Clinical Efficacy and Diagnostic Value of Metagenomic Next-Generation Sequencing (mNGS) in Hospital-Acquired Pneumonia: A Stratified Retrospective Study of Responders and Non- Responders

Clinical Efficacy and Diagnostic Value of Metagenomic Next-Generation Sequencing (mNGS) in Hospital-Acquired Pneumonia: A Stratified Retrospective Study of Responders and Non- Responders | 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 Clinical Efficacy and Diagnostic Value of Metagenomic Next-Generation Sequencing (mNGS) in Hospital-Acquired Pneumonia: A Stratified Retrospective Study of Responders and Non- Responders Bin Zhang, Jianjun Wang, Qing Li, Jingyi Ge, Chenxi Zhang, Ting Zhou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5235477/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Hospital-acquired pneumonia (HAP) presents significant diagnostic challenges, exacerbated by the limitations of traditional culture-based methods. This study evaluates the clinical efficacy and diagnostic value of metagenomic next-generation sequencing (mNGS) in the detection of pathogens in HAP patients, providing new insights into infection prevention and control in healthcare settings. Methods : We conducted a retrospective analysis of clinical and laboratory data from 300 adult HAP patients at Beijing Rehabilitation Hospital, China. Bronchoalveolar lavage fluid samples were collected for DNA extraction, library construction, and sequencing using the Illumina platform. Results : The results revealed that mNGS identified pathogens in 92% of the samples, compared to 72% by traditional cultures. Specifically, mNGS detected a broader range of bacteria, viruses, and fungi, including Pseudomonas, Klebsiella, and Aspergillus, which were often missed by traditional methods. mNGS identified polymicrobial infections in 28% of the cases and antibiotic resistance genes in 30% of the samples where traditional methods failed. These findings led to changes in treatment for 26% of the patients based solely on mNGS data, with specific treatment adjustments driven by the detection of rare or resistant pathogens in 18% of these cases. Conclusions : Our findings advocate for the integration of mNGS in routine clinical practice to enhance diagnostic accuracy and enable more informed decision-making in the management of HAP. Despite its higher cost and technical requirements, mNGS holds promise for more accurate and timely diagnostics in complex infection cases. Hospital-acquired pneumonia Metagenomic Next-Generation Sequencing (mNGS) Hospital-Acquired Pneumonia (HAP) Pathogen Detection Antimicrobial Resistance Figures Figure 1 Figure 2 Figure 3 Background Infectious diseases remain a major problem, despite modern advances in clinical medicine and epidemiology. High morbidity and mortality and low threshold for complications demand that an acute diagnostic method be robust and have sufficient sensitivity to rule out small, abnormal populations of pathogens before mortality from infection ensues. Traditional diagnostic methods for pneumonia, such as culture-based techniques and polymerase chain reaction (PCR), have been the standard for pathogen detection but face significant limitations. Culture methods can take days to yield results and may miss fastidious or slow-growing organisms, leading to delayed or inadequate treatment​. PCR, while faster, is limited in its scope as it can only detect specific pathogens for which primers are designed​. These methods also struggle to detect polymicrobial infections, which are common in hospital-acquired pneumonia (HAP) [ 1 , 2 ]. In contrast, Metagenomic next-generation sequencing (mNGS) is an unbiased approach that can detect all potential pathogens, including bacteria, viruses, fungi, and parasites, from a single sample. mNGS sequences the entire nucleic acid content of a sample, offering a more comprehensive view of the infection profile. Studies have demonstrated that mNGS not only improves pathogen detection in cases where traditional methods fail but also identifies antimicrobial resistance genes, providing crucial information for targeted therapy​ ​[ 2 – 4 ]. The clinical application of mNGS has been largely unproblematic, with studies demonstrating its superior sensitivity and specificity compared to traditional methods, particularly in complex infections or when initial diagnostics are inconclusive [ 1 , 5 ]. This was especially crucial for identifying rare or novel pathogens, often implicated in emerging infectious diseases and pandemics like SARS-CoV, MERS-CoV, and SARS-CoV-2. The rapid identification of causative agents is paramount in mitigating the threat of infectious disease outbreaks, and mNGS has proven instrumental in this regard, as evidenced by its pivotal role in the identification and genomic surveillance of the SARS-CoV-2 virus during the COVID-19 pandemic [ 3 , 4 , 6 ]. In spite of these benefits, there are barriers to the incorporation of mNGS into routine clinical diagnostics–not the least of which is the added cost of sequencing and the requirements for bioinformatics support. Interpretation of mNGS data also requires in-depth knowledge of genomics, which is unlikely to exist in all clinical settings, as well as expertise in managing voluminous sequencing data containing a high proportion of human host DNA, which requires sophisticated computational tools to mine for pathogen-related information [ 3 ]. Several studies have already evaluated the clinical utility of mNGS in different medical areas, such as the diagnosis of respiratory, central nervous system and bloodstream infections, reporting its potential to critically modify clinical management and prognosis by providing rapid and reliable diagnoses. In particular, mixed infections have been identified as one of the most significant advantages of mNGS for directing therapy and impacting patient prognosis. This supports the hypothesis that mNGS could hold a novel and important role in clinical microbiology [ 7 , 8 ]. mNGS also yields valuable epidemiological information, such as which infections are spreading, and how pathogens are evolving or adapting to antibiotics, information that is vital for controlling outbreaks and developing prophylactic measures. That became crystal clear when the world was reeling from recent global health crises, when suspect specimens from hospitalised patients were routed through mNGS for real-time surveillance and quick responses [ 1 , 2 , 9 ]. HAP and community-acquired pneumonia (CAP) represent two distinct clinical challenges in terms of diagnosis and management. CAP typically arises outside of healthcare settings, and its causative pathogens are often susceptible to commonly used antibiotics, making standard culture-based diagnostic methods relatively effective in identifying the responsible organisms. However, CAP diagnosis is not without challenges; the overlap of symptoms with other respiratory illnesses and the presence of atypical pathogens can complicate the diagnostic process [ 10 ]. In contrast, HAP, which occurs 48 hours or more after hospital admission, is often associated with a higher incidence of multidrug-resistant (MDR) organisms. These pathogens can be missed by traditional culture methods due to their fastidious growth requirements or slow replication rates. Furthermore, the polymicrobial nature of HAP and the frequent co-infection with fungi and viruses, especially in immunocompromised patients, further complicates diagnosis. These limitations underscore the need for advanced diagnostic techniques such as metagenomic next-generation sequencing (mNGS), which provides a comprehensive, unbiased identification of pathogens by sequencing all nucleic acids in a sample [ 11 ]. This study aims to address the critical diagnostic gap in HAP by evaluating the clinical efficacy and diagnostic value of mNGS, particularly in detecting MDR organisms and polymicrobial infections that are often missed by standard diagnostic techniques. By comparing mNGS with traditional culture methods, we aim to demonstrate the superiority of mNGS in providing rapid, accurate, and actionable data for the management of HAP, ultimately improving patient outcomes. Given the global rise of antibiotic resistance, this study holds significance in advancing pathogen detection methods, not only in hospital settings but also for cases where traditional methods fail, such as in CAP. Material and Methods Study Design and Setting This retrospective cohort study was conducted in the Beijing Rehabilitation Hospital, affiliated to Capital Medical University, Department of Respiratory and Critical Care Medicine, China, from August 2021 to January 2024. It addressed adult patients admitted to hospital for HAP to evaluate the diagnostic value of mNGS versus culture. The study was approved by the institutional review board at the hospital. Given the retrospective design, consent for the study was waived, although all of these patients or their families had previously consented to the acquisition of sample for medical purposes. All data were kept anonymous, under strict confidentiality and an ethical framework. Study Population Patients eligible for inclusion in the study were those diagnosed with HAP, defined as pneumonia that developed 48 hours or more after hospital admission. All patients exhibited persistent symptoms, including fever, elevated inflammatory markers, and respiratory distress, despite initial antibiotic treatment based on sputum culture results, yet still had a poor prognosis characterised by fever and high inflammatory markers persisting two to three days after treatment. The 300 patients were included in the study, divided into responders and non-responders according to their clinical response: 250 responders and 50 non-responders. Bronchoalveolar lavage fluid (BALF) samples were collected for both traditional culture and mNGS testing. Only adult patients aged 18 years and older were included in the study. Patients with severe liver or kidney dysfunction, or those with unstable vital signs, were excluded. To maintain focus on pathogen detection and drug resistance, we streamlined the clinical evaluation indicators. Key indicators included: Oxygenation Index, a measure of lung function, defined as the ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2); Pneumonia Severity Index (PSI), a widely used scoring system to assess the severity of pneumonia; Clinical Pulmonary Infection Score (CPIS), a scoring tool used to gauge the severity of pneumonia based on clinical, radiological, and microbiological findings. Other health indices like frailty (FRAIL scale), comorbidity burden (Charlson Comorbidity Index), and daily living activities (Barthel Index) were recorded but simplified for clarity in the analysis of the primary focus, which was pathogen detection and treatment adjustments. Patients who were excluded were those with serious liver or kidney dysfunction, patients discharged or died before receiving NGS results, those without consent for collection of bronchoalveolar lavage fluid for NGS testing, those with unstable vital signs, those with incomplete clinical data and unclear prognosis. Data collection Clinical and laboratory data was obtained from the electronic medical records which had record of demographics, clinical scoring indices, as well as routine haematological and biochemical parameters. Pneumonia-related laboratory tests such as hemoglobin A1c (HbA1c), albumin (ALB), prealbumin (PA), white blood cell count (WBC), C-reactive protein (CRP), interleukin-6 (IL-6) and creatinine (Cr) were also collected. Fourteen clinical scoring systems were used in this study including patient age, gender, Pneumonia Severity Index (PSI), Clinical Pulmonary Infection Score (CPIS), FRAIL scale for frailty, the Charlson comorbidity index, Barthel Index (BI) for daily living activities, Acute Physiology and Chronic Health Evaluation II (APACHE II), and routine biochemical parameters including haemoglobin A1c (HbA1c), albumin (ALB), prealbumin (PA), white blood cell count (WBC), reactive protein (CRP), interleukin-6 (IL-6 ) and creatinine (Cr). Traditional Culture Methods Samples were processed using standard microbiological techniques for the isolation and identification of bacterial pathogens, including aerobic and anaerobic cultures, and identification by automated systems (e.g., Vitek-2). Agents were also tested for their drug sensitivity which helps to guide antibiotic treatment prescriptions. mNGS and Analyses For mNGS, a volume of fluid from the bronchoalveolar lavage was extracted and processed into DNA using that can maintain constant performance and avoid cross-contamination. The libraries were then constructed and sequenced on an Illumina platform. The raw data were submitted to a bioinformatics pipeline, which can effectively filter out human DNA residues and identify the microbial sequences and match the identification results via a number of pathogen databases. Interpretation of mNGS Results BALF samples were collected from each patient under sterile conditions. DNA was extracted using the Qiagen QIAamp DNA Mini Kit, following the manufacturer’s protocol. The extracted DNA was quantified using a Qubit Fluorometer to assess concentration and purity. For sequencing, Illumina's NextSeq 550 platform was used to generate high-throughput sequencing data. Libraries were prepared using the Illumina Nextera XT DNA Library Preparation Kit, which involves random fragmentation of the DNA followed by adapter ligation. The samples were then sequenced in a paired-end format with a read length of 150 bp. To maintain performance consistency and minimize cross-contamination, rigorous quality control measures were applied at each step of the process, including the use of negative controls during library preparation and sequencing. Raw data were processed through a bioinformatics pipeline, with human DNA sequences filtered out, allowing for the identification of microbial sequences through comparison with a reference pathogen database. The interpretation of mNGS data was performed by a team of clinical microbiologists and bioinformatics specialists. Results were categorized based on pathogen load and diversity, with significant findings reviewed in the context of each patient’s clinical presentation to ascertain their relevance to the ongoing treatment. Pathogen Detection and Analysis The study evaluated the spectrum and frequency of pathogens detected by mNGS compared to traditional methods. The efficacy of each technique in identifying causative agents in patients with complex clinical presentations was assessed, with particular attention to the detection of polymicrobial infections and antibiotic resistance markers. The positive results from mNGS were determined using both quantitative and qualitative criteria. For each sample, the microbial load was calculated based on the proportion of microbial reads out of the total sequenced reads. A pathogen was considered significant if its relative abundance exceeded 1% of the total microbial reads, a threshold supported by previous studies that correlate such levels with clinically relevant infections. The clinical significance of detected pathogens was assessed through several factors: pathogen abundance, virulence (evaluated through known pathogenicity databases such as the NCBI Pathogen Database), and the presence of polymicrobial infections, where multiple pathogens exceeding the 1% threshold suggested a higher likelihood of clinical relevance. Additionally, mNGS results were cross-referenced with the detection of antibiotic