental drivers of bacterial resistance. Our study results indicated that community-acquired Klebsiella pneumoniae infections were identified at higher rates in outpatients, whereas relatively lower proportions were observed within the internal medicine department and ICU. This finding highlights distinct differences in the sources of infection and transmission patterns across these clinical settings. The elevated prevalence of K. pneumoniae infections in outpatients can be attributed to the organism's widespread presence in community environments and its ability to colonize both the respiratory and gastrointestinal tracts [ 25 ]. Moreover, outpatients are routinely managed with chronic underlying conditions, such as COPD and bronchiectasis, for which K. pneumoniae is a principal causative agent during episodes of infectious exacerbation [ 26 ]. The variation in bacterial resistance observed across different pulmonary conditions can be primarily attributed to the underlying lung diseases, prior antibiotic exposure, and environmental factors within healthcare settings. Chronic pulmonary conditions, such as bronchiectasis, COPD, and recurrent exacerbations, compromise mucociliary clearance and disrupt epithelial defense mechanisms in the airways, thereby facilitating the colonization by resistant organisms. Additionally, patients with severe lung disease often require repeated hospitalizations, broad-spectrum antibiotic therapy, and the use of mechanical ventilation, along with other invasive interventions, all of which contribute to the increased prevalence of multidrug-resistant and extensively drug-resistant pathogens. Structural alterations in the lungs and localized immunological deficiencies create favorable conditions for the survival and development of antibiotic resistance among gram-negative bacteria, including Acinetobacter baumannii , Pseudomonas aeruginosa , and Klebsiella pneumoniae . Consequently, these factors account for the diverse patterns of bacterial resistance observed across various pulmonary disease categories. The elevated rate of bacterial growth observed in patients with hematological diseases can predominantly be attributed to immunodeficiency and ongoing exposure to healthcare environments [ 27 ]. In conditions such as leukemia and bone marrow failure, both the quantity and functional capacity of neutrophils and lymphocytes are diminished, resulting in compromised innate immunity that facilitates bacterial growth and colonization [ 28 ]. Additionally, these patients frequently experience repeated hospitalizations, necessitate central venous catheter placements, and receive broad-spectrum antibiotic therapy, all of which significantly heighten the risk of acquiring multidrug-resistant and extensively drug-resistant bacterial infections [ 29 ]. Immunosuppressive therapies, hypogammaglobulinemia, and cytopenia further weaken the body's antibacterial immune response, ultimately fostering conditions conducive to increased bacterial proliferation [ 30 , 31 ]. In patients with chronic kidney disease, levels of resistance were markedly elevated across all categories, consistent with international research trends. This phenomenon arises because individuals with renal insufficiency often undergo medical interventions such as dialysis, which increases exposure to healthcare-associated pathogens [ 32 , 33 ]. Furthermore, since the majority of antibiotics are eliminated via the urinary tract, impaired excretion leads to prolonged exposure of causative organisms to these agents, thereby enhancing the likelihood of resistance development [ 34 ]. Among diabetic patients, the high prevalence of resistant infections (MDR 42.3%) is linked to weakened immune responses and the frequent occurrence of soft tissue and urinary tract infections. These infections, in turn, increase the likelihood of repeated antibiotic use in both outpatient and inpatient settings [ 35 ]. In patients with rheumatoid arthritis, elevated resistance levels are associated with the use of anti-inflammatory and immunosuppressive therapies [ 36 ]. The observation of significantly elevated bacterial resistance rates among patients with complications is a noteworthy finding. Specifically, in cases of sepsis and septicemia, multidrug resistance was identified in 31.7% of isolates, with extensively drug-resistant strains comprising 26.1%. Among patients with acute respiratory distress syndrome, the rate of multidrug resistance reached 30.3%, while those experiencing multiple organ failure exhibited the highest rate of multidrug resistance at 52.2%. Patients with complications are typically managed in ICU and internal medicine departments, where various factors converge to promote the prevalence of highly resistant organisms. These factors include the use of broad-spectrum antibiotics, the utilization of invasive medical devices, and prolonged treatment durations, all of which contribute to an environment conducive to the selection and persistence of bacteria with elevated resistance profiles [ 37 , 38 ]. Mongolia routinely reports the number of deaths attributable to antibiotic resistance to the international community. A review of the literature indicates that studies on pulmonary pathogens have predominantly concentrated on multidrug-resistant tuberculosis [ 39 – 41 ] and Streptococcus pneumoniae [ 42 ]. However, investigations into the bacteria associated with other pulmonary diseases and their corresponding antibiotic resistance patterns have not yet met international standards. This underscores the necessity for expanding this research and conducting annual studies in the future. Recent studies conducted in Vietnam have revealed that bacterial detection rates are significantly elevated during acute exacerbations of COPD. Furthermore, these studies frequently identify bacteria exhibiting resistance to multiple high-level antibiotics [ 43 ]. Additionally, research from China and Iran has indicated a notable prevalence of carbapenem-resistant Acinetobacter baumannii and Klebsiella pneumoniae in hospital-acquired respiratory tract infections [ 44 – 47 ]. The study results have direct applications in clinical practice across several critical areas. Firstly, they facilitate the updating of empirical antibiotic therapy protocols for various departments, ensuring alignment with the predominant pathogens and resistance levels specific to those settings. Secondly, the findings can be utilized to revise the hospital antibiogram, which serves as essential information for physicians' daily treatment decisions and plays a significant role in improving patient outcomes while reducing antibiotic resistance. Thirdly, the high prevalence of multidrug-resistant and pan-drug-resistant pathogens identified in ICU and internal medicine departments reflects elevated rates of healthcare-associated infections in these areas, underscoring the urgent need to enhance Infection Prevention and Control measures. This enhancement should include improving hand hygiene practices among healthcare workers, refining departmental disinfection protocols, strictly adhering to ventilator and catheter care bundles, and implementing patient isolation procedures when highly resistant pathogens are identified. Lastly, the results provide valuable insights for the improvement and refinement of the Antibiotic Stewardship Program, including the revision and establishment of an updated restricted antibiotic list for practical implementation in clinical settings. International organizations underscore the necessity of several strategic approaches to combat antibiotic resistance. First, the implementation of antibiotic stewardship programs is critical, alongside the provision of consistent guidance to promote appropriate medication practices in all healthcare settings. This includes ensuring correct dosage, duration, and selection of antibiotics while minimizing the overuse of broad-spectrum agents [ 48 ]. Second, enhanced and enforced infection control and prevention measures are essential, encompassing hand hygiene practices, disinfection protocols, patient isolation procedures, and vaccination initiatives [ 49 ]. Third, restricting antibiotic use in livestock and agricultural practices is vital to mitigate the transmission risk of resistance to human populations [ 50 – 53 ]. Finally, there is a compelling need for research and development of novel antibiotics and alternative therapeutic strategies, such as bacteriophage therapy and antimicrobial peptides [ 54 – 57 ]. The coordinated implementation of these measures across clinical, public health, and policy levels is crucial to curbing the spread of antibiotic resistance [ 58 ]. This study presents several significant limitations that should be acknowledged. First, as a retrospective, single-center investigation, caution is warranted when extrapolating findings to other healthcare settings in Mongolia, particularly to primary care facilities and rural areas where conditions may differ substantially. The reliance on electronic medical records for patient information may not fully capture specific clinical parameters, including detailed histories of prior antibiotic use, duration of therapy, and dosing information. Second, the study exclusively utilized culture-based results and did not incorporate molecular biological methods to validate resistance mechanisms at the genetic level, such as extended-spectrum beta-lactamases, carbapenemases, mecA, or NDM genes. This limitation restricts the capacity to elucidate the underlying causes of antibiotic resistance. Furthermore, certain pathogenic organisms, such as S. maltophilia and S. pneumoniae , were represented by relatively small case numbers, which limited the statistical power to accurately analyze resistance prevalence patterns within these groups. Third, cases yielding negative bacterial cultures were not systematically investigated for viral, fungal, or atypical pathogens, potentially resulting in gaps in understanding the complete etiological spectrum of pulmonary infections. Additionally, the study design did not permit a causal assessment of how clinical progression, treatment adjustments, and antibiotic substitutions influenced resistance outcomes. These limitations should be considered when interpreting the findings and their applicability to broader clinical contexts. This study identified that gram-negative bacteria, particularly Klebsiella pneumoniae , Acinetobacter baumannii , and Pseudomonas aeruginosa , are predominantly isolated from patients with pneumonia. These pathogens exhibit a high prevalence of multidrug-resistant, extensively drug-resistant, and pandrug-resistant strains. Among gram-positive bacteria, methicillin-resistant Staphylococcus aureus and Enterococcus faecium also demonstrate significant resistance, severely limiting therapeutic options. Consequently, the selection of initial treatment for pneumonia should be guided by the hospital's