Appropriateness of Antibiotic Therapy and its Association with Clinical Outcomes among Critically Ill Patients: A Retrospective Study from the United Arab Emirates

Appropriateness of Antibiotic Therapy and its Association with Clinical Outcomes among Critically Ill Patients: A Retrospective Study from the United Arab Emirates | 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 Appropriateness of Antibiotic Therapy and its Association with Clinical Outcomes among Critically Ill Patients: A Retrospective Study from the United Arab Emirates Inas Kassis, Mosab Al Balas, Sheilla Hilal Hamed Rashid, Shabaz Mohiuddin Gulam, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8481307/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 Inappropriate antibiotic prescribing is a major driver of antimicrobial resistance and preventable harm, with patients in intensive care units being particularly vulnerable due to frequent and complex antibiotic exposure. Despite expanding antimicrobial stewardship efforts, contemporary evidence evaluating prescribing quality and its clinical implications in intensive care settings within the United Arab Emirates remains limited. Methods A retrospective cohort study was conducted in the intensive care unit of a tertiary care hospital. Adult patients admitted between January 2023 and December 2024, with a length of stay exceeding 24 hours but less than 30 days, who received at least one systemic antibiotic and had complete medical records, were included. Data were extracted from electronic medical records and a regional health information exchange platform. Antibiotic appropriateness was evaluated against institutional or Infectious Diseases Society of America guidelines, assessing indication, empiric choice, agent selection, dose, route and frequency, duration, microbiologic concordance, and therapeutic drug monitoring. Results Among 8,697 antibiotic orders retrieved, 1,859 unique records were identified, of which 500 patient records met the eligibility criteria and were analyzed. Overall, 36% of patients received inappropriate antibiotic therapy. The most common reason for inappropriateness was the absence of a clear indication, particularly related to postoperative prophylaxis (44.4%). At the agent level, cefuroxime (76.5%), cefepime (48.2%), and clindamycin (37.1%) exhibited the highest rates of inappropriate use. Multivariable logistic regression identified positive culture results (AOR 1.25, p < 0.001) and sepsis (AOR 1.18, p = 0.0025) as predictors of appropriate antibiotic use, whereas the presence of a central line was inversely associated (AOR 0.79, p = 0.004). Conclusion The overall rate of inappropriate antibiotic prescribing was relatively low, reflecting strengths in culture-guided therapy and infection-focused management. Nevertheless, persistent gaps were identified in postoperative prophylaxis and surgical prescribing practices. These findings highlight the need for targeted antimicrobial stewardship interventions within surgical specialties to further optimize antibiotic use in critically ill patients. Anti-Bacterial Agents Antimicrobial Stewardship Guideline Adherence Intensive Care Units Treatment Outcome Figures Figure 1 1. Background Antimicrobial resistance (AMR) has emerged as a major global public health threat, with an increasing number of deaths directly or indirectly attributable to resistant infections. According to the Centers for Disease Control and Prevention, approximately 1.27 million deaths in 2019 were directly caused by AMR, while an estimated 4.95 million deaths were associated with AMR worldwide ( 1 , 2 ). Patterns of antimicrobial use within healthcare settings play a central role in the development and spread of resistance, with inappropriate antibiotic prescribing being a key contributor to increased morbidity, mortality, and the emergence of resistant pathogens globally ( 3 – 5 ). Patients admitted to intensive care units (ICU) are estimated to be five to ten times more susceptible to infections than those in general wards or non-hospital settings, largely due to the complexity and critical nature of their underlying conditions ( 6 ). Antibiotic prescribing in ICUs is influenced by multiple factors, including physician’s prescribing behaviors ( 3 , 7 ), prolonged durations of therapy ( 8 – 10 ), the absence of definitive infection markers ( 11 ), and patient-related characteristics such as comorbidities, prior hospital admissions, invasive surgical procedures, microbiological culture results, and previous antibiotic exposure ( 12 – 14 ). These challenges often constrain intensivists to a narrow time window for clinical assessment and decision-making, increasing the risk of inappropriate antibiotic initiation, continuation, or escalation ( 15 – 17 ). Evidence from previous studies assessing antibiotic use in ICUs have reported substantial variability in the prevalence of inappropriate prescribing. Studies from low- and middle-income countries have documented higher rates of irrational antibiotic use in the ICUs, ranging from 43% to 77.2% ( 8 , 18 , 19 ), whereas relatively lower rates, between 29.8% and 44%, have been reported in upper-middle- and high-income countries ( 10 , 20 ). Importantly, the implementation of antimicrobial stewardship programs (ASPs) within healthcare facilities has been consistently associated with improved physician adherence to evidence-based infection management guidelines and more judicious antibiotic prescribing ( 10 , 20 ). In the United Arab Emirates (UAE), several national initiatives have been undertaken to optimize antibiotic use and mitigate AMR, including the adoption of ASP frameworks across healthcare institutions ( 21 ), enforcement of regulatory restrictions on the dispensing of broad-spectrum antibiotics in community pharmacies ( 22 ), and the establishment of the UAE National AMR Surveillance System to monitor resistance trends nationwide ( 23 – 25 ). Despite these efforts, existing studies have largely focused on hospital-wide antibiotic utilization, with ICU data often aggregated with other patient populations and without detailed evaluation of prescribing appropriateness or associated clinical outcomes ( 26 – 29 ). Consequently, the present study was conducted to assess the appropriateness of antibiotic prescribing among ICU patients and to examine its association with clinical outcomes. 2. Methods 2.1. Study Design and Setting This retrospective cohort study was conducted at a large tertiary care hospital operating under the Abu Dhabi Health Services Company and PureHealth management systems in the UAE. The ICU comprises 20 beds, including two isolation rooms and six rooms equipped for renal replacement therapy (RRT). 2.2. Antimicrobial Stewardship The hospital has an established ASP committee responsible for reviewing international guidelines and adapting them into institution-specific protocols for antibiotic use. Clinical pharmacists play an active role in the development, periodic review, and updating of these guidelines and are involved in key ASP activities, including formulary restriction, prospective audit and feedback, and participation in daily ward rounds. 