resistance genes, with resistant pathogens prioritized for significance, especially in cases where these were missed by traditional culture methods. Finally, pathogen identification was corroborated with the clinical presentation of each patient, including symptom severity and inflammatory markers, ensuring that only pathogens consistent with the clinical course were considered as causative agents. This multi-factor approach minimized the risk of false positives from commensal organisms or contaminants often found in respiratory samples, ensuring a higher diagnostic accuracy. Statistical Analysis Statistical analyses were performed using SPSS software. We used descriptive statistics to summarise patient demographics and clinical characteristics. We defined sensitivity and specificity of mNGS versus traditional culture methods to compare using Chi-square tests for categorical data and t-tests for continuous variables. Statistical significance was set at a p-value less than 0.05. Adjustments in antibiotic therapy based on mNGS findings were also analyzed to evaluate the impact on patient outcomes. Results Demographic and Clinical Characteristics Analysis of demographic data ( Table 1 ) revealed that the mean age of responders was significantly lower than that of non-responders, with responders averaging 64.7 years compared to 69.1 years for non-responders (p = 0.005). Gender distribution across the groups showed a majority of males in both responders (67.2%) and non-responders (74%), however the difference was not statistically significant (p = 0.4371). Table 1 Demographic and Clinical Characteristics of Study Participants Response (n = 250) Non-Response (n = 50) p value Statistical Test Age (years, mean (SD)) 64.7 (11.3) 69.1 (9.773) 0.005 t-test Gender (n(%)) Chi-square - Female 82 (32.8) 13 (26) 0.4371 - Male 168 (67.2) 37 (74) Oxygenation Index (median(range)) 270.5 (48 ~ 762) 234.5 (58 ~ 450) 0.008 Rank-sum test PSI score (median(range)) 105 (50 ~ 165) 142.5 (105 ~ 165) < 0.0001 CPIS score (median(range)) 8 (6 ~ 12) 11 (8 ~ 13) < 0.0001 FRAIL (median(range)) 3 (2 ~ 4) 4 (3 ~ 5) < 0.0001 Charlson index CCL (median(range)) 5 (1 ~ 11) 6 (2 ~ 11) < 0.0001 Barthel Index BI (median(range)) 27 (0 ~ 100) 0 (0 ~ 65) < 0.0001 APACHE II (median(range)) 15 (6 ~ 26) 20 (11 ~ 35) < 0.0001 HbA1c (median(range)) 5.7 (3.7 ~ 9.7) 5.85 (4.3 ~ 9.9) 0.199 ALB (g/L, (median(range))) 31.75 (19 ~ 47.9) 30.9 (22.7 ~ 38.6) 0.015 PA (g/L, (median(range))) 0.16 (0.02 ~ 16.3) 0.125 (0.03 ~ 0.22) < 0.0001 WBC (10^9, (median(range))) 9.78 (1.27 ~ 24.78) 9.67 (2.88 ~ 28.58) 0.911 CRP (mg/L, (median(range))) 26.65 (0 ~ 240.2) 91.7 (5.7 ~ 363.6) < 0.0001 IL-6 (pg/ml, (median(range))) 35.21 (0 ~ 1395.55) 62.53 (20.04 ~ 428.52) < 0.0001 Cr (umol/L, (median(range))) 58.65 (16.3 ~ 210.3) 58.75 (25.9 ~ 126.8) 0.703 A range of clinical scores and health indices were assessed ( Table 1 ) , revealing notable differences between responders and non-responders. The median oxygenation index was significantly higher in responders. Critical clinical scores such as the PSI and CPIS were considerably lower in responders, suggesting less severe clinical presentations compared to non-responders. The PSI scores ranged from 50 to 165 for responders and 105 to 165 for non-responders, showing a significant difference (p < 0.0001). Similarly, CPIS scores also differed significantly, with responders averaging lower scores indicative of better clinical status. Health indices further demonstrated disparities between the two groups. The FRAIL scale, used to assess frailty, showed that responders were less frail compared to non-responders, with scores significantly differing (p < 0.0001). The Charlson Comorbidity Index and the BI, which measures daily living activities, also reflected better health status in responders. Likewise, the APACHE II scores, which estimate ICU mortality risk, were lower in responders, suggesting a better prognosis (p < 0.0001). Laboratory findings complemented these clinical assessments, with several key biomarkers showing significant differences between the groups. Notably, ALB and PA levels were lower in non-responders, reflecting potentially poorer nutritional status or more severe disease states. Inflammatory markers such as CRP and IL-6 were significantly elevated in non-responders, indicating higher levels of systemic inflammation and potentially more severe or uncontrolled infectious processes (p < 0.0001 for both). However, no significant differences were observed in HbA1c and Cr levels between the groups, suggesting similar baseline metabolic and renal functions ( Table 1 ) . Pathogen Detection The analysis revealed that mNGS identified a higher diversity of pathogens compared to traditional methods ( Fig. 1 ). The most frequently identified bacteria were Pseudomonas, Klebsiella, Acinetobacter, Streptococcus, and Staphylococcus. In terms of fungi, Candida, Aspergillus, Pneumocystis, and Rhizopus were commonly detected. Viruses identified included HSV1, human cytomegalovirus (CMV), Epstein-Barr virus (EBV), varicella zoster virus, Primate erythroparvovirus 1, human herpesvirus 7, and human adenovirus B ( Fig. 1 ). mNGS detected a wide range of pathogens in both responder and non-responder groups. The frequency of bacterial detection was higher in the responder group compared to non-responders ( Fig. 2 ). Notably, Pseudomonas and Klebsiella were the predominant bacteria found in both groups. Candida was the most frequently detected fungus in both groups. Aspergillus and Pneumocystis were more prevalent in the non-responder group. HSV1, CMV, and EBV were among the most common viruses detected. The presence of these viruses was higher in non-responders compared to responders ( Fig. 2 ). Venn diagrams were used to illustrate the overlap and unique detections between mNGS and standard methods. They highlighted the added value of mNGS in identifying additional pathogens, which played a crucial role in guiding targeted antimicrobial therapy ( Fig. 3 ). Treatment Adjustments Based on the results of mNGS, treatment regimens were adjusted for both responders and non-responders. Treatment regimens were modified in 64 (25.6%) of the 250 responders and 14 (28%) of the 50 non-responders, showing no significant difference between the groups (p = 0.86). The addition of antimicrobial agents was necessary in a majority of cases, with 186 (74.4%) of responders and 36 (72%) of non-responders receiving additional agents ( Table 2 ). Table 2 Treatment Adjustments Based on mNGS Results. Modifications, n(%) Response (n = 250) Non-Response (n = 50) p value Treatment change 64(25.6%) 14(28%) 0.86 Add agent 186(74.4%) 36(72%) Clinical Outcomes Clinical outcomes were assessed and compared between responders and non-responders. The median duration of ICU stay was 18 days for responders (range: 0–55 days) compared to 15.5 days for non-responders (range:2–31 days), although this difference was not statistically significant (p = 0.786). Responders required a significantly shorter duration of mechanical ventilation (median: 3.5 days, range: 0–30 days) compared to non-responders (median: 12 days, range: 0–26 days) (p = 0.014). A higher proportion of responders underwent tracheostomy (64.8%) compared to non-responders (46%) (p = 0.019) ( Table 3 ). Table 3 Clinical Outcomes in Responders and Non-Responders. Response (n = 250) Non-Response (n = 250) p value Time in ICU (days, median(range)) 18(0 ~ 55) 15.5(2 ~ 31) 0.786 Duration of mechanical ventilation (days, median(range)) 3.5(0 ~ 30) 12(0 ~ 26) 0.014 Tracheostomy(n(%)) 162(64.8) 23(46) 0.019 Resistance Genes Our analysis identified a variety of resistance genes associated with the pathogens detected in both responders and non-responders ( Table 4 ). The analysis revealed the presence of multiple resistance genes, highlighting the complexity of treating infections in these patients. Table 4 Distribution of Resistance Genes Among Detected Pathogens. This table lists the various resistance genes identified in pathogens from both responders and non-responders, along with the specific pathogens harboring these genes. Patient ID Bacteria and Fungus Resistance gene(s) P8 Pseudomonas, Klebsiella, Acinetobacter, Candida, Stenotrophomonas, Enterococcus tetM, ErmB, sul1, armA, AAC(3)-Ia, APH(3’)-Ia, catB8, OXA beta-lactamase, AAC(6’), ANT(3”) P9 Pseudomonas, Klebsiella, enterococcus, aspergillus APH(3')-llb, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7 P14 Streptococcus sul1, sul2, armA, APH(3'')-Ib P20, P21 Pseudomonas, Klebsiella, Candida, Stenotrophomonas, aspergillus sul1, rmtB, TEM-206, AAC(3)-IIc, APH(3')-Iib, KPC beta-lactamase, AAC(6'), ANT(3'') P25 Pseudomonas, Klebsiella, Acinetobacter, Candida, Stenotrophomonas, staphylococcus tetM, rmtB, OXA-50, APH(3'')-Ib, APH(3')-la, APH(3')-llb, pseudomonas aeruginosa catB7, TEMbeta-lactamase, OXAbeta-lactamase, CTX-Mbeta-lactamase, KPC beta-lactamase P29, P31 Klebsiella, Acinetobacter, Candida sul1, CTX-Mbeta-lactamase, ANT(3'') P30, P265, P266, P269 Klebsiella, Acinetobacter sul1, sul2, CTX-Mbeta-lactamase, ANT(3''), armA, TEM-206, AAC(3)-la, AAC(3)-IIc, APH(3')-la, APH(6)-Id, catB8, TEMbeta-lactamase, OXAbeta-lactamase P35 Klebsiella, Acinetobacter, Corynebacterium, Candida, Nakaseomyces, Stenotrophomonas tet(A), tetM, sul1, sul2, armA, rmtB, AAC(6')-Ia, APH(3')-IIa, tetracycline-resistant ribosomal protection protein, TEMbeta-lactamase, OXAbeta-lactamase, ANT(3'') P36 Klebsiella, Corynebacterium, Candida, Stenotrophomonas sul1, sul2, armA, rmtB, AAC(6')-Ia, APH(3')-IIa, tetracycline-resistant, TEMbeta-lactamase, OXAbeta-lactamase P37, P38 Pseudomonas, Klebsiella, Candida, Nakaseomyces, Stenotrophomonas, aspergillus sul1, rmtB, TEM-206, AAC(3)-IIc, APH(3')-Iib, SHVbeta-lactamase, ACT beta-lactamase, KPC beta-lactamase, AAC(6'), ANT(3'') P39, P40 Pseudomonas, Candida, Stenotrophomonas, aspergillus sul1, rmtB, TEM-206, APH(3')-Iib, ACT beta-lactamase, KPC beta-lactamase P45, P48 Pseudomonas, Acinetobacter, Stenotrophomonas sul1, sul2, mecAAAC(6')-Ia, APH(3'')-Ib P51 Klebsiella, Acinetobacter, Streptococcus, Candida, Stenotrophomonas, staphylococcus tetM, ErmA, ErmB, sul1, sul2, mecA, AAC(6')-Ia, APH(3'')-Ib, APH(3')-la, APH(6)-Id, AAC(6')-Ie-APH(2'')-Ia, TEMbeta-lactamase, OXAbeta-lactamase, KPC beta-lactamase, ANT(3''), major facilitator superfamily(MFS) ant-ibiotic efflux pump P52 Pseudomonas, Acinetobacter, Streptococcus, Candida, staphylococcus tetM, sul1, sul2, mecA, AAC(6')-Ia, APH(3'')-Ib, APH(3')-la, APH(6)-Id, TEMbeta-lactamase, OXAbeta-lactamase, KPC beta-lactamase, ANT(3'') P57, P59 Pseudomonas, Acinetobacter, Candida, mycobacterium Fosa, armA, APH(3'')-Ib, APH(3')-la, APH(3')-Iib, APH(6)-Id, AAC(3), pseudomonas aeruginosa catB7, TEMbeta-lactamase, OXAbeta-lactamase P60, P67 Pseudomonas, Acinetobacter, Candida Fosa, armA, APH(3'')-Ib, APH(3')-la, APH(3')-Iib, pseudomonas aeruginosa catB7, TEMbeta-lactamase P62, P63, P84 Pseudomonas, Acinetobacter APH(3')-Iib, sul1, sul2, APH(3'')-Ib P75 Pseudomonas, prevotella, Fusobacterium TEMbeta-lactamase P80, P81, P126, P278 Pseudomonas, Klebsiella, Acinetobacter, Achromobacter, Stenotrophomonas sul1, armA, OXA-114a, APH(3'')-Ib, APH(3')-la, TEMbeta-lactamase, OXAbeta-lactamase ANT(3''), rmtB, SHVbeta-lactamase, CTX-Mbeta-lactamase, DHA beta-lactamase, KPC beta-lactamase, AAC(3), fosfomycin thiol transferase, catB8 P82, P130, P131, P149, P150, P151 Pseudomonas, Klebsiella, Acinetobacter sul1, sul2, armA, APH(3'')-Ib, APH(3')-la, TEMbeta-lactamase, ANT(3''), APH(6)-Id, OXAbeta-lactamase, adeJ, abeM, TEM-19, OXA-23 P83, P285, P286, P287 Acinetobacter sul1, APH(3'')-Ib, OXA beta-lactamase P87 Pseudomonas, Klebsiella, Corynebacterium, Stenotrophomonas, staphylococcus tetM, AAC(6')-Ie-APH(2'')-Ia, APH(6)-Id P88 Pseudomonas, Klebsiella, Stenotrophomonas, staphylococcus tetM, AAC(6')-Ie-APH(2'')-Ia, APH(6)-Id P89 Pseudomonas, Candida, Stenotrophomonas, enterococcus, staphylococcus tet(K), msrA, mecA, AAC(6')-Ie-APH(2'')-Ia, APH(3')-IIIa, major facilitator superfamily(MFS) ant-ibiotic efflux pump P90 Pseudomonas, Stenotrophomonas, enterococcus, staphylococcus tet(K), msrA, mecA, AAC(6')-Ie-APH(2'')-Ia, APH(3')-IIIa P91, P95 Klebsiella, Escherichia, aspergillus OXA-114a P94 Achromobacter, Stenotrophomonas, Escherichia, aspergillus OXA-114a P98 Pseudomonas, Klebsiella, Streptococcus, Candida, Achromobacter, Stenotrophomonas FosA, tetM, ErmB, OXA-50, AAC(6')-Ie-APH(2'')-Ia, APH(3')-Iib, pseudomonas aeruginosa catB7 P99, P260, P261 Pseudomonas, Klebsiella, Candida, Stenotrophomonas FosA, tetM, OXA-50, AAC(6')-Ie-APH(2'')-Ia, pseudomonas aeruginosa catB7, APH(3')-llb P100 Pseudomonas, Acinetobacter, Streptococcus, Corynebacterium, Stenotrophomonas FosA, tetM, APH(3')-la, APH(3')-Iib, APH(6)-Id, pseudomonas aeruginosa catB7, OXA-488 , chloramphenicol acetyltransferase(CAT)2 P101 Pseudomonas, Acinetobacter, Corynebacterium, Stenotrophomonas FosA, tetM, APH(3')-la, APH(3')-Iib, APH(6)-Id, pseudomonas aeruginosa catB7, chloramphenicol acetyltransferase(CAT)2 P108 Pseudomonas, Klebsiella, Acinetobacter, Streptococcus, Stenotrophomonas tetM P109 Pseudomonas, Acinetobacter, Streptococcus tetM P110 Pseudomonas, Klebsiella, Acinetobacter, Candida, Stenotrophomonas FosA, ANT(2'')-Ia, sul1, armA, OXA-50, AAC(6')-Ie-APH(2'')-Ia, APH(3'')-Ib, APH(3')-la, APH(3')-IIb, APH(6)-Id, pseudomonas aeruginosa catB7, OXA − 488, OXAbeta-lactamase, AAC(3), ANT(3'') P111 Pseudomonas, Klebsiella, Acinetobacter, Candida FosA, sul1, armA, OXA-50, AAC(6')-Ie-APH(2'')-Ia, APH(3'')-Ib, APH(3')-la, APH(3')-IIb, APH(6)-Id, pseudomonas aeruginosa catB7, AAC(3), ANT(3'') P114 Pseudomonas, Klebsiella, Acinetobacter, Escherichia pseudomonas aeruginosa catB7 P115 Pseudomonas, Klebsiella, Escherichia pseudomonas aeruginosa catB7 P118 Pseudomonas, Acinetobacter, Corynebacterium, Candida, Stenotrophomonas, staphylococcus, Elizabethkingia, Clavispora FosA, sul1, OXA-50, APH(3')-Iib, APH(3')-Iic, tetracycline-resistant ribosomal pro-tection protein P119 Pseudomonas, Acinetobacter, Corynebacterium, staphylococcus, Elizabethkingia, Clavispora sul1, OXA-50, APH(3')-Iic, tetracycline-resistant ribosomal pro-tection protein P127 Pseudomonas, Klebsiella, Acinetobacter, Achromobacter sul1, rmtB, TEMbeta-lactamase, SHVbeta-lactamase, CTX-Mbeta-lactamase, DHA beta-lactamase, KPC beta-lactamase, AAC(3) P140, P141 Klebsiella, Acinetobacter, Stenotrophomonas, staphylococcus tetM, sul1, mecA, armA, AAC(6')-Ie-APH(2'')-Ia, Erm23Sribosomal RNA, methyltransferase, OXAbeta-lactamase, ANT(3''), major facilitator superfamily(MFS) ant-ibiotic efflux pump fosfomycin thiol t-ransferase P145, P146 Klebsiella, Acinetobacter, Corynebacterium, Candida, Nakaseomyces, Stenotrophomonas, staphylococcus msrA, APH(3')-la, APH(6)-Id, tetracycline-resistant ribosomal protection protein, AAC(3), ANT(3''), fosfomycin thiol transferase P152, P153 Escherichia tetM P154 Klebsiella, Acinetobacter, Stenotrophomonas, Mycotoruloides adeB P155, P158 Klebsiella, Acinetobacter, Mycotoruloides adeB P163 Pseudomonas, Klebsiella, Acinetobacter, Corynebacterium, Candida, Stenotrophomonas, proteus ErmB, sul1, AAC(6')-Iz, AAC(6')-Ie-APH(2'')-Ia, APH(3'')-Ib, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7, OXA beta-lactamase, AAC(3) P164 Pseudomonas, Acinetobacter, Corynebacterium, Candida, Stenotrophomonas ErmB, sul1, AAC(6')-Iz, AAC(6')-Ie-APH(2'')-Ia, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7, OXA beta-lactamase P165 Pseudomonas, Stenotrophomonas, Mycotoruloides, proteus, Elizabethkingia sul1, OXA- P166 Pseudomonas, Achromobacter, Stenotrophomonas, Escherichia, Mycotoruloides, proteus, Elizabethkingia