current antibiogram data to ensure that department-specific antibiotic prescribing policies align with local resistance patterns. Additionally, it is vital to strengthen infection control and antimicrobial stewardship programs. Moving forward, multicenter prospective studies and the establishment of routine molecular-level detection and surveillance will be essential for accurately defining the profiles of pneumonia pathogens and antibiotic resistance in Mongolia. List of abbreviations AMR antimicrobial resistance BAL bronchoalveolar lavage BW bronchial washing CDC Disease Control and Prevention COPD chronic obstructive pulmonary disease GBD the Global Burden of Disease ICU intensive care unit MDR multidrug-resistant MRSA methicillin-resistant Staphylococcus aureus NR non-resistant PDR pandrug-resistant WHO the World Health Organization XDR extensively drug-resistant Declarations Ethics approval and consent to participate: This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Research Ethics Review Committee of Mongolian National University of Medical Sciences (MNUMS) on April 8, 2025 (Approval No. 24-25/07-01). The need for informed consent from all participants was waived by the Clinical Research Subcommittee of the Mongolia–Japan Hospital of MNUMS on February 27, 2025. The committee authorized the study to be conducted at the Mongolia–Japan Hospital. Participants' registration data, demographic information, and laboratory test results were kept confidential and analyzed using a coded (blinded) approach. Consent for publication: Not applicable Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors state that there are no financial or non-financial conflicts of interest to disclose. Funding: No funding was received for this study. Authors' contributions: Conceptualization: Jargaltulga Ulziijargal; Ichinnorov Dashtseren Methodology: Ichinnorov Dashtseren; Jargaltulga Ulziijargal Data curation: Ekaterina Faermark; Usukhbayar Khenchbish; Tilyekbyergyen Bauyrjan; Laila Jukhai Formal analysis: Jargaltulga Ulziijargal; Ichinnorov Dashtseren Investigation: Amgalanzaya Erdenebaatar; Zesemdorj Otgon-Uul; Laila Jukhai Writing – original draft: Jargaltulga Ulziijargal; Ekaterina Faermark; Usukhbayar Khenchbish; Tilyekbyergyen Bauyrjan; Ichinnorov Dashtseren Writing – review & editing: Ichinnorov Dashtseren; Jargaltulga Ulziijargal Supervision: Zesemdorj Otgon-Uul; Ichinnorov Dashtseren Acknowledgements: The authors would like to express their sincere gratitude to the administration of Mongolian–Japanese Hospital for granting permission to conduct this study and for facilitating access to the hospital’s electronic medical record system. 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Cell Host Microbe. 2019;25:219–32. https://doi.org/10.1016/j.chom.2019.01.014 . Zhou Z, Li M, Fu H, Han Z, Wu Z, Fan H, et al. Biomaterial-driven innovations in phage therapy: Current strategies and future perspectives. Microbiol Res. 2026;302:128351. https://doi.org/10.1016/j.micres.2025.128351 . Muñoz-Egea M-C, Rodríguez A, Esteban J, García-Quintanilla M. Phage Therapy for Hospital-Acquired Respiratory Bacterial Infections: A Review. Open Respir Arch. 2026;8:100507. https://doi.org/10.1016/j.opresp.2025.100507 . Global action plan on antimicrobial resistance. https://www.who.int/publications/i/item/9789241509763 . Accessed 29 Dec 2025. Table 3 Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9327263","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622940235,"identity":"bf1a01e9-f8d3-4c1e-8ccf-678ae33e2802","order_by":0,"name":"Jargaltulga Ulziijargal","email":"","orcid":"","institution":"Mongolian National University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jargaltulga","middleName":"","lastName":"Ulziijargal","suffix":""},{"id":622940237,"identity":"7fe4c825-6d1a-4437-9004-0a027184bb37","order_by":1,"name":"Ekaterina Faermark","email":"","orcid":"","institution":"Mongolian National University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ekaterina","middleName":"","lastName":"Faermark","suffix":""},{"id":622940239,"identity":"a6eb85ac-e614-427f-91fe-e2bb6b92e3c4","order_by":2,"name":"Usukhbayar Khenchbish","email":"","orcid":"","institution":"Mongolian National University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Usukhbayar","middleName":"","lastName":"Khenchbish","suffix":""},{"id":622940240,"identity":"30eccae8-4999-4a5c-aa28-ac033f615b87","order_by":3,"name":"Tilyekbyergyen Bauyrjan","email":"","orcid":"","institution":"Mongolian National University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tilyekbyergyen","middleName":"","lastName":"Bauyrjan","suffix":""},{"id":622940241,"identity":"8dc91704-763e-4d82-a47a-c3fc74d386eb","order_by":4,"name":"Amgalanzaya Erdenebaatar","email":"","orcid":"","institution":"Mongolian National University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amgalanzaya","middleName":"","lastName":"Erdenebaatar","suffix":""},{"id":622940242,"identity":"1dc786d3-7041-4a5a-8ad0-1849e2cabec7","order_by":5,"name":"Laila Jukhai","email":"","orcid":"","institution":"Mongolian National University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Laila","middleName":"","lastName":"Jukhai","suffix":""},{"id":622940243,"identity":"9737c2a9-544a-4986-801d-64ee6618ea46","order_by":6,"name":"Zesemdorj Otgon-Uul","email":"","orcid":"","institution":"Mongolian National University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zesemdorj","middleName":"","lastName":"Otgon-Uul","suffix":""},{"id":622940244,"identity":"3c18192b-52aa-4247-b5a1-36d4cab3d9c4","order_by":7,"name":"Ichinnorov Dashtseren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACNgYehgMQJvMBmKABsVrYEojTwgDUAmPAVeLXwsd+9uDhwh02+fwSOV83F7Zts2dgb94mwZhzB7fDePISDs88k2Y5c0buttsz224nNvAcK5Ng3PYMtxYJHoPDvG2HDQzOnN12m7ftdgKDRI4ZUMthYrSceQbSYs8g/4ZYLcd72EBaGBskeAho4ckBaUkzkGxvM7vNc+52YhtPWrFFIh4t8u1njD/zttkY8DMzP7vNU3bbnp/98MYbH/FowWIviEggQcMoGAWjYBSMAkwAADZOT5p/zQ4uAAAAAElFTkSuQmCC","orcid":"","institution":"Mongolian National University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ichinnorov","middleName":"","lastName":"Dashtseren","suffix":""}],"badges":[],"createdAt":"2026-04-05 15:38:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9327263/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9327263/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107488760,"identity":"8d974ce4-7dce-4f8a-b3f2-98b77f4da29b","added_by":"auto","created_at":"2026-04-22 02:45:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1243470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9327263/v1/bd267042-6da1-4604-b081-8ad73878df33.pdf"},{"id":107465482,"identity":"3f26575e-1d47-49a5-983a-c0b247a13722","added_by":"auto","created_at":"2026-04-21 18:20:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37816,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9327263/v1/89b25b68035916ad9fa88a9e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bacterial Profile and Antimicrobial Resistance in Patients with Pulmonary Infection: A Retrospective Single-Center Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRespiratory tract infections continue to be among the primary causes of morbidity and mortality globally, with lower respiratory infections and bloodstream infections comprising the majority of cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As of 2021, lower respiratory tract infections ranked as the fifth leading cause of mortality globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although the mortality rate attributed to this condition has decreased compared to the year 2000, an estimated 2.5\u0026nbsp;million deaths were still recorded worldwide in 2021 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Over 85% of these deaths occur in low- and middle-income countries, highlighting significant disparities in access to healthcare services, as well as in diagnostic and therapeutic capacities. According to the 2025 report by the Health Development Center of Mongolia, an analysis of the ten-year average morbidity from 2014 to 2024 indicates that respiratory diseases rank second among the leading causes of outpatient morbidity and very top among the causes of morbidity for hospitalized patients in Mongolia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. During the COVID-19 pandemic, the distribution of respiratory tract infections shifted temporarily; however, subsequent secondary bacterial, viral, and fungal complications following SARS-CoV-2 infection have led to an overall increase in respiratory mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the Global Antibiotic Resistance Surveillance Report 2025, eight common bacterial pathogens responsible for bloodstream infections, urinary tract infections, gastrointestinal infections, and urogenital infections\u0026mdash;specifically \u003cem\u003eAcinetobacter spp.\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eNeisseria gonorrhoeae\u003c/em\u003e, \u003cem\u003enon-typhoidal Salmonella spp.\u003c/em\u003e, \u003cem\u003eShigella spp.\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, and \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e exhibit elevated levels of antimicrobial resistance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Five major pathogens - \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e - accounted for 54.9% of all deaths attributable to bacterial infections globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The organisms discussed herein are the primary etiological agents responsible for pneumonia, sepsis, and healthcare-associated infections. Respiratory infections constitute a substantial burden of respiratory morbidity and represent a major contributor to global mortality related to infectious diseases. This underscores the need for the ongoing refinement of infection control, diagnostic, and prevention strategies.\u003c/p\u003e \u003cp\u003eGlobally, antimicrobial resistance (AMR) has increased by 40% between 2018 and 2023, with disproportionately higher rates observed in low- and middle-income countries. This burden is particularly pronounced in settings with limited capacity for AMR surveillance, inadequate microbiological diagnostic infrastructure, and insufficient evaluation of targeted antimicrobial therapies. As of 2023, the global prevalence of antibiotic resistance is estimated to be 17.2%, with a range of 3.5% to 39.5%. In contrast, countries in the Western Pacific Region reported a prevalence of 9.1%, ranging from 2.1% to 25.4%. The prevalence of AMR is observed to increase annually at a steady rate of 5% to 15% globally [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This data underscores the growing challenge of AMR, highlighting the urgent need for continued surveillance, research, and intervention strategies. According to estimates from the World Bank, antimicrobial resistance is projected to impose an additional healthcare burden of up to 1 trillion USD by 2050 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs the prevalence of antimicrobial resistance continues to rise, first-line treatment options are becoming increasingly limited. This situation is resulting in a growing dependence on intravenous antibiotic therapy in place of oral regimens. The escalating reliance on second-line and last-resort antibiotics is placing a significant burden on healthcare systems, increasing treatment costs, and posing a considerable risk of diminished therapeutic effectiveness [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The growing dependency on second-line and last-resort antibiotics has become increasingly noteworthy, as this trend exacerbates the healthcare burden, elevates costs, and presents significant risks associated with diminished therapeutic efficacy.