2.3. Study Population Clinical records of patients admitted to the ICU between January 2023 and December 2024 were retrieved and screened according to predefined inclusion and exclusion criteria. Patients aged 18 years and above who were admitted to the ICU for more than 24 hours and received at least one systemic antibiotic during their ICU stay were included. Patients were excluded if their ICU stay was shorter than 24 hours or exceeded 30 days, or if essential clinical data were missing from their records. 2.4. Study Tool and Variables A standardized data collection form was developed to ensure consistent and comprehensive extraction of relevant information from patient medical records. The tool captured variables aligned with the study objectives, including patient demographics, clinical characteristics, laboratory and microbiological indices, antibiotic therapy details, antimicrobial stewardship indicators, and clinical outcomes. Sociodemographic variables included age, gender, body mass index (BMI), and ethnicity. Clinical variables encompassed the reason for ICU admission, comorbidities, and lifestyle factors. ICU-related and outcome variables included the presence of central lines, mechanical ventilation, intubation, vasopressor use, systemic corticosteroid therapy, nutritional support, sedatives, RRT, development of organ dysfunction, length of ICU stay, and discharge disposition. Laboratory and microbiological variables included body temperature, hematologic parameters, inflammatory markers, renal function indices, blood glucose, mean arterial pressure (MAP), culture and susceptibility results, infection type, and identified organisms. Antibiotic therapy-related variables comprised prescribed agents, indication, route of administration, dosage, dosing frequency, duration of therapy, therapeutic drug monitoring (where applicable), and any modifications such as escalation, de-escalation, optimization, or discontinuation. In addition, supplementary variables were collected on patients’ clinical history within 90 days prior to ICU admission, including previous hospital admissions, invasive procedures or surgeries, microbiological culture results, and prior antibiotic exposure. 2.5. Data Collection Following development of the standardized data collection tool, all ICU antibiotic orders during the study period were retrieved from the hospital’s electronic medical record (EMR) system. To ensure one unique record per patient encounter, repeated antibiotic orders within the same admission were consolidated. Readmissions occurring within 24 hours of discharge were considered a continuation of the initial admission and were merged into a single episode. The original dataset was retained to allow review of all antibiotic orders associated with each patient. Screening was conducted in two stages: an initial automated search based on admission criteria and antibiotic administration records, followed by manual verification by a trained investigator to confirm eligibility and data completeness. To ensure consistent representation across the study period, eligible records were stratified by year and month of admission. A quota of 30 patients per month was selected. In months with fewer eligible admissions, all available patients were included. Data were extracted using the standardized form from structured EMR modules, including admission and discharge records, laboratory and microbiology systems, and the medication administration records. When clarification was required, unstructured documents such as clinical progress notes were reviewed. Supplementary data were retrieved from Malaffi, the regional health information exchange platform, to capture patients’ clinical history within 90 days prior to ICU admission. Data collection was performed by a trained investigator following standardized operating procedures. To ensure data quality and integrity, a random subset of the collected records was independently reviewed by a second investigator. Routine validation checks were applied to assess data completeness, consistency, and temporal accuracy. An audit trail was systematically maintained throughout the data collection process to document all revisions and quality control measures. 2.6. Appropriateness of Antibiotic Therapy The appropriateness of antibiotic therapy was evaluated based on adherence to the hospital’s institutional antibiotic use guidelines. In instances where institutional guidelines were unavailable, recommendations from the Infectious Diseases Society of America (IDSA) were used as the reference standard. When uncertainty arose, the appropriateness of the antibiotic regimen was re-evaluated by a second investigator, and consensus was reached through deliberation. Definition of appropriateness were informed by criteria from the World Health Organization and antimicrobial stewardship frameworks and were further refined through a review of published studies employing comparable definitions within their research. Core elements of their definitions regarding appropriateness included indication, antibiotic selection, dosage, duration of therapy, and adherence to clinical guidelines (8,9,19,30,31). Inappropriate antibiotic use was defined as non-compliance with one or more key parameters, including appropriateness of indication, empiric therapy selection, antibiotic choice, dosage and necessary dose adjustment, route and frequency of administration, duration of therapy, consideration of microbiological susceptibility results, and monitoring of therapeutic drug levels when indicated. 2.7. Ethical Approval The study protocol was reviewed and approved by the Institutional Ethics Committee of Gulf Medical University, Ajman, UAE (Reference Number: IRB-COP-STD-44-Oct-2024) and Tawam Hospital, Al Ain, UAE (Reference Number: MF2058-2025-1247). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (Clinical trial number: Not applicable). Patient confidentiality was strictly maintained throughout the study. No personal identifiers were recorded, and all data were stored in encrypted, password-protected electronic files accessible only to authorized members of the research team. 2.8. Statistical Analysis All statistical analysis were performed using Microsoft Excel (Professional Plus 2016) and Statistical Package for Social Sciences (IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY, USA). Continuous variables were summarized as medians and interquartile ranges (IQR), while categorical variables were expressed as frequencies and percentages. Associations between adherence and categorical variables were analyzed using the Chi-square test or Fischer's exact test, as appropriate. Non-parametric continuous variables were analyzed using the Mann-Whitney U test. Factors independently associated with adherence were identified using logistic regression analysis. Univariable logistic regression analysis was initially conducted and all variables with a p -value ≤0.25 in univariable analysis were considered for inclusion in the multivariable logistic regression model. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were calculated to determine the strength of association. A p -value < 0.05 was considered statistically significant. 