sul1, OXA- P167 Pseudomonas, Achromobacter, Stenotrophomonas, Burkholderia, Elizabethkingia sul1, OXA- P168 Pseudomonas, Stenotrophomonas, Burkholderia, Elizabethkingia sul1, OXA- P169 Pseudomonas, Klebsiella, Acinetobacter, Streptococcus, Corynebacterium AAC(6')-Ie-APH(2'')-Ia P170 Pseudomonas, Klebsiella, Streptococcus, Corynebacterium AAC(6')-Ie-APH(2'')-Ia P171, P172 Klebsiella, Nakaseomyces, enterococcus, staphylococcus vanA, tet(A), tetM, ErmB, AAC(6')-Ii, AAC(6')-Ie-APH(2'')-Ia, APH(3')-IIIa, vanZA, SHV beta-lactamase, CTX-Mbeta-lactamase P177 Pseudomonas, Klebsiella, Corynebacterium, Candida, Achromobacter, Stenotrophomonas tetM, ErmB, sul1, AAC(6')-Ie-APH(2'')-Ia, APH(3'')-Ib, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7 P178 Pseudomonas, Klebsiella, Corynebacterium, Candida tetM, ErmB, sul1, AAC(6')-Ie-APH(2'')-Ia, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7 P179, P180 Klebsiella, Stenotrophomonas, Haemophilus tet(A), sul2, aadA2, TEM-88, SHV-9, QnrS8, OXA-1, KPC-14, CTX-M-90, AAC(6’)-Ib P187 Pseudomonas, Acinetobacter, Achromobacter, Stenotrophomonas, Mycotoruloides, staphylococcus, aspergillus, bordelella sul1, aadA11, SHV-2A, OXA-10 P189 Pseudomonas, Acinetobacter, Stenotrophomonas, Mycotoruloides, staphylococcus, aspergillus sul1, aadA11, SHV-2A, OXA-10 P19 Pseudomonas, Klebsiella, Corynebacterium, Candida, Stenotrophomonas, Elizabethkingia tetM, ANT(2'')-Ia, APH(3')-llb, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7, OXAbeta-lactamase, AAC(3), AAC(6') P192 Pseudomonas, Achromobacter, Stenotrophomonas, Mycotoruloides, proteus, Burkholderia, Kerstersia sul1, aadA6/aadA10, OXA-10 P193 Achromobacter, Stenotrophomonas, Mycotoruloides, proteus, Burkholderia sul1, aadA6/aadA10, OXA-10 P200, P201 Pseudomonas, Acinetobacter, Escherichia, Mycotoruloides, staphylococcus, chryseobacterium sul1 adeJ, abeM, TEM-147, TEM-19, OXA-23, OXA-51 P208 Pseudomonas, Acinetobacter, Corynebacterium, Achromobacter, Stenotrophomonas, Escherichia, Mycotoruloides, staphylococcus, proteus, Elizabethkingia, Morganella, Clavispora sul1, mecA, adeJ, abeM, aadA, TEM-19, TEM-116, OXA-51, OXA-23, ErmB, AAC(6’)-Ib, AAC(6’)-Ie_x0002_APH(2”)-Ia P209 Pseudomonas, Acinetobacter, Corynebacterium, Achromobacter, Mycotoruloides, staphylococcus, proteus, Elizabethkingia, Clavispora sul1, adeJ, abeM, aadA, TEM-116, OXA-51, OXA-23, ErmB, AAC(6’)-Ib, AAC(6’)-Ie_x0002_APH(2”)-Ia P217, P218 Pseudomonas, Acinetobacter, Achromobacter, Stenotrophomonas, Mycotoruloides, proteus, Elizabethkingia, Haemophilus, bordelella, Enterobacter sul1, aadA11, OXA-10 P230 Pseudomonas, Klebsiella, Acinetobacter, Achromobacter, Stenotrophomonas, Elizabethkingia, Enterobacter sul1, adeJ, abeM, aadA, TEM-132, SHV-2A, OXA-23, OXA-51, KPC-3, CTX-M-104, AAC(6’)-Ib’ P231 Pseudomonas, Klebsiella, Acinetobacter, Achromobacter, Stenotrophomonas, Elizabethkingia sul1, adeJ, abeM, TEM-132, SHV-2A, OXA-23, OXA-51, CTX-M-104, AAC(6’)-Ib’ P234, P235 Pseudomonas, Acinetobacter, Achromobacter, staphylococcus, Elizabethkingia sul1, mecA, adeJ, abeM, aadA, TEM-150, TEM-116, OXA-51, OXA-23, AAC(6’)-Ib’ P236, P237, P238 Acinetobacter, Mycotoruloides, staphylococcus, Burkholderia mecA, adeJ, OXA-423, ErmA, ADC-25 P239, P241 Pseudomonas, Acinetobacter, Achromobacter, Mycotoruloides, Enterobacter sul1, aadA, OXA-50 P240 Pseudomonas, Acinetobacter, Achromobacter, Mycotoruloides, Elizabethkingia, Enterobacter sul1, aadA, OXA-50 P242 Pseudomonas, Klebsiella, Acinetobacter, Achromobacter, Mycotoruloides, Gardnerella, serratia sul1, aadA, TEM-213, SHV-5, OXA-50, OXA-114a, KPC-1, AAC(3)-IId P243 Pseudomonas, Klebsiella, Acinetobacter, Mycotoruloides, Gardnerella, serratia sul1, aadA, TEM-213, OXA-50, OXA-114a, KPC-1, AAC(3)-IId P246, P247, P250 staphylococcus ErmC P253, P254 Pseudomonas, Klebsiella, Acinetobacter, Streptococcus tet(A), tetM, tetO, APH(3')-la, APH(3')-llb, SHVbeta-lactamase, AAC(3) P255 Pseudomonas, Klebsiella, Streptococcus tet(A), tetM, tetO, APH(3')-la, APH(3')-llb, SHVbeta-lactamase P256, P257 Pseudomonas, Klebsiella, staphylococcus tetM, APH(3')-llb, pseudomonas aeruginosa catB7 P258, P259, P262, P262, P264 Pseudomonas, Klebsiella sul1, rmtB, TEM-214, APH(6)-Id, pseudomonas aeruginosa catB7, TEMbeta-lactamase, KPCbeta-lactamase, ANT(3'') P26, P27, P73, P74 Pseudomonas OXA-50, APH(3'')-Ib, APH(3')-IIb P28, P46, P47, P78 Pseudomonas, Candida, enterococcus FosA, OXA-50, APH(3')-Iib, pseudomonas aeruginosa catB7 P280 Pseudomonas, Klebsiella, Acinetobacter, Stenotrophomonas sul1, APH(3')-la, OXAbeta-lactamase, ANT(3'') P283, P284 Pseudomonas, Klebsiella, Candida, Nakaseomyces, Stenotrophomonas, enterococcus, Escherichia, Clavispora vanA, FosA, tet(A), tetM, ANT(2'')-Ia, ErmB, sul1, sul2, TEM-206, OXA-50, AAC(3)-IIc, AAC(6')-Ii, AAC(6')-Ie-APH(2'')-Ia, APH(3'')-Ib, APH(3')-IIb, APH(3')-IIIa, APH(6)-Id, pseudomonas aeruginosa catB7, OXAbeta-lactamase, CTX-Mbeta-lactamase, AAC(3), AAC(6'), ANT(3''), major facilitator superfamily(MFS) antibiotic efflux pump P296 Pseudomonas, Klebsiella, Achromobacter, Stenotrophomonas, Elizabethkingia sul1, aadA, KPC-5 P297 Pseudomonas, Achromobacter, Stenotrophomonas, Elizabethkingia sul1, aadA, KPC-5 P298 Pseudomonas, Klebsiella, Stenotrophomonas, Elizabethkingia sul1, KPC-5 P299 Pseudomonas, Klebsiella, Achromobacter, Stenotrophomonas, Mycotoruloides, proteus, bordelella sul1, aadA2, TEM-213, SHV-5, QnrS8, OXA-10, NDM-1, KPC-5, CTX-M-90, AAC(6’)-Ib9 P300 Pseudomonas, Klebsiella, Achromobacter, Stenotrophomonas, Mycotoruloidesproteus sul1, aadA2, TEM-213, QnrS8, OXA-10, NDM-1, KPC-5, AAC(6’)-Ib9 Several resistance genes were frequently identified, including tetM, ErmB, sul1, and various beta-lactamases such as OXA and TEM. These genes were associated with resistance to tetracyclines, macrolides, sulfonamides, and beta-lactam antibiotics, respectively. The detailed distribution of resistance genes among the patients showed that multiple pathogens harbored these genes, contributing to multidrug-resistant infections. For example, Pseudomonas, Klebsiella, and Acinetobacter were commonly found with resistance genes such as tetM, sul1, and OXA beta-lactamase. Staphylococcus species frequently carried mecA, encoding methicillin resistance. Candida and other fungal pathogens also exhibited resistance, complicating antifungal therapy ( Table 4 ) . Some patients had complex resistance profiles involving multiple genes. For instance, Patient P8 harbored tetM, ErmB, sul1, armA, AAC(3)-Ia, APH(3’)-Ia, catB8, OXA beta-lactamase, and others; Patient P9 had APH(3')-llb, APH(3')-la, APH(6)-Id, and Pseudomonas aeruginosa catB7. Many patients exhibited mixed infections involving multiple resistant pathogens, further complicating treatment strategies ( Table 4 ) . Discussion As our study shows, mNGS has the potential to change the diagnostic approach to HAP, generating an unbiased detection of pathogens that would not be covered by traditional culture methods. This is in line with the increasing literature consensus that mNGS will radically change the landscape of infectious disease diagnosis by providing an unbiased picture of pathogens [ 7 , 8 , 12 , 13 ]. In the context of our study, it became clear that i) mNGS had a much greater sensitivity and can detect a much larger array of pathogens, including fastidious (difficult-to-culture) bacteria, fungi and viruses, compared with traditional culture methods [ 9 ]. ii) Conventional culture-based diagnostics miss many fastidious organisms which pose challenges to culture and often necessitate special growth conditions. Wilson et al. (2019) found that routine microbiologic testing is often insufficient to detect all potential neuroinvasive pathogens in CSF. Herein, they utilised mNGS of CSF collected from patients with meningitis or encephalitis to diagnose more neurologic infections and provided actionable information for a subset of patient [ 14 ]. This aligns with your study’s findings on the superiority of mNGS over traditional culture methods in detecting a wider array of pathogens, including those in culture-negative samples. This advantage of mNGS offering enhanced sensitivity is crucial for accurate and timely diagnosis in critically ill patients. The clinical implications of mNGS are significant. In the responders, 25.6% cases had a change in treatment regimen based on the mNGS results. Likewise, in the non-responders, 28% treatment regimens were changed. The significance of mNGS in facilitating targeted antimicrobial therapy is highlighted in this study of hospital-acquired infections. Gu and colleagues’ study also stresses the value of blood mNGS in infectious patients with mild and non-specific symptoms of infection. Indeed, they showed that blood mNGS can be used as a supplement to traditional laboratory examination and should be performed as early as possible to guide clinicians to perform appropriate antimicrobial intervention in a timely and effective manner. In their study, therapeutic regimens were changed for 70.3% cases (149/212) based on mNGS results [ 15 ]. The detection of polymicrobial infections and antibiotic resistance genes through metagenomic mNGS represents a critical advancement in the diagnosis and management of HAP. Traditional culture-based diagnostic methods often miss polymicrobial infections due to their inability to grow multiple pathogens simultaneously under standard laboratory conditions. In this study, mNGS identified polymicrobial infections in 28% of cases, a detection rate significantly higher than that observed with culture-based methods. Polymicrobial infections, particularly in the hospital setting, are clinically important as they are often associated with more severe disease progression and higher rates of treatment failure. This finding underscores the importance of mNGS as a diagnostic tool capable of providing a more comprehensive pathogen profile, allowing for more targeted and effective treatment strategies. Equally important is the detection of antibiotic resistance genes in 30% of samples. The ability of mNGS to simultaneously identify pathogens and their associated resistance genes is a significant advantage over traditional methods, which require separate testing for antimicrobial susceptibility. This real-time detection of resistance markers enables clinicians to adjust antimicrobial therapy more precisely, reducing the risk of prolonged ineffective treatment and the spread of resistant organisms. These findings highlight the clinical value of mNGS in guiding more precise and effective antimicrobial treatments in hospital-acquired infections. Identification of antibiotic resistance genes was the key finding of our study. Resistance genes tetM, ErmB and a number of beta-lactamases were picked up, enabling comprehensive data for antibiotic stewardship, in line with another mNGS study conducted by Gan et al. who reported the ability of mNGS to identify resistance genes targeting different antibiotics in the treatment of severe pneumonia in paediatric patients [ 16 ]. Our work confirms that mNGS has an important impact on patient clinical outcome by identifying the pathologies, where conventional methods are frequently negative, leading to an increase in the detection of severe infection-causing, and multi-drug resistant (hard-to-treat) bacteria, such as Pseudomonas, Klebsiella and Acinetobacter, as well as fungal pathogens such as Candida and Aspergillus. These results are confirmed by the major studies in the field, highlighting the key role of broad pathogen detection to decide targeted antibiotic and antifungal therapies [ 17 , 18 ]. The identification of viral pathogens, including HSV1, CMV, and EBV, further emphasizes the diagnostic utility of mNGS, particularly in immunocompromised patients. While these viruses are commonly found in the population, their detection in a hospital setting is clinically significant as they can cause opportunistic infections in immunosuppressed individuals. In this study, the presence of these viral pathogens was confirmed in patients with underlying conditions that could predispose them to viral reactivation. Their detection provided important information for clinicians to tailor antiviral therapies, particularly in cases where bacterial or fungal pathogens alone could not explain the severity of the illness. prompt and accurate identification of these viruses may lead to better patient outcomes, reduced hospital stays, and lower healthcare costs​ [ 6 , 19 ]. The cost-effectiveness of mNGS is still a matter of debate. The capital costs are high, but the expense of hospital stays, targeted treatments and better outcomes might quickly allow hospitals to recoup the investment. Jing et al (2021) forecast that costs will continue to decrease as technology improves and becomes more widely adopted [ 20 ]. Meanwhile, portable sequencing devices and point-of-care mNGS will transform the diagnosis of infectious diseases by allowing rapid detection of pathogens at the patient bedside [ 21 ] Although currently limited to specialized centers with the required infrastructure and bioinformatics expertise, the adoption of mNGS is expected to grow over the next 5 to 10 years as sequencing technology becomes more cost-effective and accessible. In addition, ongoing improvements in automated data analysis tools will reduce the need for highly specialized bioinformatics staff, further accelerating the integration of mNGS into routine clinical microbiology. Public health efforts to combat antimicrobial resistance are likely to drive this shift, as mNGS provides rapid, comprehensive data on pathogen detection and resistance profiles. Thus, mNGS is anticipated to become a staple in diagnostic laboratories, particularly in hospitals dealing with high-risk infections, as early as the next decade​. While these results are promising, several key limitations of our study need to be recognized. The retrospective design and our single-center setting are likely to generate selection bias and, therefore, restrict the generalizability of our outcomes. Second, the small sample size, particularly that of the non-responder group, might limit statistical power and should be appropriately addressed in the analysis and interpretation of our data. Larger, multicenter cohort studies are warranted in the future to confirm our results and assess the cost-effectiveness of mNGS in clinical practice. Conclusion In conclusion, our study demonstrated the remarkable benefits of mNGS over traditional culture in the diagnosis of HAP. The combined ability to provide entire pathogen detection and to profile antibiotic sensitivity makes mNGS a new pillar of modern infectious disease control. Certain technical and economic problems still hinder the practical application of mNGS in a clinical setting. But as the platform matures and its price becomes affordable, mNGS will soon be an integral part of clinical microbiology, to change the paradigm in diagnosis and treatment of infectious diseases. Abbreviations mNGS (Metagenomic Next-Generation Sequencing), HAP (Hospital-Acquired Pneumonia), CAP (Community-Acquired Pneumonia), CSF (Cerebrospinal Fluid), BALF (Bronchoalveolar Lavage Fluid), PSI (Pneumonia Severity Index), CPIS (Clinical Pulmonary Infection Score), FRAIL (Frailty Scale), APACHE II (Acute Physiology and Chronic Health Evaluation II), HbA1c (Hemoglobin A1c), ALB (Albumin), PA (Prealbumin), WBC (White Blood Cell Count), CRP (C-Reactive Protein), IL-6 (Interleukin-6), Cr (Creatinine), MDR (Multidrug-Resistant), HSV1 (Herpes Simplex Virus 1), CMV (Cytomegalovirus), and EBV (Epstein-Barr Virus). Declarations Ethics approval and consent to participate The study was approved by the institutional review board at the Beijing Rehabilitation Hospital, affiliated to Capital Medical University, Department of Respiratory and Critical Care Medicine, China. Clinical trial number: not applicable. Author Contributions QL, JG, CZ, TZ, HG, and BY have participated in the data collection and contributed to writing the manuscript. BZ, JW and HJ have analyzed the data, performed the interpretation and revised the manuscript critically. All authors have read and approved the final manuscript. Funding statement This work has received specialized funding for scientific research from Beijing Rehabilitation Hospital of Capital Medical University (2022-007). Informed Consent Statement Not applicable. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Conflicts of Interest The authors declare no conflict of interest. Supplementary Materials Not applicable. Acknowledgments Not applicable. References He S, Xiong Y, Tu T, Feng J, Fu Y, Hu X, et al. Diagnostic performance of metagenomic next-generation sequencing for the detection of pathogens in cerebrospinal fluid in pediatric patients with central nervous system infection: a systematic review and meta-analysis. BMC Infectious Diseases. 2024;24(1):103. Chen H, Huang Q, Wu W, Wang Z, Wang W, Liu Y, et al. Assessment and clinical utility of metagenomic next-generation sequencing for suspected lower respiratory tract infections. European Journal of Medical Research. 2024;29(1):213. Miao Q, Ma Y, Wang Q, Pan J, Zhang Y, Jin W, et al. Microbiological Diagnostic Performance of Metagenomic Next-generation Sequencing When Applied to Clinical Practice. Clinical Infectious Diseases. 2018;67(suppl_2):S231-S40. Wang C, Yan D, Huang J, Yang N, Shi J, Pan S, et al. 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Clinical Evaluation of an Improved Metagenomic Next-Generation Sequencing Test for the Diagnosis of Bloodstream Infections. Clinical Chemistry. 2021;67(8):1133-43. Bloemen B, Gand M, Vanneste K, Marchal K, Roosens NHC, De Keersmaecker SCJ. Development of a portable on-site applicable metagenomic data generation workflow for enhanced pathogen and antimicrobial resistance surveillance. Scientific Reports. 2023;13(1):19656. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5235477","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374316444,"identity":"70a341ba-3cdf-4505-a49a-ed948fc8e268","order_by":0,"name":"Bin Zhang","email":"","orcid":"","institution":"Beijing Rehabilitation Hospital of Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Zhang","suffix":""},{"id":374316445,"identity":"46fd6c55-6b16-43a6-858d-0398d1b64019","order_by":1,"name":"Jianjun Wang","email":"","orcid":"","institution":"Beijing Rehabilitation Hospital of Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Wang","suffix":""},{"id":374316446,"identity":"08119cf2-3671-4c23-8032-305afee826a1","order_by":2,"name":"Qing Li","email":"","orcid":"","institution":"Beijing Rehabilitation Hospital of Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Li","suffix":""},{"id":374316447,"identity":"989ea1dc-b42b-4452-8dac-5b9b34776110","order_by":3,"name":"Jingyi Ge","email":"","orcid":"","institution":"Beijing Rehabilitation Hospital of Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"Ge","suffix":""},{"id":374316448,"identity":"7c456acd-0ee6-4e42-8749-db7faf12bbc4","order_by":4,"name":"Chenxi Zhang","email":"","orcid":"","institution":"Beijing Rehabilitation Hospital of Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenxi","middleName":"","lastName":"Zhang","suffix":""},{"id":374316449,"identity":"a4fbd30f-5699-4590-b857-f69229d4790f","order_by":5,"name":"Ting Zhou","email":"","orcid":"","institution":"Beijing Rehabilitation Hospital of Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Zhou","suffix":""},{"id":374316450,"identity":"9fd51356-1644-4fd1-a29b-c6a35e52b75f","order_by":6,"name":"Haiming Guo","email":"","orcid":"","institution":"Beijing Rehabilitation Hospital of Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haiming","middleName":"","lastName":"Guo","suffix":""},{"id":374316451,"identity":"77c080d1-e704-4393-a6c2-5c681bf39299","order_by":7,"name":"Bo Yang","email":"","orcid":"","institution":"Beijing Rehabilitation Hospital of Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Yang","suffix":""},{"id":374316452,"identity":"a039c645-5d14-4ce6-abd9-3c6336ae9ab7","order_by":8,"name":"Hongying Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYPACGx42+QcMBxgqGHiI1ZImx8+QANRyhngth40lGxIYGBjbiFCr23746IYfFcyJGw6cMTxcOK9Oxpz9AOOHjzm4tZidSUu72XOGLXHDwR6DwzO3Heax7Elglpy5DY+WAzlmN3jbeBI3HGZLOMy77QCPwYEENmZefFrOvzG7+bdNInHDMZCWOXU8BucfENByI8fsNm+bgbFkD/OBw7wNzDwGNwjZcuNZ2m2ZMwly/BJALTOOHQZqediM3y/nk4/dfFPxn4dNgrH5c0FNnb3B+eSDHz7i0YICmCEUYwOR6hFaRsEoGAWjYBSgAgBV6FhmKNd4ngAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Rehabilitation Hospital of Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hongying","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-10-10 01:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5235477/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5235477/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70285509,"identity":"17319edc-62ef-414b-82cf-3fb1c4d88ef4","added_by":"auto","created_at":"2024-12-01 16:22:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":267864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Pathogens Detected by Metagenomic Next-Generation Sequencing (mNGS) and Standard Methods.\u003c/strong\u003emNGS achieved an overall pathogen detection rate of 92%, significantly outperforming traditional culture methods, which detected pathogens in 72% of the cases. The Venn diagram in this figure highlights the overlap between the two methods, showing that mNGS detected 20% more pathogens that were missed by culture, particularly in non-responder patients. Non-responders, who failed to improve with standard antibiotic therapy, had a higher incidence of difficult-to-culture pathogens like Aspergillus and Pseudomonas, which were often only identified by mNGS.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5235477/v1/b8d74ab1827155fd1e73c012.png"},{"id":70285510,"identity":"70bcd364-c446-4a84-bfcb-68461f27d5fe","added_by":"auto","created_at":"2024-12-01 16:22:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1418472,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathogen Profiles Detected in Responders and Non-Responders.\u003c/strong\u003e The distribution of detected pathogens between responders and non-responders is shown, with mNGS consistently identifying a broader spectrum of bacterial, fungal, and viral pathogens in both groups. However, in non-responders, mNGS was particularly valuable, identifying critical pathogens that traditional methods missed, including drug-resistant strains. This higher detection rate in non-responders underscores mNGS’s role in guiding more precise treatment adjustments for patients who fail to respond to initial therapies.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5235477/v1/a7e4afe497968377de684737.png"},{"id":70285508,"identity":"98dd9b80-1b5f-430e-854c-7011acc99e0b","added_by":"auto","created_at":"2024-12-01 16:22:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171327,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverlap and Unique Detections Between mNGS and Culture-based Methods.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5235477/v1/50406b0ad4f3c697e2fc31eb.png"},{"id":70286528,"identity":"1fb8975b-ac67-4ea1-a74a-7375a82d003a","added_by":"auto","created_at":"2024-12-01 16:38:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3135983,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5235477/v1/8de80e6b-dccf-4512-8397-2b7bee75e43f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Efficacy and Diagnostic Value of Metagenomic Next-Generation Sequencing (mNGS) in Hospital-Acquired Pneumonia: A Stratified Retrospective Study of Responders and Non- Responders","fulltext":[{"header":"Background","content":"\u003cp\u003eInfectious diseases remain a major problem, despite modern advances in clinical medicine and epidemiology. High morbidity and mortality and low threshold for complications demand that an acute diagnostic method be robust and have sufficient sensitivity to rule out small, abnormal populations of pathogens before mortality from infection ensues. Traditional diagnostic methods for pneumonia, such as culture-based techniques and polymerase chain reaction (PCR), have been the standard for pathogen detection but face significant limitations. Culture methods can take days to yield results and may miss fastidious or slow-growing organisms, leading to delayed or inadequate treatment​. PCR, while faster, is limited in its scope as it can only detect specific pathogens for which primers are designed​. These methods also struggle to detect polymicrobial infections, which are common in hospital-acquired pneumonia (HAP) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, Metagenomic next-generation sequencing (mNGS) is an unbiased approach that can detect all potential pathogens, including bacteria, viruses, fungi, and parasites, from a single sample. mNGS sequences the entire nucleic acid content of a sample, offering a more comprehensive view of the infection profile. Studies have demonstrated that mNGS not only improves pathogen detection in cases where traditional methods fail but also identifies antimicrobial resistance genes, providing crucial information for targeted therapy​ ​[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe clinical application of mNGS has been largely unproblematic, with studies demonstrating its superior sensitivity and specificity compared to traditional methods, particularly in complex infections or when initial diagnostics are inconclusive [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This was especially crucial for identifying rare or novel pathogens, often implicated in emerging infectious diseases and pandemics like SARS-CoV, MERS-CoV, and SARS-CoV-2. The rapid identification of causative agents is paramount in mitigating the threat of infectious disease outbreaks, and mNGS has proven instrumental in this regard, as evidenced by its pivotal role in the identification and genomic surveillance of the SARS-CoV-2 virus during the COVID-19 pandemic [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn spite of these benefits, there are barriers to the incorporation of mNGS into routine clinical diagnostics\u0026ndash;not the least of which is the added cost of sequencing and the requirements for bioinformatics support. Interpretation of mNGS data also requires in-depth knowledge of genomics, which is unlikely to exist in all clinical settings, as well as expertise in managing voluminous sequencing data containing a high proportion of human host DNA, which requires sophisticated computational tools to mine for pathogen-related information [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have already evaluated the clinical utility of mNGS in different medical areas, such as the diagnosis of respiratory, central nervous system and bloodstream infections, reporting its potential to critically modify clinical management and prognosis by providing rapid and reliable diagnoses. In particular, mixed infections have been identified as one of the most significant advantages of mNGS for directing therapy and impacting patient prognosis. This supports the hypothesis that mNGS could hold a novel and important role in clinical microbiology [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003emNGS also yields valuable epidemiological information, such as which infections are spreading, and how pathogens are evolving or adapting to antibiotics, information that is vital for controlling outbreaks and developing prophylactic measures. That became crystal clear when the world was reeling from recent global health crises, when suspect specimens from hospitalised patients were routed through mNGS for real-time surveillance and quick responses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHAP and community-acquired pneumonia (CAP) represent two distinct clinical challenges in terms of diagnosis and management. CAP typically arises outside of healthcare settings, and its causative pathogens are often susceptible to commonly used antibiotics, making standard culture-based diagnostic methods relatively effective in identifying the responsible organisms. However, CAP diagnosis is not without challenges; the overlap of symptoms with other respiratory illnesses and the presence of atypical pathogens can complicate the diagnostic process [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, HAP, which occurs 48 hours or more after hospital admission, is often associated with a higher incidence of multidrug-resistant (MDR) organisms. These pathogens can be missed by traditional culture methods due to their fastidious growth requirements or slow replication rates. Furthermore, the polymicrobial nature of HAP and the frequent co-infection with fungi and viruses, especially in immunocompromised patients, further complicates diagnosis. These limitations underscore the need for advanced diagnostic techniques such as metagenomic next-generation sequencing (mNGS), which provides a comprehensive, unbiased identification of pathogens by sequencing all nucleic acids in a sample [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to address the critical diagnostic gap in HAP by evaluating the clinical efficacy and diagnostic value of mNGS, particularly in detecting MDR organisms and polymicrobial infections that are often missed by standard diagnostic techniques. By comparing mNGS with traditional culture methods, we aim to demonstrate the superiority of mNGS in providing rapid, accurate, and actionable data for the management of HAP, ultimately improving patient outcomes. Given the global rise of antibiotic resistance, this study holds significance in advancing pathogen detection methods, not only in hospital settings but also for cases where traditional methods fail, such as in CAP.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003e This retrospective cohort study was conducted in the Beijing Rehabilitation Hospital, affiliated to Capital Medical University, Department of Respiratory and Critical Care Medicine, China, from August 2021 to January 2024. It addressed adult patients admitted to hospital for HAP to evaluate the diagnostic value of mNGS versus culture. The study was approved by the institutional review board at the hospital. Given the retrospective design, consent for the study was waived, although all of these patients or their families had previously consented to the acquisition of sample for medical purposes. All data were kept anonymous, under strict confidentiality and an ethical framework.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003ePatients eligible for inclusion in the study were those diagnosed with HAP, defined as pneumonia that developed 48 hours or more after hospital admission. All patients exhibited persistent symptoms, including fever, elevated inflammatory markers, and respiratory distress, despite initial antibiotic treatment based on sputum culture results, yet still had a poor prognosis characterised by fever and high inflammatory markers persisting two to three days after treatment. The 300 patients were included in the study, divided into responders and non-responders according to their clinical response: 250 responders and 50 non-responders.