\u003c/p\u003e \u003cp\u003eAntimicrobial resistance poses one of the most significant threats to global public health advancement. In 2019, of the 4.95\u0026nbsp;million deaths recorded worldwide, approximately 1.27\u0026nbsp;million were directly attributable to AMR [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The increase in antimicrobial resistance has accelerated dramatically in recent years, with projections estimating that by 2050, around 10\u0026nbsp;million deaths annually will be caused by infections due to resistant pathogens. This anticipated mortality rate is expected to surpass that of malignant neoplasms, highlighting the urgent need for effective strategies to combat AMR [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultidrug-resistant infections significantly limit therapeutic options, necessitating the combined administration of multiple antimicrobials, which often have heightened adverse effect profiles. This increases the risk of nosocomial transmission and compromises infection control systems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The efficacy of antimicrobial agents remains essential for advanced medical interventions, including major surgical procedures, solid organ transplantation, and immunosuppressive therapies for oncological treatments [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHigh-level resistance significantly limits therapeutic options and fosters excessive reliance on broad-spectrum antibiotics, highlighting the urgent need for enhanced hospital infection control measures, refined antibiotic stewardship policies, and improved laboratory surveillance systems. Therefore, a comprehensive investigation of the microbial spectrum and antimicrobial resistance patterns among patients with pulmonary infections is essential for optimizing therapeutic outcomes and guiding evidence-based antimicrobial selection.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e1. Study design and setting\u003c/p\u003e\n\u003cp\u003eA retrospective descriptive study was conducted at Mongolia\u0026ndash;Japan Hospital, Ulaanbaatar, Mongolia. The study covered the period from January 1, 2023, to December 31, 2024, utilizing data extracted from the hospital\u0026rsquo;s electronic medical record (EMR) system. The investigation aimed to describe the bacterial profile and antimicrobial resistance patterns among patients with suspected pulmonary infections.\u003c/p\u003e\n\u003cp\u003e2. Study population and sample size\u003c/p\u003e\u003cp\u003eThe study population comprised patients who underwent microbiological examination of lower respiratory tract specimens \u0026mdash; including sputum, bronchial washing (BW), and bronchoalveolar lavage (BAL). Both hospitalized and ambulatory patients with clinically suspected pulmonary infection were identified through the hospital\u0026rsquo;s EMR database. Patients with complete clinical and laboratory data who fulfilled the inclusion criteria were enrolled, irrespective of whether bacterial pathogens were isolated.\u003c/p\u003e \u003cp\u003eAs the study adopted a retrospective descriptive design, no a priori sample size estimation was performed. A total enumeration approach was employed, including all eligible patients during the study period, yielding a final sample of 354 cases.\u003c/p\u003e\n\u003ch3\u003e3. Sample collection and laboratory processing\u003c/h3\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. \u003cem\u003eSample collecting\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eSputum, BW, and BAL samples were collected from patients with suspected lower respiratory tract infections for analysis.\u003c/p\u003e \u003cp\u003eFor sputum sample collection, patients received the following instructions. The first morning's sputum sample was to be collected for testing. Patients were instructed to remove any dentures and oral appliances, thoroughly rinse their oral cavities with clean water, take a deep breath, and expectorate 2\u0026ndash;5 ml of sputum through forceful coughing into a clean, wide-mouth, sterile single-use container. Patients were cautioned to avoid including saliva in the sample. Sputum samples should be delivered to the laboratory within 2 hours of collection. The quality of the sputum samples was assessed using the Q score evaluation criteria (Bartlett's criteria), and patients were instructed to provide repeat samples if the initial specimen did not meet the required standards [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBronchial wash and lavage fluids were collected by experienced pulmonologists using a video bronchoscope under sterile conditions to minimize the risk of contamination with normal microbial flora from the upper respiratory tract. Bronchial washing was obtained by instilling 20\u0026ndash;30 ml of sterile normal saline into a segmental or lobar bronchus, followed by immediate aspiration of the fluid. A minimum of 3\u0026ndash;5 ml of the aspirated fluid was collected in a sterile container. BAL is performed by wedging the bronchoscope into a segmental bronchus and instilling 100 ml of sterile normal saline, and at least 5\u0026ndash;10 ml of subsequent aliquots were submitted for analysis. All samples were transported in a cold box maintained at 2\u0026ndash;8\u0026deg;C and delivered to the laboratory within 30\u0026ndash;60 minutes following collection [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Laboratory processing\u003c/h2\u003e \u003cp\u003eUpon receipt, specimens were logged and processed within a Biosafety Level 2 cabinet. All specimens were Gram-stained and examined microscopically to evaluate the ratio of polymorphonuclear leukocytes (PMNs) to squamous epithelial cells, as well as to assess bacterial morphology. Sputum specimens exhibiting more than 25 PMNs and fewer than 10 squamous epithelial cells per low-power field (LPF) were classified as acceptable quality and subsequently cultured. Specimens deemed unacceptable were rejected, and repeat specimen collection was recommended.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. \u003cem\u003eBacterial isolation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eQuality-assured specimens were cultured and identified using a variety of culture media. Blood agar plates (BAP, CO₂) were utilized for the cultivation of aerobic bacteria. MacConkey agar (MAC, O₂) was employed for the isolation of Gram-negative bacteria. Sabouraud agar supplemented with chloramphenicol was used for fungal isolation, while mannitol salt agar facilitated the cultivation of \u003cem\u003eStaphylococcus species\u003c/em\u003e. Additionally, chocolate agar (CHOC, CO₂) and Brain\u0026ndash;Heart Infusion broth were also utilized in the culturing process.\u003c/p\u003e \u003cp\u003eBronchial washing and bronchoalveolar lavage samples were enriched in brain\u0026ndash;heart infusion broth and incubated for 24 hours before subculture. A sterile disposable 1 \u0026micro;L loop was utilized for specimen transfer, employing a four-quadrant streaking technique on the culture media. All cultures were incubated at 35\u0026ndash;37\u0026deg;C in a 5% CO₂ atmosphere for 24 hours. After incubation, colony-forming units (CFU) were quantified. In sputum cultures, bacterial growth of \u0026ge;\u0026thinsp;10⁵ CFU/mL and in BAL samples of \u0026ge;\u0026thinsp;10⁴ CFU/mL was deemed clinically significant. Isolates that met these thresholds were further characterized [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. \u003cem\u003eAntimicrobial susceptibility testing\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eA bacterial suspension equivalent to the 0.5 McFarland standard was prepared from cultured colonies. Identification of bacterial and fungal species, as well as antimicrobial susceptibility testing, was conducted using VITEK-2 analyzer ID/AST cards. Minimum inhibitory concentration (MIC) values were determined in accordance with CLSI M100 guidelines, and isolates were classified as susceptible, intermediate, or resistant based on established breakpoints. For quality control purposes, standard reference strains, including \u003cem\u003eE. coli\u003c/em\u003e ATCC 25922, \u003cem\u003eS. aureus\u003c/em\u003e ATCC 25923, and \u003cem\u003eP. aeruginosa\u003c/em\u003e ATCC 27853, were employed throughout the testing process [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMicrobial resistance was classified based on the number of antibiotics to which the organisms exhibited resistance, adhering to the classification systems established by the Centers for Disease Control and Prevention (CDC) and the European Centre for Disease Prevention and Control (ECDC). Isolates were categorized as non-resistant or susceptible (NR), multidrug-resistant (MDR), extensively drug-resistant (XDR), or pandrug-resistant (PDR) according to these standardized definitions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e4. Statistical analysis\u003c/h3\u003e\n\u003cp\u003eCollected study data were analyzed using the Statistical Package for the Social Sciences, version 26. Descriptive statistics were summarized in tables and presented as percentages. Group comparisons were conducted using the chi-square test. Initially, the presence and absence of bacterial isolation were compared among patients with pulmonary diseases. Subsequently, the distribution of etiologic agents and antimicrobial resistance patterns among culture-positive groups was analyzed using percentages. A 95% confidence interval was applied, and a p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study included participants with a relatively equal gender distribution (male 47.7%, female 52.3%), and the majority were of advanced age. The predominant pathogens identified in this study were \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (36.2%), \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (17.0%), methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MRSA) (10.9%), and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (6.2%), which represented the most common causative agents of lower respiratory tract infections. High levels of multidrug resistance were observed among these isolates. Specifically, 31.9% of \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, 8.0% of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, 11.8% of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, and 73.3% of MRSA isolates were classified as multidrug-resistant.