3. Results The initial dataset comprised 8,697 antibiotic prescription orders retrieved from the hospital database, including multiple entries per patient encounter. After consolidation of repeated orders within the same admission, 1,859 unique patient records were identified. During eligibility screening, 598 records were assessed; 98 were excluded due to age criteria (n = 55), ICU length of stay (LoS) outside the predefined range (n = 30), or antibiotic exposure outside the eligible duration (n = 13). A total of 500 patients met the inclusion criteria and were included in the final analysis. 3.1. Baseline Characteristics of ICU Patients Baseline demographic, clinical, laboratory, and microbiological characteristics of the study cohort stratified by the appropriateness of antibiotic therapy are presented in Table 1. Patients who received appropriate antibiotic therapy demonstrated indicators of greater baseline illness severity and poorer prognostic profiles at ICU admission compared with those who received inappropriate therapy. Significant differences were observed in age, reason for ICU admission, burden of comorbidities, recent health exposure, organ dysfunction, and microbiological findings. Patients in the appropriate therapy group were more likely to present with sepsis or septic shock, have prior positive cultures, and exhibit multidrug-resistant organism (MDRO) isolates. Table 1. Baseline characteristics of ICU patients stratified by appropriateness of antibiotic therapy Variable Total (n = 500) Appropriate therapy (n = 320) Inappropriate therapy (n = 180) p -value § Demographic characteristics Age, years, median ( IQR ) 47 (35–64) 52 (36–66) 42 (33–57) <0.001 BMI, kg/m 2 , median ( IQR ) 26.3 (22.5–31.1) 26.3 (22.5–31.1) 27.1 (23.0–31.7) 0.180 LoS, days, median ( IQR ) 5.5 (4–9) 6 (4–9) 5(4–8) 0.285 Gender: Female, n (%) 239 (47.8) 144 (45.0) 95 (52.8) 0.095 Male, n (%) 261 (52.2) 176 (55.0) 85 (47.2) Ethnicity: UAE nationals, n (%) 136 (27.2) 96 (30.0) 40 (22.2) 0.057 Other Arab a , n (%) 99 (19.8) 64 (20.0) 35 (19.4) Asian b , n (%) 178 (35.6) 109 (34.1) 69 (38.3) African c , n (%) 81 (16.2) 50 (15.6) 31 (17.2) Others, n (%) 6 (1.2) 1 (0.3) 5 (2.8) Reason for ICU admission Sepsis, n (%) 207 (41.4) 160 (50.0) 47 (26.1) <0.001 Septic shock, n (%) 99 (19.8) 74 (23.1) 25 (13.9) 0.013 Postoperative, n (%) 145 (29.0) 67 (20.9) 78 (43.3) <0.001 Trauma, n (%) 32 (6.4) 15 (4.7) 17 (9.4) 0.037 DKA, n (%) 15 (3.0) 11 (3.4) 4 (2.2) 0.445 Comorbidities Renal impairment d , n (%) 104 (20.8) 83 (25.9) 21 (11.7) <0.001 Cardiovascular disorders, n (%) 213 (42.6) 155 (48.4) 58 (32.2) <0.001 Neurological/neurodegenerative disorders, n (%) 32 (6.4) 21 (6.6) 11 (6.1) 0.843 Gastrointestinal disorders, n (%) 37 (7.4) 27 (8.4) 10 (5.6) 0.237 Endocrine disorders, n (%) 194 (38.8) 143 (44.7) 51 (28.3) <0.001 Respiratory disorders, n (%) 46 (9.2) 28 (8.8) 18 (10.0) 0.643 Musculoskeletal disorders, n (%) 19 (3.8) 13 (4.1) 6 (3.3) 0.682 Vascular disorders, n (%) 44 (8.8) 31 (9.7) 13 (7.2) 0.350 Hematological disorders, n (%) 36 (7.2) 26 (8.1) 10 (5.6) 0.286 Urinary disorders, n (%) 17 (3.4) 14 (4.4) 3 (1.7) 0.128 Cancer-related conditions, n (%) 112 (22.4) 69 (21.6) 43 (23.9) 0.549 Autoimmune diseases, n (%) 18 (3.6) 13 (4.1) 5 (2.8) 0.459 Recurrent infections, n (%) 21 (4.2) 16 (5.0) 5 (2.8) 0.234 Post COVID-19 syndrome, n (%) 2 (0.4) 2 (0.6) 0 (0) 0.538 Recent risk factors (90 days before admission) Prior hospital admission, n (%) 259 (51.8) 180 (56.3) 79 (43.9) 0.008 Prior invasive procedure, n (%) 221 (44.2) 126 (39.4) 95 (52.8) 0.005 Prior antibiotic use, n (%) 352 (70.4) 224 (70.0) 128 (71.1) 0.990 Prior positive culture, n (%) 98 (19.6) 82 (25.6) 16 (8.9) <0.001 ICU parameters Mechanical ventilation, n (%) 248 (49.6) 162 (50.6) 86 (47.8) 0.541 Use of vasopressors e , n (%) 157 (31.4) 102 (31.9) 55 (30.6) 0.760 Use of systemic corticosteroid therapy f , n (%) 250 (50.0) 168 (52.5) 82 (45.6) 0.136 Renal replacement therapy HD, n (%) 27 (5.4) 21 (6.6) 6 (3.3) 0.125 CRRT, n (%) 70(14.0) 52 (16.3) 18 (10.0) 0.053 Received enteral/parenteral nutrition, n (%) 294 (58.8) 201 (62.8) 93 (51.7) 0.015 Use of sedatives, n (%) 214 (42.8) 133 (41.6) 81 (45.0) 0.456 Organ dysfunction, n (%) 155 (31.0) 119 (37.2) 36 (20.0) <0.001 Microbiology parameters Testing performed, n (%) 495 (99.0) 319 (99.7) 176 (97.8) 0.059 MDRO isolates, n (%) 78 (15.6) 64 (20.0) 14 (7.8) <0.001 Non-MDRO isolates, n (%) 135 (27.0) 107 (33.4) 28 (15.6) <0.001 Notes: a Other Arabs included the following countries: Syria, Jordan, Palestine, Oman, Iraq, Bahrain, KSA, Lebanon, Yemen, Qatar. b Asia included the following countries: Philippines, India, Bangladesh, Pakistan, Nepal, Sri Lanka, Afghanistan, Indonesia, Iran. c Africa included the following countries: Djibouti, Rwanda, Mauritania, Uganda, Egypt, Ethiopia, Somalia, Morocco, Tunisia, Nigeria, Sahrawi, South Africa, Comoros. d Renal Impairment included all patients with AKI, any stages of chronic kidney disease (CKD), End Stage Renal Disease (ESRD), or Hemodialysis (HD). e Vasopressors refers to the following used: norepinephrine, epinephrine, dopamine, dobutamine, vasopressin and phenylephrine. f Steroids refers to the following used: dexamethasone, hydrocortisone, fludrocortisone, methylprednisolone and prednisolone all in intravenous form. Abbreviations: BMI: Body Mass Index, CRRT: Continuous Renal Replacement Therapy, COVID-19: Coronavirus disease 2019, DKA: Diabetic Ketoacidosis, HD: Hemodialysis, ICU: Intensive Care Unit, LoS: Length of Stay, MDRO: Multidrug-Resistant Organism, UAE: United Arab Emirates. Tests: Continuous data were assessed using Mann-Whitney U test, while categorical data were assessed using chi-square test or fisher-exact test. § A p -value < 0.05 level was considered to be statistically significant. 3.2. Appropriateness of Antibiotic Therapy Among the 500 ICU patients included, 320 (64%) received antibiotic therapy deemed appropriate based on clinical guideline adherence and microbiological evidence, whereas 180 (36%) patients received inappropriate antibiotic therapy. 3.2.1. Reasons for Inappropriate Antibiotic Use Patterns of inappropriate antibiotic prescribing are summarized in Table 2. The most common reason was lack of indication or unjustified use (86.7%), followed by duration-related issues (35.6%), and deviations from guideline or stewardship principles (33.3%). Prescription of postoperative antibiotics without indication (44.4%), prolonged duration of therapy (26.1%), and unnecessary Methicillin Resistant Staphylococcus aureus (MRSA) coverage (20.6%) were the most frequently identified contributors to inappropriate use. Table 2. Reasons for inappropriate antibiotic use Category/Reason † Frequency, n (%) * Lack of indication/unjustified use (156; 86.7%) No indication for postoperative antibiotics 80 (44.4) No indication for MRSA coverage 37 (20.6) No indication for antibiotic therapy 34 (18.8) MRSA not confirmed 4 (2.2) Treatment of colonization rather than infection 1 (0.6) Guideline or stewardship deviations (60; 33.3%) Escalation without clinical indication 34 (18.8) Escalation despite documented resistance 2 (1.1) Unwarranted escalation 1 (0.6) Missed de-escalation opportunity 5 (2.8) Not in accordance with clinical guidelines 18 (10.0) Duration-related issues (64; 35.6%) Prolonged duration of therapy 47 (26.1) Incomplete duration of therapy 13 (7.2) No justification for early discontinuation 3 (1.7) Therapy discontinued despite positive culture 1 (0.6) Drug/dose/route errors (35; 19.4%) Omission of loading dose for vancomycin 28 (15.6) Lack of TDM monitoring for vancomycin 1 (0.6) Incorrect dosing frequency 2 (1.1) Failure to switch from intravenous to oral route 4 (2.2) Inappropriate selection/coverage (14; 7.8%) Inappropriate antibiotic selection 5 (2.8) Inadequate spectrum of coverage 4 (2.2) Resistance to prescribed antibiotic 5 (2.8) Duplication (4; 2.2%) Duplicate coverage 3 (1.7) Duplicate therapy 1 (0.6) Notes: * Percentages are calculated using the total number of patients with inappropriate antibiotic therapy (n = 180). † Patients may have had more than one reason recorded; therefore, percentages do not sum to 100%. Abbreviations: MRSA: Methicillin-resistant Staphylococcus aureus, TDM: Therapeutic Drug Monitoring Figure 1 presents the distribution of antibiotic prescribing practices across medical, surgical, and ICU specialties. Results are stratified by specialty, appropriateness of empirical antibiotic initiation, culture positivity, and overall inappropriateness of antibiotic therapy prescribed by the primary physician or intensivist. Among patients managed primarily by the ICU team, the rate of appropriate empirical antibiotic initiation was high (89.3%), while the proportion receiving inappropriate therapy was relatively low (32.1%). In contrast, surgical specialties overseeing patients during ICU admission demonstrated less favorable prescribing patterns, with the highest rates of inappropriate antibiotic prescribing observed in thoracic surgery (72.7%), neurosurgery (62.7%), and plastic surgery (62.5%). Nephrology and urology exhibited exemplary antimicrobial stewardship, with no cases of inappropriate therapy documented. Lower rates of inappropriateness were also noted in oncology (21.2%) and neurology (12.5%). A detailed summary of infection indications, empiric initiation, culture positivity, and inappropriate antibiotic therapy across specialties is provided in Supplementary Table S1. 3.2.2. Appropriateness of Frequently Prescribed Antibiotics A total of 1,605 antibiotic prescription orders were recorded among the study cohort. High rates of appropriate use were observed for amikacin (96.9%), meropenem (92.2%), and piperacillin/tazobactam (87.1%), indicating strong adherence to prescribing guidelines. Linezolid (86.2%) also demonstrated a high appropriateness rate. In contrast, cefepime (48.2%), clindamycin (37.1%), metronidazole (36.2%), and cefazolin (33.9%) exhibited the highest proportions of inappropriate use. A complete breakdown of prescribed antibiotics and corresponding rates of inappropriate use is provided in Table 3. Table 3. Complete list of prescribed antibiotics and proportion of inappropriate use Antibiotic class Antibiotic ATC code Number of prescriptions (n) * Inappropriate use, n (%) † Cephalosporins Cefuroxime J01DC02 17 (1.1) 13 (76.5) Cefepime J01DE01 85 (5.3) 41 (48.2) Cefazolin J01DB04 56 (3.5) 19 (34.5) Ceftriaxone J01DD04 59 (3.7) 11 (18.6) Cefiderocol J01DI04 2 (0.1) 1 (50.0) Ceftazidime/avibactam J01DD52 19 (1.2) 1 (5.3) Nitroimidazoles Metronidazole J01XD01 69 (4.3) 25 (36.2) Lincosamides Clindamycin J01FF01 35 (2.2) 13 (37.1) Glycopeptides Vancomycin J01XA01 296 (18.4) 76 (25.7) Macrolides Azithromycin J01FA10 119 (7.4) 27 (22.7) Clarithromycin J01FA09 1 (0.1) 1 (100) Tetracyclines Doxycycline J01AA02 18 (1.1) 4 (22.2) Tigecycline J01AA12 7 (0.4) 1 (14.3) Penicillins Co-Amoxiclav J01CR02 19 (1.2) 4 (21.1) Flucloxacillin J01CF05 5 (0.3) 1 (20.0) Sulfonamides TMP/SMX J01EE01 15 (1.0) 3 (20.0) β-lactam/β-lactamase inhibitors Piperacillin/tazobactam J01CR05 295 (18.4) 38 (12.9) Oxazolidinones Linezolid J01XX08 58 (3.6) 8 (13.8) Fluroquinolones Levofloxacin J01MA12 9 (0.6) 1 (11.1) Carbapenems Meropenem J01DH02 218 (13.6) 17 (7.8) Monobactams Aztreonam J01DF01 16 (1.0) 1 (6.3) Aminoglycosides Amikacin J01GB06 64 (4.0) 2 (3.1) Other Rifaximin A07AA11 16 (1.0) 2 (12.5) Notes: * Percentages for prescriptions are calculated using the total number of prescriptions across all antibiotics (N = 1,605). † Percentages use the number of prescriptions for each drug as the denominator. Abbreviations: ATC: Anatomical Therapeutic Chemical, TMP/SMX: Trimethoprim/sulfamethoxazole 3.2.3. Clinical Pathway of Antibiotic Prescribing Empirical antibiotic therapy was initiated in 475 (95.0%) patients. Culture and susceptibility testing (CST) was performed in 402 (84.6%) of these patients, and antibiotic therapy was subsequently modified in 377 (93.8%) patients based on microbiological results. Modifications included escalation, de-escalation, optimization, or appropriate discontinuation, reflecting widespread adoption of culture-guided antimicrobial management. 3.3. Predictors Associated with Antibiotic Appropriateness Results of univariable and multivariable logistic regression analysis are shown in Table 4. In univariable analysis, multiple factors were significantly associated with antibiotic appropriateness, including cardiovascular diseases, endocrine disorders, renal impairment, recent hospitalization or invasive surgery, receipt of enteral/parenteral nutrition, RRT, positive culture results, bacteremia, persistent infection, sepsis, septic shock, organ failure, and MDRO status. After multivariable adjustment, three factors remained independently associated with appropriate antibiotic therapy: Presence of a central line (AOR = 0.79, 95% CI = 0.68–1.08, p = 0.004), positive microbiological culture results (AOR = 1.25, 95% CI = 1.13–1.38, p < 0.001), and sepsis (AOR = 1.18, 95% CI = 1.06–1.32, p = 0.003). Presence of a central line was associated with approximately 21% lower odds of receiving appropriate antibiotic therapy. Conversely, patients with positive microbiological cultures and those with sepsis had approximately 25% and 18% higher odds, respectively, of receiving appropriate antibiotic treatment. Table 4. Logistic regression analysis of therapy-related variables associated with antibiotic appropriateness Variable COR (95% CI) p -value † AOR (95% CI) p -value § Age 1.00 (1.00–1.01) <0.001 1.00 (1.00–1.00) 0.668 Gender 0.93 (0.86–1.01) 0.095 0.94 (0.86–1.02) 0.131 BMI 1.00 (0.99–1.00) 0.156 1.00 (0.99–1.00) 0.296 LoS 1.00 (1.00–1.01) 0.309 Cardiovascular disease 1.80 (1.70–1.90) <0.001 0.97 (0.86–1.09) 0.619 Endocrine disorder 1.20 (1.10–1.30) <0.001 1.09 (0.98–1.21) 0.100 Renal impairment 1.22 (1.10–1.35) <0.001 1.08 (0.95–1.23) 0.255 Recurrent infections 1.14 (0.92–1.40) 0.235 1.12 (0.91–1.38) 0.293 Hospitalization within past 90 days 1.12 (1.03–1.22) 0.008 1.01 (0.93–1.10) 0.867 Invasive surgery within past 90 days 0.88 (0.81–1.04) 0.004 0.96 (0.88–1.04) 0.297 Antibiotic use within past 90 days 0.99 (0.90–1.08) 0.794 MDR colonization within past 90 days 0.92 (0.79–1.07) 0.272 Total number of MDR risk factors 1.02 (0.99–1.06) 0.185 1.03 (0.99–1.06) 0.146 Presence of central line 0.85 (0.72–1.01) 0.043 0.79 (0.68–1.08) 0.004 Mechanical ventilation 1.03 (0.94–1.12) 0.542 Intubation 1.00 (0.92–1.09) 0.958 Vasopressor