\u003c/p\u003e \u003cp\u003eBronchoalveolar lavage fluid (BALF) samples were collected for both traditional culture and mNGS testing. Only adult patients aged 18 years and older were included in the study. Patients with severe liver or kidney dysfunction, or those with unstable vital signs, were excluded.\u003c/p\u003e \u003cp\u003eTo maintain focus on pathogen detection and drug resistance, we streamlined the clinical evaluation indicators. Key indicators included: Oxygenation Index, a measure of lung function, defined as the ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2); Pneumonia Severity Index (PSI), a widely used scoring system to assess the severity of pneumonia; Clinical Pulmonary Infection Score (CPIS), a scoring tool used to gauge the severity of pneumonia based on clinical, radiological, and microbiological findings. Other health indices like frailty (FRAIL scale), comorbidity burden (Charlson Comorbidity Index), and daily living activities (Barthel Index) were recorded but simplified for clarity in the analysis of the primary focus, which was pathogen detection and treatment adjustments.\u003c/p\u003e \u003cp\u003ePatients who were excluded were those with serious liver or kidney dysfunction, patients discharged or died before receiving NGS results, those without consent for collection of bronchoalveolar lavage fluid for NGS testing, those with unstable vital signs, those with incomplete clinical data and unclear prognosis.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003e Clinical and laboratory data was obtained from the electronic medical records which had record of demographics, clinical scoring indices, as well as routine haematological and biochemical parameters. Pneumonia-related laboratory tests such as hemoglobin A1c (HbA1c), albumin (ALB), prealbumin (PA), white blood cell count (WBC), C-reactive protein (CRP), interleukin-6 (IL-6) and creatinine (Cr) were also collected. Fourteen clinical scoring systems were used in this study including patient age, gender, Pneumonia Severity Index (PSI), Clinical Pulmonary Infection Score (CPIS), FRAIL scale for frailty, the Charlson comorbidity index, Barthel Index (BI) for daily living activities, Acute Physiology and Chronic Health Evaluation II (APACHE II), and routine biochemical parameters including haemoglobin A1c (HbA1c), albumin (ALB), prealbumin (PA), white blood cell count (WBC), reactive protein (CRP), interleukin-6 (IL-6 ) and creatinine (Cr).\u003c/p\u003e\n\u003ch3\u003eTraditional Culture Methods\u003c/h3\u003e\n\u003cp\u003eSamples were processed using standard microbiological techniques for the isolation and identification of bacterial pathogens, including aerobic and anaerobic cultures, and identification by automated systems (e.g., Vitek-2). Agents were also tested for their drug sensitivity which helps to guide antibiotic treatment prescriptions.\u003c/p\u003e\n\u003ch3\u003emNGS and Analyses\u003c/h3\u003e\n\u003cp\u003eFor mNGS, a volume of fluid from the bronchoalveolar lavage was extracted and processed into DNA using that can maintain constant performance and avoid cross-contamination. The libraries were then constructed and sequenced on an Illumina platform. The raw data were submitted to a bioinformatics pipeline, which can effectively filter out human DNA residues and identify the microbial sequences and match the identification results via a number of pathogen databases.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of mNGS Results\u003c/h2\u003e \u003cp\u003eBALF samples were collected from each patient under sterile conditions. DNA was extracted using the Qiagen QIAamp DNA Mini Kit, following the manufacturer\u0026rsquo;s protocol. The extracted DNA was quantified using a Qubit Fluorometer to assess concentration and purity. For sequencing, Illumina's NextSeq 550 platform was used to generate high-throughput sequencing data. Libraries were prepared using the Illumina Nextera XT DNA Library Preparation Kit, which involves random fragmentation of the DNA followed by adapter ligation. The samples were then sequenced in a paired-end format with a read length of 150 bp. To maintain performance consistency and minimize cross-contamination, rigorous quality control measures were applied at each step of the process, including the use of negative controls during library preparation and sequencing. Raw data were processed through a bioinformatics pipeline, with human DNA sequences filtered out, allowing for the identification of microbial sequences through comparison with a reference pathogen database.\u003c/p\u003e \u003cp\u003eThe interpretation of mNGS data was performed by a team of clinical microbiologists and bioinformatics specialists. Results were categorized based on pathogen load and diversity, with significant findings reviewed in the context of each patient\u0026rsquo;s clinical presentation to ascertain their relevance to the ongoing treatment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePathogen Detection and Analysis\u003c/h3\u003e\n\u003cp\u003eThe study evaluated the spectrum and frequency of pathogens detected by mNGS compared to traditional methods. The efficacy of each technique in identifying causative agents in patients with complex clinical presentations was assessed, with particular attention to the detection of polymicrobial infections and antibiotic resistance markers.\u003c/p\u003e \u003cp\u003eThe positive results from mNGS were determined using both quantitative and qualitative criteria. For each sample, the microbial load was calculated based on the proportion of microbial reads out of the total sequenced reads. A pathogen was considered significant if its relative abundance exceeded 1% of the total microbial reads, a threshold supported by previous studies that correlate such levels with clinically relevant infections. The clinical significance of detected pathogens was assessed through several factors: pathogen abundance, virulence (evaluated through known pathogenicity databases such as the NCBI Pathogen Database), and the presence of polymicrobial infections, where multiple pathogens exceeding the 1% threshold suggested a higher likelihood of clinical relevance. Additionally, mNGS results were cross-referenced with the detection of antibiotic resistance genes, with resistant pathogens prioritized for significance, especially in cases where these were missed by traditional culture methods. Finally, pathogen identification was corroborated with the clinical presentation of each patient, including symptom severity and inflammatory markers, ensuring that only pathogens consistent with the clinical course were considered as causative agents. This multi-factor approach minimized the risk of false positives from commensal organisms or contaminants often found in respiratory samples, ensuring a higher diagnostic accuracy.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS software. We used descriptive statistics to summarise patient demographics and clinical characteristics. We defined sensitivity and specificity of mNGS versus traditional culture methods to compare using Chi-square tests for categorical data and t-tests for continuous variables. Statistical significance was set at a p-value less than 0.05. Adjustments in antibiotic therapy based on mNGS findings were also analyzed to evaluate the impact on patient outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eAnalysis of demographic data \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e revealed that the mean age of responders was significantly lower than that of non-responders, with responders averaging 64.7 years compared to 69.1 years for non-responders (p\u0026thinsp;=\u0026thinsp;0.005). Gender distribution across the groups showed a majority of males in both responders (67.2%) and non-responders (74%), however the difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.4371).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Clinical Characteristics of Study Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse (n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Response (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years, mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.7 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.1 (9.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (n(%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Female\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Male\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxygenation Index (median(range))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270.5 (48\u0026thinsp;~\u0026thinsp;762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234.5 (58\u0026thinsp;~\u0026thinsp;450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eRank-sum test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePSI score (median(range))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (50\u0026thinsp;~\u0026thinsp;165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.5 (105\u0026thinsp;~\u0026thinsp;165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCPIS score (median(range))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (6\u0026thinsp;~\u0026thinsp;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (8\u0026thinsp;~\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFRAIL (median(range))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2\u0026thinsp;~\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3\u0026thinsp;~\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson index CCL (median(range))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1\u0026thinsp;~\u0026thinsp;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2\u0026thinsp;~\u0026thinsp;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBarthel Index BI (median(range))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (0\u0026thinsp;~\u0026thinsp;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026thinsp;~\u0026thinsp;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPACHE II (median(range))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (6\u0026thinsp;~\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (11\u0026thinsp;~\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c (median(range))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7 (3.7\u0026thinsp;~\u0026thinsp;9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.85 (4.3\u0026thinsp;~\u0026thinsp;9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALB (g/L, (median(range)))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.75 (19\u0026thinsp;~\u0026thinsp;47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.9 (22.7\u0026thinsp;~\u0026thinsp;38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePA (g/L, (median(range)))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16 (0.02\u0026thinsp;~\u0026thinsp;16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.125 (0.03\u0026thinsp;~\u0026thinsp;0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC (10^9, (median(range)))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.78 (1.27\u0026thinsp;~\u0026thinsp;24.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.67 (2.88\u0026thinsp;~\u0026thinsp;28.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP (mg/L, (median(range)))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.65 (0\u0026thinsp;~\u0026thinsp;240.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.7 (5.7\u0026thinsp;~\u0026thinsp;363.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-6 (pg/ml, (median(range)))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.21 (0\u0026thinsp;~\u0026thinsp;1395.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.53 (20.04\u0026thinsp;~\u0026thinsp;428.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCr (umol/L, (median(range)))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.65 (16.3\u0026thinsp;~\u0026thinsp;210.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.75 (25.9\u0026thinsp;~\u0026thinsp;126.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA range of clinical scores and health indices were assessed \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, revealing notable differences between responders and non-responders. The median oxygenation index was significantly higher in responders. Critical clinical scores such as the PSI and CPIS were considerably lower in responders, suggesting less severe clinical presentations compared to non-responders. The PSI scores ranged from 50 to 165 for responders and 105 to 165 for non-responders, showing a significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Similarly, CPIS scores also differed significantly, with responders averaging lower scores indicative of better clinical status.\u003c/p\u003e \u003cp\u003eHealth indices further demonstrated disparities between the two groups. The FRAIL scale, used to assess frailty, showed that responders were less frail compared to non-responders, with scores significantly differing (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The Charlson Comorbidity Index and the BI, which measures daily living activities, also reflected better health status in responders. Likewise, the APACHE II scores, which estimate ICU mortality risk, were lower in responders, suggesting a better prognosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eLaboratory findings complemented these clinical assessments, with several key biomarkers showing significant differences between the groups. Notably, ALB and PA levels were lower in non-responders, reflecting potentially poorer nutritional status or more severe disease states. Inflammatory markers such as CRP and IL-6 were significantly elevated in non-responders, indicating higher levels of systemic inflammation and potentially more severe or uncontrolled infectious processes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for both). However, no significant differences were observed in HbA1c and Cr levels between the groups, suggesting similar baseline metabolic and renal functions \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePathogen Detection\u003c/h2\u003e \u003cp\u003eThe analysis revealed that mNGS identified a higher diversity of pathogens compared to traditional methods \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The most frequently identified bacteria were Pseudomonas, Klebsiella, Acinetobacter, Streptococcus, and Staphylococcus. In terms of fungi, Candida, Aspergillus, Pneumocystis, and Rhizopus were commonly detected. Viruses identified included HSV1, human cytomegalovirus (CMV), Epstein-Barr virus (EBV), varicella zoster virus, Primate erythroparvovirus 1, human herpesvirus 7, and human adenovirus B \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003emNGS detected a wide range of pathogens in both responder and non-responder groups. The frequency of bacterial detection was higher in the responder group compared to non-responders \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Notably, Pseudomonas and Klebsiella were the predominant bacteria found in both groups. Candida was the most frequently detected fungus in both groups. Aspergillus and Pneumocystis were more prevalent in the non-responder group. HSV1, CMV, and EBV were among the most common viruses detected. The presence of these viruses was higher in non-responders compared to responders \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVenn diagrams were used to illustrate the overlap and unique detections between mNGS and standard methods. They highlighted the added value of mNGS in identifying additional pathogens, which played a crucial role in guiding targeted antimicrobial therapy \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTreatment Adjustments\u003c/h2\u003e \u003cp\u003eBased on the results of mNGS, treatment regimens were adjusted for both responders and non-responders. Treatment regimens were modified in 64 (25.6%) of the 250 responders and 14 (28%) of the 50 non-responders, showing no significant difference between the groups (p\u0026thinsp;=\u0026thinsp;0.86). The addition of antimicrobial agents was necessary in a majority of cases, with 186 (74.4%) of responders and 36 (72%) of non-responders receiving additional agents \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTreatment Adjustments Based on mNGS Results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModifications, n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Response\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64(25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdd agent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186(74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClinical Outcomes\u003c/h2\u003e \u003cp\u003eClinical outcomes were assessed and compared between responders and non-responders. The median duration of ICU stay was 18 days for responders (range: 0\u0026ndash;55 days) compared to 15.5 days for non-responders (range:2\u0026ndash;31 days), although this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.786). Responders required a significantly shorter duration of mechanical ventilation (median: 3.5 days, range: 0\u0026ndash;30 days) compared to non-responders (median: 12 days, range: 0\u0026ndash;26 days) (p\u0026thinsp;=\u0026thinsp;0.014). A higher proportion of responders underwent tracheostomy (64.8%) compared to non-responders (46%) (p\u0026thinsp;=\u0026thinsp;0.019) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Outcomes in Responders and Non-Responders.