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e47.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e52.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eAge, median (IQR), y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(49\u0026ndash;70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eHeight, median (IQR), cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(158\u0026ndash;168)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eWeight, median (IQR), kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(55\u0026ndash;78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;18.5 (underweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e10.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e18.5\u0026ndash;24.9 (normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e42.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e25.0\u0026ndash;29.9 (overweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e31.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e30\u0026thinsp;\u0026lt;\u0026thinsp;Obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e16.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLower secondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eUpper secondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e45.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eVocational or technical education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e16.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAssociate degree / Diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e33.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e81.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCohabiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eFamily member, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eHousehold type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDormitory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTraditional Mongolian ger / Yurt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGated house\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e15.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eApartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e73.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eShared room / Rented room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e24.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e21.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e53.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eWorking conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e89.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePhysically demanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHazardous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eAllergy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e30.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e29.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eCumulative smoking, median (IQR), y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(20\u0026ndash;45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eIf quit smoking: Total smoked, median (IQR), y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(5\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBacterial pathogens were identified in 78.0% of the study population, with no notable variation in the distribution of bacterial species across study groups. Acute exacerbation of chronic obstructive pulmonary disease (COPD) and bronchitis were the most frequently diagnosed conditions (57.6%), followed by pneumonia and lung abscess (33.9%). The rate of bacterial growth did not differ significantly among pulmonary disease categories (\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e). No significant association was observed between bacterial growth and most comorbidities. In contrast, haematological disorders were significantly more prevalent among patients with bacterial growth (5.1%) compared with culture-negative patients (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline clinical characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\" style=\"width: 30.1294%;\"\u003e\n \u003cp\u003ePresence of bacteria\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\" style=\"width: 15.8965%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\" style=\"width: 14.2329%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN\u003c/td\u003e\n \u003ctd\u003e%\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003en\u003cbr\u003e276\u003c/td\u003e\n \u003ctd style=\"width: 9.7966%;\"\u003e%\u003cbr\u003e78.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.2515%;\"\u003en\u003cbr\u003e78\u003c/td\u003e\n \u003ctd style=\"width: 9.9815%;\"\u003e%\u003cbr\u003e22.0\u003c/td\u003e\n \u003ctd style=\"width: 1.6636%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003cth\u003e\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003eWards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eOutpatient care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e59.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e26.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e37.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eGeneral ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e28.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e12.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e6.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e11.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e60.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e56.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003ePneumonia and pulmonary abscess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e33.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e35.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e26.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eCOPD exacerbation and bronchitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e57.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e57.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e57.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eBronchiectasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e11.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e12.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e7.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003ePulmonary fibrosis exacerbation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e9.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e8.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e12.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eCardiovascular disorders\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e70.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e69.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e74.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e12.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e15.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eLiver disorders\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e11.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e11.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e9.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e9.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e9.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e9.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eRheumatoid arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e6.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e11.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eNeurological disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e7.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eHematologic disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003ePresence of any complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e48.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e46.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e52.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eRespiratory failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e36.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e34.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e43.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eARDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e10.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e10.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eSepsis / Septicemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eComa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eSingle organ failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e17.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e12.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eMultiple organ failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003eTotal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e15.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eDeath due to lung disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e4.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e4.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eDeath due to non-pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" style=\"width: 9.7966%;\"\u003e\n \u003cp\u003e5.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" style=\"width: 4.2515%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\" style=\"width: 9.9815%;\"\u003e\n \u003cp\u003e1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\" style=\"width: 1.6636%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eICU intensive care unit, COPD chronic obstructive pulmonary disease, ARDS acute respiratory distress syndrome\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u0026dagger; including arterial hypertension, ischemic heart disease, arrythmia and heart failure\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u0026Dagger; including chronic hepatitis and liver cirrhosis\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003estatistical markers (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOverall, 48.0% of patients developed at least one complication. Respiratory failure was the most common (36.7%), followed by single-organ failure (16.1%), acute respiratory distress syndrome (ARDS) (10.7%), multiple organ failure (7.6%), sepsis (7.6%), and coma (4.2%). The incidence of complications did not differ significantly between patients with and without bacterial growth (\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e). A total of 22 deaths (15.1%) were recorded. Mortality was significantly higher among patients with bacterial growth than among those without (18.8% vs 2.9%, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Pulmonary disease\u0026ndash;related mortality was observed exclusively in the bacterial growth\u0026ndash;positive group (4.7% vs 0%, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e), with pneumonia being the leading cause of death.