use 1.01 (0.93–1.11) 0.761 RRT 1.15 (1.03–1.28) 0.010 0.94 (0.82–1.07) 0.336 Enteral/parenteral nutrition received 1.13 (1.03–1.23) 0.008 1.02 (0.93–1.12) 0.616 Positive culture result 1.33 (1.23–1.44) <0.001 1.25 (1.13–1.38) <0.001 Bacteremia 1.22 (1.07–1.39) 0.002 1.03 (0.90–1.17) 0.693 Persistent infection despite therapy 1.24 (1.13–1.37) <0.001 1.10 (0.99–1.22) 0.068 Sepsis 1.25 (1.15–1.36) <0.001 1.18 (1.06–1.32) 0.003 Septic shock 1.14 (1.03–1.27) 0.013 0.94 (0.83–1.07) 0.381 Organ failure 1.20 (1.10–1.32) <0.001 1.04 (0.93–1.17) 0.479 Steroid use 1.07 (0.98–1.16) 0.137 1.06 (0.97–1.15) 0.181 Sedative use 0.97 (0.89–1.05) 0.457 MDRO status 1.11 (1.05–1.17) <0.001 0.99 (0.93–1.06) 0.840 Notes: † Variables with a p-value ≤0.25 in univariate analysis were considered for inclusion in the multivariable logistic regression model. § A p -value < 0.05 level was considered to be statistically significant. Abbreviations: AOR: Adjusted Odds Ratio, BMI: Body Mass Index, COR: Crude Odds Ratio, LoS: Length of Stay, MDRO: Multidrug-Resistant Organism, MDR: Multidrug-Resistant, RRT: Renal Replacement Therapy 3.4. Impact of Antibiotic Therapy Appropriateness on Clinical Outcomes Associations between antibiotic therapy appropriateness and clinical outcomes are summarized in Table 5. Patients who received appropriate antibiotic therapy had higher crude rates of nosocomial infections (29.1% vs. 10.6%, p < 0.001), persistent infection despite therapy (30.6% vs. 13.3%, p < 0.001), and organ dysfunction (37.2% vs. 20.0%, p < 0.001). These findings likely reflects greater baseline illness severity and poor prognostic indicators of patients in the appropriate therapy group, as shown in the baseline characteristics, rather than an adverse effect of appropriate therapy. No statistically significant differences were observed between groups in ICU LoS (median 6 vs. 5 days, p = 0.289), in-hospital mortality (12.8% vs. 9.4%, p = 0.433), transfer to ward (74.7% vs. 75.6%, p = 0.823), or discharge status (12.5% vs. 15.0%, p = 0.436). Table 5. Association of clinical outcomes with the appropriateness of antibiotic therapy Clinical variables Total, n (%) * Appropriate therapy, n (%) † Inappropriate therapy, n (%) † p -value § LoS, days, median ( IQR ) 5.5 (4–9) 6 (4–9) 5(4–8) 0.289 Adverse drug events 50 (10) 31 (9.7) 19 (10.6) 0.756 In-hospital mortality 58 (11.6) 41 (12.8) 17 (9.4) 0.433 Transferred to ward 375 (75.0) 239 (74.7) 136 (75.6) Discharged alive 67 (13.4) 40 (12.5) 27 (15.0) Notes: * Percentages are calculated relative to the overall cohort (N = 500). † Percentages are calculated using their respective denominators (n = 320 and n = 180). Abbreviations: LoS: Length of Stay Tests: Continuous data was assessed with Mann-Whitney U test, and categorical data was assessed with chi-square test. § A p -value < 0.05 level was considered to be statistically significant. 4. Discussion In this cohort, 64% of critically ill patients received antibiotic therapy that was deemed appropriate. This rate is comparable to findings from South Africa, where an appropriateness rate of 70.2% was reported (20). In contrast, substantially lower rates have been documented in studies from Pakistan (14%) (18) and Sudan (28%) (32), where the absence of in-house infection management guidelines and ASP were identified as key contributors. The relatively higher appropriateness observed in our study likely reflects the influence of a national ASP framework (33,34), the presence of an active institutional ASP committee aligned with IDSA guidelines, and the integration of clinical pharmacists within the ICU multidisciplinary team. These factors have previously been associated with improved guideline adherence and reduced antimicrobial misuse (28,35,36). The leading driver of inappropriate antibiotic use in more than three-quarters of cases was lack of indication or unjustified therapy, with postoperative antibiotics prescribed without indication accounting for nearly half of all inappropriate prescriptions. Duration-related issues, particularly prolonged therapy (26.1%), were also frequently observed. Similar findings have been reported in Türkiye, where one study identified unnecessary antibiotic use in 38.9% of patients and prolonged surgical prophylaxis in 34.1% (10), while another reported lacked indication in 47% and prolonged therapy in 26% of cases (9). Conversely, a Brazilian study highlighted different contributors to inappropriateness. In 80.2% of empirical treatments, the most frequently reported reasons were failure to request cultures and bacterial resistance, whereas in 58.3% of sensitivity-guided treatments, resistance to the chosen antibiotics and excessive cost were the predominant factors (19). These variations likely reflect differences in case mix, admission profiles, and institutional practices across study settings. In the present study, 29% of ICU admissions were postoperative, and prolonged prophylaxis beyond the recommended 24-hour period was common. Robust evidence indicates that extending postoperative antibiotic prophylaxis beyond this timeframe offers no additional clinical benefit (37–40). Duration-related problems were also observed among non-surgical patients, reinforcing previous evidence that shorter antibiotic courses can reduce AMR and nosocomial infections (41), while still allowing for individualized decision-making based on clinical and laboratory assessments (33,42). Although intensivists were primarily responsible for ICU patient management, antibiotic prescribing involved multiple medical and surgical specialties. Surgical services exhibited the highest rates of inappropriate antibiotic use, ranging from 62.7% to 72.7% across disciplines. Comparable findings have been reported in Türkiye, where inappropriate prescribing was more frequent in surgical ICUs than in medical ICUs (51.4% vs 30.7%, p < 0.001) (9). In contrast, a South African study reported high antibiotic overuse among cardiology patients, with 78.5% lacking a documented indication (20). These discrepancies may stem from differences in admission rates, patient demographics, case mix, study methodologies, and availability of stewardship resources, such as infectious disease consultations and clinical pharmacist involvement. Our findings emphasize the need to strengthen stewardship interventions within surgical departments, including regular audits and targeted feedback, which have been shown to improve prescribing practices (29). Among antibiotic classes, cephalosporins demonstrated the highest rates of inappropriateness, particularly cefuroxime (76.5%), cefepime (48.2%), and cefazolin (33.9%). These patterns were especially evident among surgical specialties, suggesting suboptimal prophylactic prescribing practices in these departments. Comparable trends have been reported in Kenya, where cefuroxime (93.3%), ceftriaxone (83%), and cefazolin (85.7%) were inappropriately used (43). This is concerning given the intrinsic resistance of organisms such as Pseudomonas aeruginosa and Enterococcus species to first and second generation cephalosporins (44). Furthermore, the UAE National AMR Surveillance report (2023) documented rising resistance to third and fourth generation