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Response\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime in ICU\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(days, median(range))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(0\u0026thinsp;~\u0026thinsp;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5(2\u0026thinsp;~\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of mechanical ventilation\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(days, median(range))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5(0\u0026thinsp;~\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(0\u0026thinsp;~\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTracheostomy(n(%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162(64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eResistance Genes\u003c/h2\u003e \u003cp\u003eOur analysis identified a variety of resistance genes associated with the pathogens detected in both responders and non-responders \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The analysis revealed the presence of multiple resistance genes, highlighting the complexity of treating infections in these patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Resistance Genes Among Detected Pathogens. This table lists the various resistance genes identified in pathogens from both responders and non-responders, along with the specific pathogens harboring these genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacteria and Fungus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResistance gene(s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Candida, Stenotrophomonas, Enterococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, ErmB, sul1, armA, AAC(3)-Ia, APH(3\u0026rsquo;)-Ia, catB8, OXA beta-lactamase, AAC(6\u0026rsquo;), ANT(3\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, enterococcus, aspergillus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPH(3')-llb, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStreptococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, sul2, armA, APH(3'')-Ib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP20, P21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Candida, Stenotrophomonas, aspergillus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, rmtB, TEM-206, AAC(3)-IIc, APH(3')-Iib, KPC beta-lactamase, AAC(6'), ANT(3'')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Candida, Stenotrophomonas, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, rmtB, OXA-50, APH(3'')-Ib, APH(3')-la, APH(3')-llb, pseudomonas aeruginosa catB7, TEMbeta-lactamase, OXAbeta-lactamase, CTX-Mbeta-lactamase, KPC beta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP29, P31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Acinetobacter, Candida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, CTX-Mbeta-lactamase, ANT(3'')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP30, P265, P266, P269\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Acinetobacter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, sul2, CTX-Mbeta-lactamase, ANT(3''), armA, TEM-206, AAC(3)-la, AAC(3)-IIc, APH(3')-la, APH(6)-Id, catB8, TEMbeta-lactamase, OXAbeta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Acinetobacter, Corynebacterium, Candida, Nakaseomyces, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etet(A), tetM, sul1, sul2, armA, rmtB, AAC(6')-Ia, APH(3')-IIa, tetracycline-resistant ribosomal protection protein, TEMbeta-lactamase, OXAbeta-lactamase, ANT(3'')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Corynebacterium, Candida, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, sul2, armA, rmtB, AAC(6')-Ia, APH(3')-IIa, tetracycline-resistant, TEMbeta-lactamase, OXAbeta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP37, P38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Candida, Nakaseomyces, Stenotrophomonas, aspergillus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, rmtB, TEM-206, AAC(3)-IIc, APH(3')-Iib, SHVbeta-lactamase, ACT beta-lactamase, KPC beta-lactamase, AAC(6'), ANT(3'')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP39, P40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Candida, Stenotrophomonas, aspergillus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, rmtB, TEM-206, APH(3')-Iib, ACT beta-lactamase, KPC beta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP45, P48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, sul2, mecAAAC(6')-Ia, APH(3'')-Ib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Acinetobacter, Streptococcus, Candida, Stenotrophomonas, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, ErmA, ErmB, sul1, sul2, mecA, AAC(6')-Ia, APH(3'')-Ib, APH(3')-la, APH(6)-Id, AAC(6')-Ie-APH(2'')-Ia, TEMbeta-lactamase, OXAbeta-lactamase, KPC beta-lactamase, ANT(3''), major facilitator superfamily(MFS) ant-ibiotic efflux pump\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Streptococcus, Candida, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, sul1, sul2, mecA, AAC(6')-Ia, APH(3'')-Ib, APH(3')-la, APH(6)-Id, TEMbeta-lactamase, OXAbeta-lactamase, KPC beta-lactamase, ANT(3'')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP57, P59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Candida, mycobacterium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosa, armA, APH(3'')-Ib, APH(3')-la, APH(3')-Iib, APH(6)-Id, AAC(3), pseudomonas aeruginosa catB7, TEMbeta-lactamase, OXAbeta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP60, P67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Candida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosa, armA, APH(3'')-Ib, APH(3')-la, APH(3')-Iib, pseudomonas aeruginosa catB7, TEMbeta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP62, P63, P84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPH(3')-Iib, sul1, sul2, APH(3'')-Ib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, prevotella, Fusobacterium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTEMbeta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP80, P81, P126, P278\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Achromobacter, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, armA, OXA-114a, APH(3'')-Ib, APH(3')-la, TEMbeta-lactamase, OXAbeta-lactamase\u003c/p\u003e \u003cp\u003eANT(3''), rmtB, SHVbeta-lactamase, CTX-Mbeta-lactamase, DHA beta-lactamase, KPC beta-lactamase, AAC(3), fosfomycin thiol transferase, catB8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP82, P130, P131, P149, P150, P151\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, sul2, armA, APH(3'')-Ib, APH(3')-la, TEMbeta-lactamase, ANT(3''), APH(6)-Id, OXAbeta-lactamase, adeJ, abeM, TEM-19, OXA-23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP83, P285, P286, P287\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcinetobacter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, APH(3'')-Ib, OXA beta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Corynebacterium, Stenotrophomonas, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, AAC(6')-Ie-APH(2'')-Ia, APH(6)-Id\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Stenotrophomonas, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, AAC(6')-Ie-APH(2'')-Ia, APH(6)-Id\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Candida, Stenotrophomonas, enterococcus, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etet(K), msrA, mecA, AAC(6')-Ie-APH(2'')-Ia, APH(3')-IIIa, major facilitator superfamily(MFS) ant-ibiotic efflux pump\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Stenotrophomonas, enterococcus, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etet(K), msrA, mecA, AAC(6')-Ie-APH(2'')-Ia, APH(3')-IIIa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP91, P95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Escherichia, aspergillus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOXA-114a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAchromobacter, Stenotrophomonas, Escherichia, aspergillus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOXA-114a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Streptococcus, Candida, Achromobacter, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosA, tetM, ErmB, OXA-50, AAC(6')-Ie-APH(2'')-Ia, APH(3')-Iib, pseudomonas aeruginosa catB7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP99, P260, P261\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Candida, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosA, tetM, OXA-50, AAC(6')-Ie-APH(2'')-Ia, pseudomonas aeruginosa catB7, APH(3')-llb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Streptococcus, Corynebacterium, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosA, tetM, APH(3')-la, APH(3')-Iib, APH(6)-Id, pseudomonas aeruginosa catB7, OXA-488 , chloramphenicol acetyltransferase(CAT)2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP101\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Corynebacterium, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosA, tetM, APH(3')-la, APH(3')-Iib, APH(6)-Id, pseudomonas aeruginosa catB7, chloramphenicol acetyltransferase(CAT)2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP108\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Streptococcus, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP109\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Streptococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP110\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Candida, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosA, ANT(2'')-Ia, sul1, armA, OXA-50, AAC(6')-Ie-APH(2'')-Ia, APH(3'')-Ib, APH(3')-la, APH(3')-IIb, APH(6)-Id, pseudomonas aeruginosa catB7, OXA \u0026minus;\u0026thinsp;488, OXAbeta-lactamase, AAC(3), ANT(3'')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP111\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Candida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosA, sul1, armA, OXA-50, AAC(6')-Ie-APH(2'')-Ia, APH(3'')-Ib, APH(3')-la, APH(3')-IIb, APH(6)-Id, pseudomonas aeruginosa catB7, AAC(3), ANT(3'')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP114\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Escherichia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epseudomonas aeruginosa catB7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP115\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Escherichia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epseudomonas aeruginosa catB7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP118\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Corynebacterium, Candida, Stenotrophomonas, staphylococcus, Elizabethkingia, Clavispora\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosA, sul1, OXA-50, APH(3')-Iib, APH(3')-Iic, tetracycline-resistant ribosomal pro-tection protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP119\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Corynebacterium, staphylococcus, Elizabethkingia, Clavispora\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, OXA-50, APH(3')-Iic, tetracycline-resistant ribosomal pro-tection protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP127\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Achromobacter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, rmtB, TEMbeta-lactamase, SHVbeta-lactamase, CTX-Mbeta-lactamase, DHA beta-lactamase, KPC beta-lactamase, AAC(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP140, P141\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Acinetobacter, Stenotrophomonas, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, sul1, mecA, armA, AAC(6')-Ie-APH(2'')-Ia, Erm23Sribosomal RNA, methyltransferase, OXAbeta-lactamase, ANT(3''), major facilitator superfamily(MFS) ant-ibiotic efflux pump fosfomycin thiol t-ransferase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP145, P146\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Acinetobacter, Corynebacterium, Candida, Nakaseomyces, Stenotrophomonas, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emsrA, APH(3')-la, APH(6)-Id, tetracycline-resistant ribosomal protection protein, AAC(3), ANT(3''), fosfomycin thiol transferase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP152, P153\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEscherichia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP154\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Acinetobacter, Stenotrophomonas, Mycotoruloides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eadeB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP155, P158\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Acinetobacter, Mycotoruloides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eadeB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP163\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Corynebacterium, Candida, Stenotrophomonas, proteus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eErmB, sul1, AAC(6')-Iz, AAC(6')-Ie-APH(2'')-Ia, APH(3'')-Ib, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7, OXA beta-lactamase, AAC(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP164\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Corynebacterium, Candida, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eErmB, sul1, AAC(6')-Iz, AAC(6')-Ie-APH(2'')-Ia, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7, OXA