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Prevalence and distribution of bacterial antibiotic resistance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Resistance Group Classification\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"104%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\" style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\" style=\"width: 17px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 47px;\"\u003e\n \u003cp\u003eResistancy group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePDR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003eXDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMDR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e100 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e8 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e23 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e4 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e47 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e15 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e7 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e6 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eMRSA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e30 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e22 (73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e17 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e14 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e9 (64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e13 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e10 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eSphingomonas paucimobilis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e8 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e5 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3 (60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e4 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e3 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus pseudoporcinus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus haemolyticus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eEnterobacter cloacae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter lwoffii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eRaoultella planticola\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus gordonii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus salivarius\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus agalactiae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus pseudintermedius\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus warneri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eViridans streptococci\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eKocuria kristinae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter junii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas stutzeri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas alcaligenes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003ePantoea spp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eRalstonia pickettii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eCitrobacter brakii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eYersinia entercolitica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eRothia mucilaginosa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas sobria\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePDR pan drug-resistant, XDR extensively drug-resistant, MDR multidrug-resistant, NR non-resistant\u003c/p\u003e\n\u003cp\u003eAmong the identified causative pathogens, gram-negative bacteria demonstrated the highest prevalence, including \u003cem\u003eK. pneumoniae\u003c/em\u003e, \u003cem\u003eA. baumannii\u003c/em\u003e, \u003cem\u003eP. aeruginosa\u003c/em\u003e species, and \u003cem\u003eE. coli\u003c/em\u003e. These gram-negative organisms exhibited particularly high rates of MDR, XDR, and PDR. Concerning gram-positive bacteria, including MRSA, \u003cem\u003eS. aureus\u003c/em\u003e, \u003cem\u003eE. faecium\u003c/em\u003e, and \u003cem\u003eS. epidermidis\u003c/em\u003e, the proportion demonstrating multidrug resistance was notably elevated.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e represented the highest proportion of all bacterial isolates at 36.2%, with predominant detection in the outpatient department (45.8%), which demonstrated statistically significant differences when compared to the internal medicine ward and ICU (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Among \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates, 23% were classified as MDR, 8% as XDR, and 4% as PDR, indicating a substantial risk within the healthcare environment.\u003c/p\u003e\n\u003cp\u003eA similar pattern was observed for \u003cem\u003eA. baumannii\u003c/em\u003e (17% of total isolates), which exhibited a particularly high proportion of pandrug resistance (31.9%), with 14.9% classified as MDR and 6.4% as XDR. These isolates demonstrated elevated resistance to \u0026beta;-lactam antibiotics, cephalosporins, and carbapenems.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e also displayed extensive resistance patterns, with 11.8% and 17.6% of \u003cem\u003eP. aeruginosa\u003c/em\u003e isolates classified as XDR and MDR, respectively, while 64.3% of \u003cem\u003eE. coli\u003c/em\u003e isolates were categorized as multidrug-resistant.\u003c/p\u003e\n\u003cp\u003eHigh levels of resistance were observed among gram-positive bacteria. Among \u003cem\u003eMRSA\u003c/em\u003e isolates, 73.3% were classified as multidrug-resistant, while an additional 6.7% exhibited extensive drug resistance. This indicates a persistent resistance to \u0026beta;-lactam antibiotics and other broad-spectrum antimicrobial agents across multiple classes. Multidrug resistance was particularly prominent among bacterial species, including \u003cem\u003eEnterococcus faecium\u003c/em\u003e and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn comparison, specific bacterial species (such as \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e, and \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e) were detected in relatively low quantities; however, they exhibited significantly different patterns between non-resistant and multidrug-resistant classifications. Notably, the proportion of pandrug-resistant isolates of \u003cem\u003eS. maltophilia\u003c/em\u003e was high, at 33.3%. Although \u003cem\u003eS. maltophilia\u003c/em\u003e had a lower prevalence compared to predominant gram-negative bacteria such as \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (36.2%) and \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e(17.0%), the elevated rate of PDR isolates necessitates careful consideration.\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of Patient Characteristics, Comorbidities, and Complications According to Antibiotic Resistance Categories\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e\n \u003cp\u003eResistancy group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003ePDR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003eEDR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\n \u003cp\u003eMDR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eWards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGeneral ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e39.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e22.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e37.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e25.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOutpatient care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e26.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePneumonia and pulmonary abscess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e15.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e24.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCOPD exacerbation and bronchitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e27.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eBronchiectasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e30.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e12.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePulmonary fibrosis exacerbation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e31.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCardiovascular disorders\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e34.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e29.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e45.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLiver disorders\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e15.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e34.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e19.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e42.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRheumatoid arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e27.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e16.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNeurological disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e9.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e38.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e9.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHematologic disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e28.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e35.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003ePresence of complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e13.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e13.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e32.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e10.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRespiratory failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e13.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e36.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e10.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eARDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e16.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e6.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSepsis / Septicemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e21.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e26.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e30.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eComa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSingle organ failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e21.