cephalosporins among Escherichia coli and Klebsiella pneumoniae (23). The CST plays a critical role in supporting appropriate antimicrobial decision making (37,38). In this study, empirical therapy was initiated in 95% of patients, CST was performed in 84.6%, and antibiotic therapy was subsequently modified in 93.8% of those tested. In contrast, a South African study reported lower CST rates (61.2%) and de-escalation (13.1%) (20), while a Sudanese study showed 80.4% empirical initiation but only 18.6% CST performance and 8.8% therapy modification (8). These differences highlight the importance of timely microbiological testing and effective interpretation of results to facilitate culture-guided therapy, optimize antimicrobial selection, and reduce unnecessary exposure to broad-spectrum agents (17,33). Multivariable logistic regression analysis identified three independent predictors of appropriate antibiotic therapy: the presence of a central line, positive microbiology culture, and sepsis. Patients with positive cultures had nearly 25% higher odds of receiving appropriate antibiotic therapy (AOR = 1.25, 95% CI = 1.13–1.38, p < 0.001). This finding is consistent with a systematic review and meta-analysis by Raman et al., which demonstrated significantly lower treatment failure when antimicrobial therapy was guided by microbiological data (OR 0.22, 95 % CI 0.14–0.35) (46). Similarly, a study from Singapore evaluating microbiology culture status in patients with severe sepsis reported that individuals with positive cultures were more likely to receive tailored and optimized therapy once results became available (11). Collectively, these findings support the role of timely pathogen identification and effective interpretation of microbiological results in enhancing antibiotic appropriateness and reinforcing culture-guided prescribing practices. Patients with sepsis had approximately 18% higher odds of receiving appropriate antibiotic therapy (AOR = 1.18, 95% CI = 1.06–1.32, p = 0.003). This aligns with a systematic review by Paul et al., which reported improved survival among patients who received appropriate antimicrobial therapy within the first 48 hours of sepsis management (95% CI = 1.37–1.86) (47). A study from Indonesia further demonstrated a significant association between inappropriate antibiotic dosing and poorer mortality outcomes among patients with sepsis ( p = 0.034) (48). Although the Surviving Sepsis Campaign outlines stepwise management strategies, the severity and uncertainty associated with early sepsis may prompt physicians to overprescribe broad-spectrum antibiotics or combination regimens to cover potential unconfirmed infections, practices that may not always align with guideline recommendations (31,49). In contrast, patients with a central line had around 21% lower odds of receiving appropriate therapy compared with those without (AOR = 0.79, 95% CI = 0.68–1.08, p = 0.003). Yokota et al. reported that although 81% of patients in their study received appropriate antibiotic therapy, none of the variables assessed were significantly associated with inappropriate of therapy among patients with central line-associated bloodstream infections (50). Evidence examining the relationship between central line use and antibiotic appropriateness remains limited. Most existing studies primarily focus on infection incidence, prevention, or mortality related to central line use rather than appropriateness of antibiotic therapy across the full treatment course, limiting direct comparison with present findings. Analysis of clinical outcomes revealed that patients receiving appropriate antibiotic therapy had higher crude rates of nosocomial infections (29.1% vs. 10.6%), persistent infections (30.6% vs. 13.3%), and organ dysfunction (37.2% vs. 20.0%), while LoS and mortality were comparable between groups. These findings likely reflect greater baseline severity and poor prognostic indicators among patients receiving appropriate therapy, rather than an adverse effect of appropriate antimicrobial management. In contrast, several studies have reported increased infection persistence, higher mortality, (8,18) and prolonged ICU stay among patients receiving inappropriate antibiotic therapy (19,45). Differences across studies may be attributable to higher patient survival, which is associated with prolonged ICU stays and increased exposure to invasive devices and hospital flora, thereby predisposing patients to nosocomial infections and persistent infections despite ongoing therapy. In addition, robust infection-control measures and effective ASPs can optimize therapeutic regimens and support improved survival among critically ill patients, even those admitted with severe disease or poor prognostic profiles. This study has few limitations. Its retrospective design relied on the accuracy and completeness of EMRs, and poor documentation occasionally hindered data extraction, resulting in the exclusion of some patients. In addition, some patients were admitted to the ICU with multiple concurrent diagnosis, which made it challenging to determine antibiotic appropriateness prior to confirmation of infection. As this was a single-center study, the generalizability of the findings may be limited. Furthermore, the limited availability of institution-specific antibiotic guidelines necessitated the use of IDSA guidelines to assess appropriateness. Although this approach ensured consistency with established evidence-based recommendations, it may not have fully captured local AMR patterns, formulary restrictions, or institution specific prescribing practices. Several real-world systemic and operational barriers were also identified. Limited availability of infectious disease consultants restricted consistent daily follow-up and timely reassessment of ICU cases, particularly those requiring rapid escalation due to MDROs. Communication gaps and suboptimal integration among multidisciplinary teams occasionally resulted in overlapping or conflicting antibiotic decisions. Additionally, gaps in adherence to local guideline-based therapy and limited awareness of resistance patterns persisted among prescribers, despite the presence of an established ASP in the hospital. Poor documentation further constrained real-time stewardship activities. Finally, defensive prescribing often driven by concern over missing life-threatening infections in clinically deteriorating patients may have contributed to unnecessary continuation of broad-spectrum empirical therapy. 