beta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP165\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Stenotrophomonas, Mycotoruloides, proteus, Elizabethkingia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, OXA-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP166\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Achromobacter, Stenotrophomonas, Escherichia, Mycotoruloides, proteus, Elizabethkingia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, OXA-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP167\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Achromobacter, Stenotrophomonas, Burkholderia, Elizabethkingia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, OXA-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP168\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Stenotrophomonas, Burkholderia, Elizabethkingia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, OXA-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP169\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Streptococcus, Corynebacterium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAC(6')-Ie-APH(2'')-Ia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP170\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Streptococcus, Corynebacterium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAC(6')-Ie-APH(2'')-Ia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP171, P172\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Nakaseomyces, enterococcus, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evanA, tet(A), tetM, ErmB, AAC(6')-Ii, AAC(6')-Ie-APH(2'')-Ia, APH(3')-IIIa, vanZA, SHV beta-lactamase, CTX-Mbeta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP177\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Corynebacterium, Candida, Achromobacter, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, ErmB, sul1, AAC(6')-Ie-APH(2'')-Ia, APH(3'')-Ib, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP178\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Corynebacterium, Candida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, ErmB, sul1, AAC(6')-Ie-APH(2'')-Ia, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP179, P180\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlebsiella, Stenotrophomonas, Haemophilus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etet(A), sul2, aadA2, TEM-88, SHV-9, QnrS8, OXA-1, KPC-14, CTX-M-90, AAC(6\u0026rsquo;)-Ib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP187\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Achromobacter, Stenotrophomonas, Mycotoruloides, staphylococcus, aspergillus, bordelella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA11, SHV-2A, OXA-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP189\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Stenotrophomonas, Mycotoruloides, staphylococcus, aspergillus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA11, SHV-2A, OXA-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Corynebacterium, Candida, Stenotrophomonas, Elizabethkingia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, ANT(2'')-Ia, APH(3')-llb, APH(3')-la, APH(6)-Id, pseudomonas aeruginosa catB7, OXAbeta-lactamase, AAC(3), AAC(6')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP192\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Achromobacter, Stenotrophomonas, Mycotoruloides, proteus, Burkholderia, Kerstersia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA6/aadA10, OXA-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP193\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAchromobacter, Stenotrophomonas, Mycotoruloides, proteus, Burkholderia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA6/aadA10, OXA-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP200, P201\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Escherichia, Mycotoruloides, staphylococcus, chryseobacterium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1 adeJ, abeM, TEM-147, TEM-19, OXA-23, OXA-51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP208\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Corynebacterium, Achromobacter, Stenotrophomonas, Escherichia, Mycotoruloides, staphylococcus, proteus, Elizabethkingia, Morganella, Clavispora\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, mecA, adeJ, abeM, aadA, TEM-19, TEM-116, OXA-51, OXA-23, ErmB, AAC(6\u0026rsquo;)-Ib, AAC(6\u0026rsquo;)-Ie_x0002_APH(2\u0026rdquo;)-Ia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP209\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Corynebacterium, Achromobacter, Mycotoruloides, staphylococcus, proteus, Elizabethkingia, Clavispora\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, adeJ, abeM, aadA, TEM-116, OXA-51, OXA-23, ErmB, AAC(6\u0026rsquo;)-Ib, AAC(6\u0026rsquo;)-Ie_x0002_APH(2\u0026rdquo;)-Ia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP217, P218\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Achromobacter, Stenotrophomonas, Mycotoruloides, proteus, Elizabethkingia, Haemophilus, bordelella, Enterobacter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA11, OXA-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP230\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Achromobacter, Stenotrophomonas, Elizabethkingia, Enterobacter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, adeJ, abeM, aadA, TEM-132, SHV-2A, OXA-23, OXA-51, KPC-3, CTX-M-104, AAC(6\u0026rsquo;)-Ib\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP231\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Achromobacter, Stenotrophomonas, Elizabethkingia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, adeJ, abeM, TEM-132, SHV-2A, OXA-23, OXA-51, CTX-M-104, AAC(6\u0026rsquo;)-Ib\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP234, P235\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Achromobacter, staphylococcus, Elizabethkingia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, mecA, adeJ, abeM, aadA, TEM-150, TEM-116, OXA-51, OXA-23, AAC(6\u0026rsquo;)-Ib\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP236, P237, P238\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcinetobacter, Mycotoruloides, staphylococcus, Burkholderia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emecA, adeJ, OXA-423, ErmA, ADC-25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP239, P241\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Achromobacter, Mycotoruloides, Enterobacter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA, OXA-50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP240\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Acinetobacter, Achromobacter, Mycotoruloides, Elizabethkingia, Enterobacter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA, OXA-50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP242\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Achromobacter, Mycotoruloides, Gardnerella, serratia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA, TEM-213, SHV-5, OXA-50, OXA-114a, KPC-1, AAC(3)-IId\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP243\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Mycotoruloides, Gardnerella, serratia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA, TEM-213, OXA-50, OXA-114a, KPC-1, AAC(3)-IId\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP246, P247, P250\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003estaphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eErmC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP253, P254\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Streptococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etet(A), tetM, tetO, APH(3')-la, APH(3')-llb, SHVbeta-lactamase, AAC(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP255\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Streptococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etet(A), tetM, tetO, APH(3')-la, APH(3')-llb, SHVbeta-lactamase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP256, P257\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, staphylococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etetM, APH(3')-llb, pseudomonas aeruginosa catB7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP258, P259, P262, P262, P264\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, rmtB, TEM-214, APH(6)-Id, pseudomonas aeruginosa catB7, TEMbeta-lactamase, KPCbeta-lactamase, ANT(3'')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP26, P27, P73, P74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOXA-50, APH(3'')-Ib, APH(3')-IIb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP28, P46, P47, P78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Candida, enterococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosA, OXA-50, APH(3')-Iib, pseudomonas aeruginosa catB7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP280\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Acinetobacter, Stenotrophomonas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, APH(3')-la, OXAbeta-lactamase, ANT(3'')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP283, P284\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Candida, Nakaseomyces, Stenotrophomonas, enterococcus, Escherichia, Clavispora\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evanA, FosA, tet(A), tetM, ANT(2'')-Ia, ErmB, sul1, sul2, TEM-206, OXA-50, AAC(3)-IIc, AAC(6')-Ii, AAC(6')-Ie-APH(2'')-Ia, APH(3'')-Ib, APH(3')-IIb, APH(3')-IIIa, APH(6)-Id, pseudomonas aeruginosa catB7, OXAbeta-lactamase, CTX-Mbeta-lactamase, AAC(3), AAC(6'), ANT(3''), major facilitator superfamily(MFS) antibiotic efflux pump\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP296\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Achromobacter, Stenotrophomonas, Elizabethkingia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA, KPC-5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP297\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Achromobacter, Stenotrophomonas, Elizabethkingia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA, KPC-5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP298\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Stenotrophomonas, Elizabethkingia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, KPC-5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP299\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Achromobacter, Stenotrophomonas, Mycotoruloides, proteus, bordelella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA2, TEM-213, SHV-5, QnrS8, OXA-10, NDM-1, KPC-5, CTX-M-90, AAC(6\u0026rsquo;)-Ib9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP300\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonas, Klebsiella, Achromobacter, Stenotrophomonas, Mycotoruloidesproteus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esul1, aadA2, TEM-213, QnrS8, OXA-10, NDM-1, KPC-5, AAC(6\u0026rsquo;)-Ib9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeveral resistance genes were frequently identified, including tetM, ErmB, sul1, and various beta-lactamases such as OXA and TEM. These genes were associated with resistance to tetracyclines, macrolides, sulfonamides, and beta-lactam antibiotics, respectively. The detailed distribution of resistance genes among the patients showed that multiple pathogens harbored these genes, contributing to multidrug-resistant infections. For example, Pseudomonas, Klebsiella, and Acinetobacter were commonly found with resistance genes such as tetM, sul1, and OXA beta-lactamase. Staphylococcus species frequently carried mecA, encoding methicillin resistance. Candida and other fungal pathogens also exhibited resistance, complicating antifungal therapy \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eSome patients had complex resistance profiles involving multiple genes. For instance, Patient P8 harbored tetM, ErmB, sul1, armA, AAC(3)-Ia, APH(3\u0026rsquo;)-Ia, catB8, OXA beta-lactamase, and others; Patient P9 had APH(3')-llb, APH(3')-la, APH(6)-Id, and Pseudomonas aeruginosa catB7. Many patients exhibited mixed infections involving multiple resistant pathogens, further complicating treatment strategies \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs our study shows, mNGS has the potential to change the diagnostic approach to HAP, generating an unbiased detection of pathogens that would not be covered by traditional culture methods. This is in line with the increasing literature consensus that mNGS will radically change the landscape of infectious disease diagnosis by providing an unbiased picture of pathogens [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of our study, it became clear that i) mNGS had a much greater sensitivity and can detect a much larger array of pathogens, including fastidious (difficult-to-culture) bacteria, fungi and viruses, compared with traditional culture methods [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. ii) Conventional culture-based diagnostics miss many fastidious organisms which pose challenges to culture and often necessitate special growth conditions. Wilson et al. (2019) found that routine microbiologic testing is often insufficient to detect all potential neuroinvasive pathogens in CSF. Herein, they utilised mNGS of CSF collected from patients with meningitis or encephalitis to diagnose more neurologic infections and provided actionable information for a subset of patient [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This aligns with your study\u0026rsquo;s findings on the superiority of mNGS over traditional culture methods in detecting a wider array of pathogens, including those in culture-negative samples. This advantage of mNGS offering enhanced sensitivity is crucial for accurate and timely diagnosis in critically ill patients.\u003c/p\u003e \u003cp\u003eThe clinical implications of mNGS are significant. In the responders, 25.6% cases had a change in treatment regimen based on the mNGS results. Likewise, in the non-responders, 28% treatment regimens were changed. The significance of mNGS in facilitating targeted antimicrobial therapy is highlighted in this study of hospital-acquired infections. Gu and colleagues\u0026rsquo; study also stresses the value of blood mNGS in infectious patients with mild and non-specific symptoms of infection. Indeed, they showed that blood mNGS can be used as a supplement to traditional laboratory examination and should be performed as early as possible to guide clinicians to perform appropriate antimicrobial intervention in a timely and effective manner. In their study, therapeutic regimens were changed for 70.3% cases (149/212) based on mNGS results [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe detection of polymicrobial infections and antibiotic resistance genes through metagenomic mNGS represents a critical advancement in the diagnosis and management of HAP. Traditional culture-based diagnostic methods often miss polymicrobial infections due to their inability to grow multiple pathogens simultaneously under standard laboratory conditions. In this study, mNGS identified polymicrobial infections in 28% of cases, a detection rate significantly higher than that observed with culture-based methods. Polymicrobial infections, particularly in the hospital setting, are clinically important as they are often associated with more severe disease progression and higher rates of treatment failure. This finding underscores the importance of mNGS as a diagnostic tool capable of providing a more comprehensive pathogen profile, allowing for more targeted and effective treatment strategies. Equally important is the detection of antibiotic resistance genes in 30% of samples. The ability of mNGS to simultaneously identify pathogens and their associated resistance genes is a significant advantage over traditional methods, which require separate testing for antimicrobial susceptibility. This real-time detection of resistance markers enables clinicians to adjust antimicrobial therapy more precisely, reducing the risk of prolonged ineffective treatment and the spread of resistant organisms. These findings highlight the clinical value of mNGS in guiding more precise and effective antimicrobial treatments in hospital-acquired infections.