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e44.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMultiple organ failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e17.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e52.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eICU intensive care unit, COPD chronic obstructive pulmonary disease, ARDS acute respiratory distress syndrome\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003ePDR pan drug-resistant, XDR extensively drug-resistant, MDR multidrug-resistant, NR non-resistant\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003e\u0026dagger; including arterial hypertension, ischemic heart disease, arrythmia and heart failure\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003e\u0026Dagger; including chronic hepatitis and liver cirrhosis\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003estatistical markers (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWhen comparing antibiotic resistance rates across various departments, notably high levels were observed in the internal medicine department (MDR 39.7%) and ICU (XDR 37.1%). Additionally, the ICU exhibited a significant prevalence of both multidrug-resistant and pandrug-resistant isolates. Importantly, the non-resistant rates in these two departments were significantly lower than those in the outpatient department (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eWithin diagnostic categories, notably high levels of MDR were observed in lung abscess (24.2%), as well as in exacerbations of COPD and bronchitis (27.7%), with statistically significant differences identified (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). In contrast, while bronchiectasis (30.3%) and pulmonary fibrosis exacerbation (31.8%) exhibited the highest proportions of MDR, comparisons of antibiotic resistance categories in these conditions did not reveal statistically significant differences.\u003c/p\u003e\n\u003cp\u003eMultidrug-resistance was significantly more prevalent among patients with cardiovascular disease (30.3%), demonstrating a notable association between cardiovascular comorbidity and MDR (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e). A higher prevalence of MDR was also observed in patients with chronic kidney disease (45.2%) and rheumatoid arthritis (33.3%), with strong statistical evidence of association (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). Extensively drug-resistant (EDR) isolates were more frequently identified in patients with rheumatoid arthritis (27.8%). In contrast, no EDR isolates were detected among patients with chronic kidney disease. Furthermore, patients with hematological disorders and diabetes mellitus exhibited significantly higher prevalence rates of both MDR and PDR phenotypes (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Neurological disorders and liver disease were associated with increased proportions of MDR and EDR isolates.\u003c/p\u003e\n\u003cp\u003eIn terms of complications, multidrug-resistant infections exhibited the highest rate at 32.6% (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). When complications were categorized, patients experiencing respiratory failure demonstrated elevated MDR rates at 36.5%, while those with acute respiratory distress syndrome presented high XDR rates at 33.3% (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). Among patients with septic complications, all resistance categories, with the exception of non-resistant, showed increased rates (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). Conversely, in patients with complications arising from coma, the MDR rate was highest at 50%.\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparative distribution of bacterial isolates in general ward, ICU, and outpatient care\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eGeneral ward\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003eOutpatient care\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eMRSA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e11.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus gordonii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus salivarius\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus pseudoporcinus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus agalactiae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus pseudintermedius\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus haemolyticus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus warneri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eViridans streptococci\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eKocuria kristinae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e34.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e45.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eEnterobacter cloacae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichiacoli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e19.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e28.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e13.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter lwoffii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter junii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas stutzeri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas alcaligenes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003ePantoea spp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eSphingomonas paucimobilis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eRaoultella planticola\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eRalstonia pickettii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eCitrobacter brakii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eYersinia entercolitica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eRothia mucilaginosa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas sobria\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003estatistical markers (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe most prevalent gram-negative bacteria identified were \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e. Among the gram-positive bacteria, the most commonly isolated organisms were \u003cem\u003eMRSA\u003c/em\u003e, \u003cem\u003eEnterococcus faecium\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, and \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e. Our findings correspond with the principal prevalent pathogens were identified in various studies, including a retrospective analysis at a prominent teaching hospital in China [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], a retrospective study in Saudi Arabia [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] focusing on ICU and diverse infection types (including lower respiratory tract infections, bloodstream infections, and urinary tract infections), the World Health Organization (WHO) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and the Global Burden of Disease (GBD) study [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe observed that Gram-negative and some gram-positive bacteria showed high resistance to β-lactam, cephalosporin, and macrolide antibiotics. \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e had a pan-drug-resistant rate of 31.9%, MRSA showed 73.3% multidrug resistance, and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e had a 23% MDR rate\u0026mdash;highlighting alarming resistance to broad-spectrum antibiotics.\u003c/p\u003e \u003cp\u003eThe primary mechanism underlying antibiotic resistance is the production of enzymes such as β-lactamases and carbapenemases by certain bacteria, which hydrolyze the β-lactam ring structure of antibiotics. These enzymes are particularly prevalent in species such as \u003cem\u003eKlebsiella\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In \u003cem\u003eMRSA\u003c/em\u003e, the mecA gene induces the production of PBP2a, which confers resistance to β-lactam antibiotics and accounts for a significant proportion of MDR isolates. Numerous studies have confirmed that increased synthesis of efflux pumps, inappropriate antibiotic usage, and inadequate infection control practices within healthcare settings contribute to the dissemination of MDR and XDR pathogens, exacerbating the issue of antibiotic resistance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study illustrates that antibiotic resistance patterns in both gram-negative and gram-positive bacteria are influenced by the interplay between antibiotic usage in healthcare settings and the intrinsic resistance mechanisms of these pathogens, leading to heightened levels of MDR, XDR, and PDR bacteria.\u003c/p\u003e \u003cp\u003eIn addition, Gram-negative pathogens such as \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (23%), \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (14.9%), \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (17.9%), and \u003cem\u003eEscherichia coli\u003c/em\u003e (64.3%) exhibited multidrug resistance (MDR). A considerable proportion of these isolates also demonstrated extensively drug-resistant (XDR) and pan-drug-resistant (PDR) phenotypes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Among Gram-positive bacteria, high rates of MDR were observed in MRSA (73.7%), \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (20%), and \u003cem\u003eEnterococcus faecium\u003c/em\u003e (38.5%) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The elevated prevalence of MDR, XDR, and PDR strains is closely associated with the frequent use of antibiotics in both outpatient and hospital settings, particularly in internal medicine wards and intensive care units [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This environment fosters the emergence of resistant strains against broad-spectrum agents such as β-lactams and fluoroquinolones. Furthermore, both intrinsic and acquired mechanisms of antimicrobial resistance in these pathogens have been well documented in numerous studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Inappropriate antibiotic use and insufficient regulation of prescribing practices remain fundamental drivers of bacterial resistance.