5. Conclusions This study demonstrated a relatively lower rate of inappropriate antibiotic use among critically ill patients compared with several published reports. Nevertheless, persistent challenges, such as unjustified postoperative antibiotic use and prolonged treatment durations, remains evident even in the presence of an established ASP. Overuse was influenced by entrenched prescribing habits within certain surgical specialties, such as neurosurgical teams managing patients with external ventricular drains (EVD) for their patients post neurosurgical procedures, and was further compounded by multidisciplinary involvement that occasionally led to inconsistent application of stewardship recommendations. To enhance antimicrobial stewardship effectiveness, interventions must extend beyond traditional stewardship models. Key strategies include strengthening interprofessional communication, clearly defining stewardship roles across subspecialties, enforcing thorough documentation of antibiotic indications, reviewing timelines and stop dates (e.g. 72-hour time-out), providing continuous education for physicians and residents, reinforcing the role of clinical pharmacists through bedside stewardship audits, and ensuring timely infectious disease consultation and follow-ups. Routine evaluation of stewardship program performance and implementation of tailored strategies to improve prescriber adherence and responsiveness to local resistance patterns are also essential. Collectively, these measures may curb inappropriate antibiotic use and improve clinical outcomes among critically ill patients in the ICU. Abbreviations AMR- Antimicrobial Resistance AOR- Adjusted Odds Ratio ASP- Antimicrobial Stewardship Program BMI- Body Mass Index CI- Confidence Interval COVID-19- Coronavirus Disease 2019 CRRT- Continuous Renal Replacement Therapy CST- Culture and Susceptibility Testing DKA- Diabetic Ketoacidosis EMR- Electronic Medical Record HD- Hemodialysis ICU- Intensive Care Unit IDSA- Infectious Diseases Society of America IQR- Interquartile Range LoS- Length of Stay MDRO- Multidrug-Resistant Organism MDR- Multidrug-Resistant MRSA- Methicillin Resistant Staphylococcus aureus RRT- Renal Replacement Therapy TMP/SMX- Trimethoprim/Sulfamethoxazole UAE- United Arab Emirates Declarations 7.1. Ethics Approval and Consent to Participate The study protocol was reviewed and approved by the Institutional Review Board of Gulf Medical University, Ajman, UAE (Reference Number: IRB-COP-STD-44-Oct-2024) and Tawam Hospital, Al Ain, UAE (Reference Number: MF2058-2025-1247), ensuring compliance with institutional data protection policies, adherence to ethical standards, and prioritization of participant confidentiality and safety (Clinical trial number: Not applicable). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. As this was a retrospective study involving the review of existing medical records, the requirement for informed consent was waived by the Institutional Review Board, as the research posed minimal risk to participants and did not involve any direct patient contact or intervention. All data were fully anonymized and de-identified prior to analysis to ensure confidentiality. Access to patient information was restricted to authorized study investigators, and data were stored on secure, password protected systems in accordance with institutional and national protection regulations. 7.2. Consent for Publication All authors have read and approved the final version of the manuscript and consent to its submission and publication 7.3. Availability of Data and Materials The data supporting the findings of this article will be made available by the corresponding author upon reasonable request. 7.4. Competing Interests The authors declare that they have no financial or personal relationships that could inappropriately influence or bias the content of this work. 7.5. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 7.6. Authors’ contributions IK was extensively involved in data collection, data curation, and data analysis. MAB and SHHR contributed to the methodological framework data curation and validation, participated in data interpretation, and provided limited supervision of the research process. SMG, JML, and KCM contributed to data interpretation. VM conceptualized and designed the study, contributed to data curation, data analysis, and data interpretation, and supervised the entire research process. All authors contributed to manuscript drafting and critical revision and approved the final version of the manuscript. 7.7. Acknowledgments The authors acknowledge the support of the participating institution in facilitating access to medical records and assisting in the conduct of this study. Each author contributed substantially to the conception and design of the study, data collection and interpretation, and the drafting and critical revision of the manuscript. All authors approved the final version of the manuscript for publication. References WHO. Antimicrobial resistance EURO [Internet]. 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Evaluation of Antibiotic Use Among Sepsis Patients in an Intensive Care Unit: A cross-sectional study at a referral hospital in Indonesia. Sultan Qaboos Univ Med J. 2018;18(3):e367–373. Shappell CN, Yu T, Klompas M, Agan AA, DelloStritto L, Faine BA et al. Frequency of Antibiotic Overtreatment and Associated Harms in Patients Presenting With Suspected Sepsis to the Emergency Department: A Retrospective Cohort Study. Clin Infect Dis 2025 June 15;80(6):1197–207. Yokota PKO, Marra AR, Belluci TR, Victor EDS, Santos OFPD, Edmond MB. Outcomes and Predictive Factors Associated with Adequacy of Antimicrobial Therapy in Patients with Central Line-Associated Bloodstream Infection. Front Public Health [Internet]. 2016 Dec 23 [cited 2025 Oct 6];4. Available from: http://journal.frontiersin.org/article/ 10.3389/fpubh.2016.00284/full Additional Declarations No competing interests reported. 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Background","content":"\u003cp\u003eAntimicrobial resistance (AMR) has emerged as a major global public health threat, with an increasing number of deaths directly or indirectly attributable to resistant infections. According to the Centers for Disease Control and Prevention, approximately 1.27\u0026nbsp;million deaths in 2019 were directly caused by AMR, while an estimated 4.95\u0026nbsp;million deaths were associated with AMR worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Patterns of antimicrobial use within healthcare settings play a central role in the development and spread of resistance, with inappropriate antibiotic prescribing being a key contributor to increased morbidity, mortality, and the emergence of resistant pathogens globally (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients admitted to intensive care units (ICU) are estimated to be five to ten times more susceptible to infections than those in general wards or non-hospital settings, largely due to the complexity and critical nature of their underlying conditions (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Antibiotic prescribing in ICUs is influenced by multiple factors, including physician\u0026rsquo;s prescribing behaviors (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), prolonged durations of therapy (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), the absence of definitive infection markers (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and patient-related characteristics such as comorbidities, prior hospital admissions, invasive surgical procedures, microbiological culture results, and previous antibiotic exposure (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These challenges often constrain intensivists to a narrow time window for clinical assessment and decision-making, increasing the risk of inappropriate antibiotic initiation, continuation, or