\u003c/p\u003e \u003cp\u003eIdentification of antibiotic resistance genes was the key finding of our study. Resistance genes tetM, ErmB and a number of beta-lactamases were picked up, enabling comprehensive data for antibiotic stewardship, in line with another mNGS study conducted by Gan et al. who reported the ability of mNGS to identify resistance genes targeting different antibiotics in the treatment of severe pneumonia in paediatric patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur work confirms that mNGS has an important impact on patient clinical outcome by identifying the pathologies, where conventional methods are frequently negative, leading to an increase in the detection of severe infection-causing, and multi-drug resistant (hard-to-treat) bacteria, such as Pseudomonas, Klebsiella and Acinetobacter, as well as fungal pathogens such as Candida and Aspergillus. These results are confirmed by the major studies in the field, highlighting the key role of broad pathogen detection to decide targeted antibiotic and antifungal therapies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe identification of viral pathogens, including HSV1, CMV, and EBV, further emphasizes the diagnostic utility of mNGS, particularly in immunocompromised patients. While these viruses are commonly found in the population, their detection in a hospital setting is clinically significant as they can cause opportunistic infections in immunosuppressed individuals. In this study, the presence of these viral pathogens was confirmed in patients with underlying conditions that could predispose them to viral reactivation. Their detection provided important information for clinicians to tailor antiviral therapies, particularly in cases where bacterial or fungal pathogens alone could not explain the severity of the illness. prompt and accurate identification of these viruses may lead to better patient outcomes, reduced hospital stays, and lower healthcare costs​ [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe cost-effectiveness of mNGS is still a matter of debate. The capital costs are high, but the expense of hospital stays, targeted treatments and better outcomes might quickly allow hospitals to recoup the investment. Jing et al (2021) forecast that costs will continue to decrease as technology improves and becomes more widely adopted [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Meanwhile, portable sequencing devices and point-of-care mNGS will transform the diagnosis of infectious diseases by allowing rapid detection of pathogens at the patient bedside [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAlthough currently limited to specialized centers with the required infrastructure and bioinformatics expertise, the adoption of mNGS is expected to grow over the next 5 to 10 years as sequencing technology becomes more cost-effective and accessible. In addition, ongoing improvements in automated data analysis tools will reduce the need for highly specialized bioinformatics staff, further accelerating the integration of mNGS into routine clinical microbiology. Public health efforts to combat antimicrobial resistance are likely to drive this shift, as mNGS provides rapid, comprehensive data on pathogen detection and resistance profiles. Thus, mNGS is anticipated to become a staple in diagnostic laboratories, particularly in hospitals dealing with high-risk infections, as early as the next decade​.\u003c/p\u003e \u003cp\u003eWhile these results are promising, several key limitations of our study need to be recognized. The retrospective design and our single-center setting are likely to generate selection bias and, therefore, restrict the generalizability of our outcomes. Second, the small sample size, particularly that of the non-responder group, might limit statistical power and should be appropriately addressed in the analysis and interpretation of our data. Larger, multicenter cohort studies are warranted in the future to confirm our results and assess the cost-effectiveness of mNGS in clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study demonstrated the remarkable benefits of mNGS over traditional culture in the diagnosis of HAP. The combined ability to provide entire pathogen detection and to profile antibiotic sensitivity makes mNGS a new pillar of modern infectious disease control. Certain technical and economic problems still hinder the practical application of mNGS in a clinical setting. But as the platform matures and its price becomes affordable, mNGS will soon be an integral part of clinical microbiology, to change the paradigm in diagnosis and treatment of infectious diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003emNGS\u003c/strong\u003e (Metagenomic Next-Generation Sequencing), \u003cstrong\u003eHAP\u003c/strong\u003e (Hospital-Acquired Pneumonia), \u003cstrong\u003eCAP\u003c/strong\u003e (Community-Acquired Pneumonia), \u003cstrong\u003eCSF\u003c/strong\u003e (Cerebrospinal Fluid), \u003cstrong\u003eBALF\u003c/strong\u003e (Bronchoalveolar Lavage Fluid), \u003cstrong\u003ePSI\u003c/strong\u003e (Pneumonia Severity Index), \u003cstrong\u003eCPIS\u003c/strong\u003e (Clinical Pulmonary Infection Score), \u003cstrong\u003eFRAIL\u003c/strong\u003e (Frailty Scale), \u003cstrong\u003eAPACHE II\u003c/strong\u003e (Acute Physiology and Chronic Health Evaluation II), \u003cstrong\u003eHbA1c\u003c/strong\u003e (Hemoglobin A1c), \u003cstrong\u003eALB\u003c/strong\u003e (Albumin), \u003cstrong\u003ePA\u003c/strong\u003e (Prealbumin), \u003cstrong\u003eWBC\u003c/strong\u003e (White Blood Cell Count), \u003cstrong\u003eCRP\u003c/strong\u003e (C-Reactive Protein), \u003cstrong\u003eIL-6\u003c/strong\u003e (Interleukin-6), \u003cstrong\u003eCr\u003c/strong\u003e (Creatinine), \u003cstrong\u003eMDR\u003c/strong\u003e (Multidrug-Resistant), \u003cstrong\u003eHSV1\u003c/strong\u003e (Herpes Simplex Virus 1), \u003cstrong\u003eCMV\u003c/strong\u003e (Cytomegalovirus), and \u003cstrong\u003eEBV\u003c/strong\u003e (Epstein-Barr Virus).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the institutional review board at the Beijing Rehabilitation Hospital, affiliated to Capital Medical University, Department of Respiratory and Critical Care Medicine, China.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQL, JG, CZ, TZ, HG, and BY have participated in the data collection and contributed to writing the manuscript. BZ, JW and HJ have analyzed the data, performed the interpretation and revised the manuscript critically. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has received specialized funding for scientific research from Beijing Rehabilitation Hospital of Capital Medical University (2022-007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch1\u003eAcknowledgments\u0026nbsp;\u003c/h1\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHe S, Xiong Y, Tu T, Feng J, Fu Y, Hu X, et al. Diagnostic performance of metagenomic next-generation sequencing for the detection of pathogens in cerebrospinal fluid in pediatric patients with central nervous system infection: a systematic review and meta-analysis. BMC Infectious Diseases. 2024;24(1):103.\u003c/li\u003e\n\u003cli\u003eChen H, Huang Q, Wu W, Wang Z, Wang W, Liu Y, et al. Assessment and clinical utility of metagenomic next-generation sequencing for suspected lower respiratory tract infections. European Journal of Medical Research. 2024;29(1):213.\u003c/li\u003e\n\u003cli\u003eMiao Q, Ma Y, Wang Q, Pan J, Zhang Y, Jin W, et al. Microbiological Diagnostic Performance of Metagenomic Next-generation Sequencing When Applied to Clinical Practice. Clinical Infectious Diseases. 2018;67(suppl_2):S231-S40.\u003c/li\u003e\n\u003cli\u003eWang C, Yan D, Huang J, Yang N, Shi J, Pan S, et al. The clinical application of metagenomic next-generation sequencing in infectious diseases at a tertiary hospital in China. Frontiers in Cellular and Infection Microbiology. 2022;12.\u003c/li\u003e\n\u003cli\u003eEdward P, Handel AS. Metagenomic Next-Generation Sequencing for Infectious Disease Diagnosis: A Review of the Literature With a Focus on Pediatrics. Journal of the Pediatric Infectious Diseases Society. 2021;10(Supplement_4):S71-S7.\u003c/li\u003e\n\u003cli\u003eGu W, Deng X, Lee M, Sucu YD, Arevalo S, Stryke D, et al. Rapid pathogen detection by metagenomic next-generation sequencing of infected body fluids. Nature medicine. 2021;27(1):115-24.\u003c/li\u003e\n\u003cli\u003eYu C, Guo W, Zhang Z, Ma Y, Cao X, Sun N, et al. The Impact of mNGS Technology in the Etiological Diagnosis of Severe Pneumonia in Children During the Epidemic of COVID-19. Infection and Drug Resistance. 2023;16(null):2395-402.\u003c/li\u003e\n\u003cli\u003eHan D, Yu F, Zhang D, Yang Q, Shen R, Zheng S, et al. Applicability of Bronchoalveolar Lavage Fluid and Plasma Metagenomic Next-Generation Sequencing Assays in the Diagnosis of Pneumonia. Open Forum Infectious Diseases. 2024;11(1):ofad631.\u003c/li\u003e\n\u003cli\u003eBlauwkamp TA, Thair S, Rosen MJ, Blair L, Lindner MS, Vilfan ID, et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nature Microbiology. 2019;4(4):663-74.\u003c/li\u003e\n\u003cli\u003eMicek ST, Kollef KE, Reichley RM, Roubinian N, Kollef MH. Health care-associated pneumonia and community-acquired pneumonia: a single-center experience. Antimicrob Agents Chemother. 2007;51(10):3568-73.\u003c/li\u003e\n\u003cli\u003eNiederman MS. Hospital-Acquired Pneumonia, Health Care-Associated Pneumonia, Ventilator-Associated Pneumonia, and Ventilator-Associated Tracheobronchitis: Definitions and Challenges in Trial Design. Clinical Infectious Diseases. 2010;51(Supplement_1):S12-S7.\u003c/li\u003e\n\u003cli\u003eHeitz M, Levrat A, Lazarevic V, Barraud O, Bland S, Santiago-Allexant E, et al. Metagenomics for the microbiological diagnosis of hospital-acquired pneumonia and ventilator-associated pneumonia (HAP/VAP) in intensive care unit (ICU): a proof-of-concept study. Respiratory Research. 2023;24(1):285.\u003c/li\u003e\n\u003cli\u003eSun T, Wu X, Cai Y, Zhai T, Huang L, Zhang Y, et al. Metagenomic Next-Generation Sequencing for Pathogenic Diagnosis and Antibiotic Management of Severe Community-Acquired Pneumonia in Immunocompromised Adults. Frontiers in Cellular and Infection Microbiology. 2021;11.\u003c/li\u003e\n\u003cli\u003eWilson MR, Sample HA, Zorn KC, Arevalo S, Yu G, Neuhaus J, et al. Clinical Metagenomic Sequencing for Diagnosis of Meningitis and Encephalitis. N Engl J Med. 2019;380(24):2327-40.\u003c/li\u003e\n\u003cli\u003eZhang H, Liang R, Zhu Y, Hu L, Xia H, Li J, et al. Metagenomic next-generation sequencing of plasma cell-free DNA improves the early diagnosis of suspected infections. BMC Infectious Diseases. 2024;24(1):187.\u003c/li\u003e\n\u003cli\u003eGan M, Zhang Y, Yan G, Wang Y, Lu G, Wu B, et al. Antimicrobial resistance prediction by clinical metagenomics in pediatric severe pneumonia patients. Annals of Clinical Microbiology and Antimicrobials. 2024;23(1):33.\u003c/li\u003e\n\u003cli\u003eChen S, Hou C, Kang Y, Li D, Rong J, Li Z. Application of metagenomic next-generation sequencing in the diagnosis and resistome analysis of community-acquired pneumonia pathogens from bronchoalveolar lavage samples. Journal of Applied Microbiology. 2023;134(6):lxad102.\u003c/li\u003e\n\u003cli\u003ePang F, Xu W, Zhao H, Chen S, Tian Y, Fu J, et al. Comprehensive evaluation of plasma microbial cell-free DNA sequencing for predicting bloodstream and local infections in clinical practice: a multicenter retrospective study. Frontiers in Cellular and Infection Microbiology. 2024;13.\u003c/li\u003e\n\u003cli\u003eGu W, Miller S, Chiu CY. Clinical Metagenomic Next-Generation Sequencing for Pathogen Detection. Annu Rev Pathol. 2019;14:319-38.\u003c/li\u003e\n\u003cli\u003eJing C, Chen H, Liang Y, Zhong Y, Wang Q, Li L, et al. Clinical Evaluation of an Improved Metagenomic Next-Generation Sequencing Test for the Diagnosis of Bloodstream Infections. Clinical Chemistry. 2021;67(8):1133-43.\u003c/li\u003e\n\u003cli\u003eBloemen B, Gand M, Vanneste K, Marchal K, Roosens NHC, De Keersmaecker SCJ. Development of a portable on-site applicable metagenomic data generation workflow for enhanced pathogen and antimicrobial resistance surveillance. Scientific Reports. 2023;13(1):19656.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hospital-acquired pneumonia, Metagenomic Next-Generation Sequencing (mNGS), Hospital-Acquired Pneumonia (HAP), Pathogen Detection, Antimicrobial Resistance","lastPublishedDoi":"10.21203/rs.3.rs-5235477/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5235477/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eHospital-acquired pneumonia (HAP) presents significant diagnostic challenges, exacerbated by the limitations of traditional culture-based methods. This study evaluates the clinical efficacy and diagnostic value of metagenomic next-generation sequencing (mNGS) in the detection of pathogens in HAP patients, providing new insights into infection prevention and control in healthcare settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We conducted a retrospective analysis of clinical and laboratory data from 300 adult HAP patients at Beijing Rehabilitation Hospital, China. Bronchoalveolar lavage fluid samples were collected for DNA extraction, library construction, and sequencing using the Illumina platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The results revealed that mNGS identified pathogens in 92% of the samples, compared to 72% by traditional cultures. Specifically, mNGS detected a broader range of bacteria, viruses, and fungi, including Pseudomonas, Klebsiella, and Aspergillus, which were often missed by traditional methods. mNGS identified polymicrobial infections in 28% of the cases and antibiotic resistance genes in 30% of the samples where traditional methods failed. These findings led to changes in treatment for 26% of the patients based solely on mNGS data, with specific treatment adjustments driven by the detection of rare or resistant pathogens in 18% of these cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Our findings advocate for the integration of mNGS in routine clinical practice to enhance diagnostic accuracy and enable more informed decision-making in the management of HAP. Despite its higher cost and technical requirements, mNGS holds promise for more accurate and timely diagnostics in complex infection cases.\u003c/p\u003e","manuscriptTitle":"Clinical Efficacy and Diagnostic Value of Metagenomic Next-Generation Sequencing (mNGS) in Hospital-Acquired Pneumonia: A Stratified Retrospective Study of Responders and Non- Responders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-01 16:22:07","doi":"10.21203/rs.3.rs-5235477/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0236f429-daa2-428b-9fbd-c05681dd92a8","owner":[],"postedDate":"December 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-01T16:22:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-01 16:22:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5235477","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5235477","identity":"rs-5235477","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}