\u003c/p\u003e \u003cp\u003eOur study results indicated that community-acquired \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e infections were identified at higher rates in outpatients, whereas relatively lower proportions were observed within the internal medicine department and ICU. This finding highlights distinct differences in the sources of infection and transmission patterns across these clinical settings. The elevated prevalence of \u003cem\u003eK. pneumoniae\u003c/em\u003e infections in outpatients can be attributed to the organism's widespread presence in community environments and its ability to colonize both the respiratory and gastrointestinal tracts [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, outpatients are routinely managed with chronic underlying conditions, such as COPD and bronchiectasis, for which \u003cem\u003eK. pneumoniae\u003c/em\u003e is a principal causative agent during episodes of infectious exacerbation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe variation in bacterial resistance observed across different pulmonary conditions can be primarily attributed to the underlying lung diseases, prior antibiotic exposure, and environmental factors within healthcare settings. Chronic pulmonary conditions, such as bronchiectasis, COPD, and recurrent exacerbations, compromise mucociliary clearance and disrupt epithelial defense mechanisms in the airways, thereby facilitating the colonization by resistant organisms. Additionally, patients with severe lung disease often require repeated hospitalizations, broad-spectrum antibiotic therapy, and the use of mechanical ventilation, along with other invasive interventions, all of which contribute to the increased prevalence of multidrug-resistant and extensively drug-resistant pathogens. Structural alterations in the lungs and localized immunological deficiencies create favorable conditions for the survival and development of antibiotic resistance among gram-negative bacteria, including \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e. Consequently, these factors account for the diverse patterns of bacterial resistance observed across various pulmonary disease categories.\u003c/p\u003e \u003cp\u003eThe elevated rate of bacterial growth observed in patients with hematological diseases can predominantly be attributed to immunodeficiency and ongoing exposure to healthcare environments [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In conditions such as leukemia and bone marrow failure, both the quantity and functional capacity of neutrophils and lymphocytes are diminished, resulting in compromised innate immunity that facilitates bacterial growth and colonization [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Additionally, these patients frequently experience repeated hospitalizations, necessitate central venous catheter placements, and receive broad-spectrum antibiotic therapy, all of which significantly heighten the risk of acquiring multidrug-resistant and extensively drug-resistant bacterial infections [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Immunosuppressive therapies, hypogammaglobulinemia, and cytopenia further weaken the body's antibacterial immune response, ultimately fostering conditions conducive to increased bacterial proliferation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn patients with chronic kidney disease, levels of resistance were markedly elevated across all categories, consistent with international research trends. This phenomenon arises because individuals with renal insufficiency often undergo medical interventions such as dialysis, which increases exposure to healthcare-associated pathogens [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, since the majority of antibiotics are eliminated via the urinary tract, impaired excretion leads to prolonged exposure of causative organisms to these agents, thereby enhancing the likelihood of resistance development [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong diabetic patients, the high prevalence of resistant infections (MDR 42.3%) is linked to weakened immune responses and the frequent occurrence of soft tissue and urinary tract infections. These infections, in turn, increase the likelihood of repeated antibiotic use in both outpatient and inpatient settings [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In patients with rheumatoid arthritis, elevated resistance levels are associated with the use of anti-inflammatory and immunosuppressive therapies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe observation of significantly elevated bacterial resistance rates among patients with complications is a noteworthy finding. Specifically, in cases of sepsis and septicemia, multidrug resistance was identified in 31.7% of isolates, with extensively drug-resistant strains comprising 26.1%. Among patients with acute respiratory distress syndrome, the rate of multidrug resistance reached 30.3%, while those experiencing multiple organ failure exhibited the highest rate of multidrug resistance at 52.2%. Patients with complications are typically managed in ICU and internal medicine departments, where various factors converge to promote the prevalence of highly resistant organisms. These factors include the use of broad-spectrum antibiotics, the utilization of invasive medical devices, and prolonged treatment durations, all of which contribute to an environment conducive to the selection and persistence of bacteria with elevated resistance profiles [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMongolia routinely reports the number of deaths attributable to antibiotic resistance to the international community. A review of the literature indicates that studies on pulmonary pathogens have predominantly concentrated on multidrug-resistant tuberculosis [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, investigations into the bacteria associated with other pulmonary diseases and their corresponding antibiotic resistance patterns have not yet met international standards. This underscores the necessity for expanding this research and conducting annual studies in the future. Recent studies conducted in Vietnam have revealed that bacterial detection rates are significantly elevated during acute exacerbations of COPD. Furthermore, these studies frequently identify bacteria exhibiting resistance to multiple high-level antibiotics [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Additionally, research from China and Iran has indicated a notable prevalence of carbapenem-resistant \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e in hospital-acquired respiratory tract infections [\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study results have direct applications in clinical practice across several critical areas. Firstly, they facilitate the updating of empirical antibiotic therapy protocols for various departments, ensuring alignment with the predominant pathogens and resistance levels specific to those settings. Secondly, the findings can be utilized to revise the hospital antibiogram, which serves as essential information for physicians' daily treatment decisions and plays a significant role in improving patient outcomes while reducing antibiotic resistance. Thirdly, the high prevalence of multidrug-resistant and pan-drug-resistant pathogens identified in ICU and internal medicine departments reflects elevated rates of healthcare-associated infections in these areas, underscoring the urgent need to enhance Infection Prevention and Control measures. This enhancement should include improving hand hygiene practices among healthcare workers, refining departmental disinfection protocols, strictly adhering to ventilator and catheter care bundles, and implementing patient isolation procedures when highly resistant pathogens are identified. Lastly, the results provide valuable insights for the improvement and refinement of the Antibiotic Stewardship Program, including the revision and establishment of an updated restricted antibiotic list for practical implementation in clinical settings.\u003c/p\u003e \u003cp\u003eInternational organizations underscore the necessity of several strategic approaches to combat antibiotic resistance. First, the implementation of antibiotic stewardship programs is critical, alongside the provision of consistent guidance to promote appropriate medication practices in all healthcare settings. This includes ensuring correct dosage, duration, and selection of antibiotics while minimizing the overuse of broad-spectrum agents [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Second, enhanced and enforced infection control and prevention measures are essential, encompassing hand hygiene practices, disinfection protocols, patient isolation procedures, and vaccination initiatives [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Third, restricting antibiotic use in livestock and agricultural practices is vital to mitigate the transmission risk of resistance to human populations [\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Finally, there is a compelling need for research and development of novel antibiotics and alternative therapeutic strategies, such as bacteriophage therapy and antimicrobial peptides [\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The coordinated implementation of these measures across clinical, public health, and policy levels is crucial to curbing the spread of antibiotic resistance [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study presents several significant limitations that should be acknowledged. First, as a retrospective, single-center investigation, caution is warranted when extrapolating findings to other healthcare settings in Mongolia, particularly to primary care facilities and rural areas where conditions may differ substantially. The reliance on electronic medical records for patient information may not fully capture specific clinical parameters, including detailed histories of prior antibiotic use, duration of therapy, and dosing information. Second, the study exclusively utilized culture-based results and did not incorporate molecular biological methods to validate resistance mechanisms at the genetic level, such as extended-spectrum beta-lactamases, carbapenemases, mecA, or NDM genes. This limitation restricts the capacity to elucidate the underlying causes of antibiotic resistance. Furthermore, certain pathogenic organisms, such as \u003cem\u003eS. maltophilia\u003c/em\u003e and \u003cem\u003eS. pneumoniae\u003c/em\u003e, were represented by relatively small case numbers, which limited the statistical power to accurately analyze resistance prevalence patterns within these groups. Third, cases yielding negative bacterial cultures were not systematically investigated for viral, fungal, or atypical pathogens, potentially resulting in gaps in understanding the complete etiological spectrum of pulmonary infections. Additionally, the study design did not permit a causal assessment of how clinical progression, treatment adjustments, and antibiotic substitutions influenced resistance outcomes. These limitations should be considered when interpreting the findings and their applicability to broader clinical contexts.\u003c/p\u003e \u003cp\u003eThis study identified that gram-negative bacteria, particularly \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, are predominantly isolated from patients with pneumonia. These pathogens exhibit a high prevalence of multidrug-resistant, extensively drug-resistant, and pandrug-resistant strains. Among gram-positive bacteria, methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003eEnterococcus faecium\u003c/em\u003e also demonstrate significant resistance, severely limiting therapeutic options. Consequently, the selection of initial treatment for pneumonia should be guided by the hospital's current antibiogram data to ensure that department-specific antibiotic prescribing policies align with local resistance patterns. Additionally, it is vital to strengthen infection control and antimicrobial stewardship programs. Moving forward, multicenter prospective studies and the establishment of routine molecular-level detection and surveillance will be essential for accurately defining the profiles of pneumonia pathogens and antibiotic resistance in Mongolia.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eAMR \u0026nbsp; \u0026nbsp; antimicrobial resistance\u003c/p\u003e\n\u003cp\u003eBAL \u0026nbsp; \u0026nbsp; bronchoalveolar lavage\u003c/p\u003e\n\u003cp\u003eBW \u0026nbsp; \u0026nbsp; bronchial washing\u003c/p\u003e\n\u003cp\u003eCDC \u0026nbsp; \u0026nbsp; Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eCOPD \u0026nbsp; \u0026nbsp;chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eGBD\u0026nbsp; \u0026nbsp; the Global Burden of Disease\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp;intensive care unit\u003c/p\u003e\n\u003cp\u003eMDR \u0026nbsp; \u0026nbsp;multidrug-resistant\u003c/p\u003e\n\u003cp\u003eMRSA \u0026nbsp; \u0026nbsp; methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNR \u0026nbsp; \u0026nbsp;non-resistant\u003c/p\u003e\n\u003cp\u003ePDR \u0026nbsp; \u0026nbsp; pandrug-resistant\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; the World Health Organization\u003c/p\u003e\n\u003cp\u003eXDR \u0026nbsp; \u0026nbsp; extensively drug-resistant\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki.\u0026nbsp;Ethical approval was obtained from the Research Ethics Review Committee of Mongolian National University of Medical Sciences (MNUMS) on April 8, 2025 (Approval No. 24-25/07-01). The need for informed consent from all participants was waived by the Clinical Research Subcommittee of the Mongolia\u0026ndash;Japan Hospital of MNUMS on February 27, 2025. The committee authorized the study to be conducted at the Mongolia\u0026ndash;Japan Hospital. Participants\u0026apos; registration data, demographic information, and laboratory test results were kept confidential and analyzed using a coded (blinded) approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors state that there are no financial or non-financial conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization:\u0026nbsp;Jargaltulga Ulziijargal;\u0026nbsp;Ichinnorov Dashtseren\u003c/p\u003e\n\u003cp\u003eMethodology:\u0026nbsp;Ichinnorov Dashtseren; Jargaltulga Ulziijargal\u003c/p\u003e\n\u003cp\u003eData curation:\u0026nbsp;Ekaterina Faermark; Usukhbayar Khenchbish; Tilyekbyergyen Bauyrjan; Laila Jukhai\u003c/p\u003e\n\u003cp\u003eFormal analysis:\u0026nbsp;Jargaltulga Ulziijargal; Ichinnorov Dashtseren\u003c/p\u003e\n\u003cp\u003eInvestigation:\u0026nbsp;Amgalanzaya Erdenebaatar; Zesemdorj Otgon-Uul; Laila Jukhai\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft:\u0026nbsp;Jargaltulga Ulziijargal; Ekaterina Faermark; Usukhbayar Khenchbish; Tilyekbyergyen Bauyrjan; Ichinnorov Dashtseren\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing:\u0026nbsp;Ichinnorov Dashtseren; Jargaltulga Ulziijargal\u003c/p\u003e\n\u003cp\u003eSupervision:\u0026nbsp;Zesemdorj Otgon-Uul; Ichinnorov Dashtseren\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors would like to express their sincere gratitude to the administration of Mongolian\u0026ndash;Japanese Hospital for granting permission to conduct this study and for facilitating access to the hospital\u0026rsquo;s electronic medical record system. We deeply appreciate the Department of Central Clinical Laboratory team for conducting culture analysis and antimicrobial susceptibility testing, and the Department of Pulmonology and Allergology team for their invaluable assistance in collecting specimens for laboratory analyses.\u003c/p\u003e\n\u003cp\u003eWe thank all healthcare professionals who contribute to antimicrobial resistance surveillance and infection prevention efforts in Mongolia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2019 Antimicrobial Resistance Collaborators. Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019. 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Accessed 29 Dec 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 3","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"low respiratory tract infection, multidrug-resistant bacteria, Klebsiella pneumoniae, Acinetobacter baumannii, MRSA","lastPublishedDoi":"10.21203/rs.3.rs-9327263/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9327263/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003ePulmonary infections remain a major cause of global morbidity and mortality, particularly in low- and middle-income countries. The rapid emergence of antimicrobial resistance, including multidrug-resistant, extensively drug-resistant, and pandrug-resistant organisms, has significantly limited therapeutic options and increased healthcare burden. This study aimed to investigate the bacterial profile and antimicrobial resistance patterns among patients with suspected pulmonary infections at a tertiary referral hospital in Mongolia.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA retrospective descriptive study was conducted at Mongolia\u0026ndash;Japan Hospital in Ulaanbaatar, from January 1, 2023, to December 31, 2024. Data were extracted from the electronic medical record system. Patients who underwent microbiological examination of lower respiratory tract specimens (sputum, bronchial washing, bronchoalveolar lavage) were included. Bacterial identification and antimicrobial susceptibility testing were performed using the VITEK-2 system in accordance with CLSI M100 guidelines. Resistance phenotypes were classified based on CDC and ECDC criteria. Statistical analysis was conducted using SPSS version 26, with chi-square testing applied for group comparisons. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eA total of 354 patients were included, with a balanced gender distribution and predominance of older adults. Bacterial pathogens were identified in 78.0% of cases. The most frequently isolated organisms were \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (36.2%), \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (17.0%), methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (10.9%), and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (6.2%). Gram-negative bacteria predominated and demonstrated high resistance rates. Among \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates, 23% were MDR, 8% XDR, and 4% PDR. \u003cem\u003eA. baumannii\u003c/em\u003e exhibited 14.9% MDR, 6.4% XDR, and 31.9% PDR. MDR was observed in 73.3% of MRSA isolates. Resistance rates were significantly higher in the Internal Medicine Department (MDR 39.7%) and ICU (XDR 37.1%) compared to the outpatient department (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). MDR was significantly associated with cardiovascular disease, chronic kidney disease, rheumatoid arthritis, diabetes mellitus, and hematological disorders (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Mortality was significantly higher among patients with bacterial growth compared to culture-negative patients (18.8% vs. 2.9%, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e), with pneumonia being the leading cause of death.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eGram-negative pathogens, particularly \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eA. baumannii\u003c/em\u003e, predominate in pulmonary infections and demonstrate alarming levels of MDR, XDR, and PDR phenotypes. High resistance rates in ICU and internal medicine settings highlight the urgent need for strengthened infection prevention and control measures and robust antimicrobial stewardship programs. Continuous surveillance, regular antibiogram updates, and multicenter prospective studies incorporating molecular resistance detection are essential to optimize empirical therapy and mitigate the growing burden of AMR in Mongolia.\u003c/p\u003e","manuscriptTitle":"Bacterial Profile and Antimicrobial Resistance in Patients with Pulmonary Infection: A Retrospective Single-Center Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 18:20:13","doi":"10.21203/rs.3.rs-9327263/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T07:29:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T19:22:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T11:53:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218464439497189627100322335720497027611","date":"2026-05-04T05:29:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T15:36:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77179203365559032654561383930787659722","date":"2026-05-03T13:53:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46512173263127342271572782368759646700","date":"2026-05-03T13:50:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205437661891278239156708195329420324277","date":"2026-05-02T14:06:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212073574201347789427481720196416848821","date":"2026-04-25T17:02:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T11:34:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-14T11:14:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-13T07:59:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-12T08:14:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-04-12T08:08:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3df7d0b0-a0a4-4192-8a66-6db1f92100c2","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T07:29:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T19:22:04+00:00","index":62,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T11:53:41+00:00","index":61,"fulltext":""},{"type":"reviewerAgreed","content":"218464439497189627100322335720497027611","date":"2026-05-04T05:29:51+00:00","index":60,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T15:36:04+00:00","index":59,"fulltext":""},{"type":"reviewerAgreed","content":"77179203365559032654561383930787659722","date":"2026-05-03T13:53:01+00:00","index":58,"fulltext":""},{"type":"reviewerAgreed","content":"46512173263127342271572782368759646700","date":"2026-05-03T13:50:30+00:00","index":57,"fulltext":""},{"type":"reviewerAgreed","content":"205437661891278239156708195329420324277","date":"2026-05-02T14:06:50+00:00","index":56,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T07:40:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 18:20:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9327263","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9327263","identity":"rs-9327263","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}