escalation (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvidence from previous studies assessing antibiotic use in ICUs have reported substantial variability in the prevalence of inappropriate prescribing. Studies from low- and middle-income countries have documented higher rates of irrational antibiotic use in the ICUs, ranging from 43% to 77.2% (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), whereas relatively lower rates, between 29.8% and 44%, have been reported in upper-middle- and high-income countries (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Importantly, the implementation of antimicrobial stewardship programs (ASPs) within healthcare facilities has been consistently associated with improved physician adherence to evidence-based infection management guidelines and more judicious antibiotic prescribing (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the United Arab Emirates (UAE), several national initiatives have been undertaken to optimize antibiotic use and mitigate AMR, including the adoption of ASP frameworks across healthcare institutions (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), enforcement of regulatory restrictions on the dispensing of broad-spectrum antibiotics in community pharmacies (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), and the establishment of the UAE National AMR Surveillance System to monitor resistance trends nationwide (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Despite these efforts, existing studies have largely focused on hospital-wide antibiotic utilization, with ICU data often aggregated with other patient populations and without detailed evaluation of prescribing appropriateness or associated clinical outcomes (\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Consequently, the present study was conducted to assess the appropriateness of antibiotic prescribing among ICU patients and to examine its association with clinical outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1. Study Design and Setting\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study was conducted at a large tertiary care hospital operating under the Abu Dhabi Health Services Company and PureHealth management systems in the UAE. The ICU comprises 20 beds, including two isolation rooms and six rooms equipped for renal replacement therapy (RRT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2. Antimicrobial Stewardship\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hospital has an established ASP committee responsible for reviewing international guidelines and adapting them into institution-specific protocols for antibiotic use. Clinical pharmacists play an active role in the development, periodic review, and updating of these guidelines and are involved in key ASP activities, including formulary restriction, prospective audit and feedback, and participation in daily ward rounds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3. Study Population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical records of patients admitted to the ICU between January 2023 and December 2024 were retrieved and screened according to predefined inclusion and exclusion criteria. Patients aged 18 years and above who were admitted to the ICU for more than 24 hours and received at least one systemic antibiotic during their ICU stay were included. Patients were excluded if their ICU stay was shorter than 24 hours or exceeded 30 days, or if essential clinical data were missing from their records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4. Study Tool and Variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA standardized data collection form was developed to ensure consistent and comprehensive extraction of relevant information from patient medical records. The tool captured variables aligned with the study objectives, including patient demographics, clinical characteristics, laboratory and microbiological indices, antibiotic therapy details, antimicrobial stewardship indicators, and clinical outcomes.\u003c/p\u003e\n\u003cp\u003eSociodemographic variables included age, gender, body mass index (BMI), and ethnicity. Clinical variables encompassed the reason for ICU admission, comorbidities, and lifestyle factors. ICU-related and outcome variables included the presence of central lines, mechanical ventilation, intubation, vasopressor use, systemic corticosteroid therapy, nutritional support, sedatives, RRT, development of organ dysfunction, length of ICU stay, and discharge disposition. Laboratory and microbiological variables included body temperature, hematologic parameters, inflammatory markers, renal function indices, blood glucose, mean arterial pressure (MAP), culture and susceptibility results, infection type, and identified organisms. Antibiotic therapy-related variables comprised prescribed agents, indication, route of administration, dosage, dosing frequency, duration of therapy, therapeutic drug monitoring (where applicable), and any modifications such as escalation, de-escalation, optimization, or discontinuation. In addition, supplementary variables were collected on patients’ clinical history within 90 days prior to ICU admission, including previous hospital admissions, invasive procedures or surgeries, microbiological culture results, and prior antibiotic exposure. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.5. Data Collection \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing development of the standardized data collection tool, all ICU antibiotic orders during the study period were retrieved from the hospital’s electronic medical record (EMR) system. To ensure one unique record per patient encounter, repeated antibiotic orders within the same admission were consolidated. Readmissions occurring within 24 hours of discharge were considered a continuation of the initial admission and were merged into a single episode. The original dataset was retained to allow review of all antibiotic orders associated with each patient. Screening was conducted in two stages: an initial automated search based on admission criteria and antibiotic administration records, followed by manual verification by a trained investigator to confirm eligibility and data completeness. To ensure consistent representation across the study period, eligible records were stratified by year and month of admission. A quota of 30 patients per month was selected. In months with fewer eligible admissions, all available patients were included.\u003c/p\u003e\n\u003cp\u003eData were extracted using the standardized form from structured EMR modules, including admission and discharge records, laboratory and microbiology systems, and the medication administration records. When clarification was required, unstructured documents such as clinical progress notes were reviewed. Supplementary data were retrieved from Malaffi, the regional health information exchange platform, to capture patients’ clinical history within 90 days prior to ICU admission. \u003c/p\u003e\n\u003cp\u003eData collection was performed by a trained investigator following standardized operating procedures. To ensure data quality and integrity, a random subset of the collected records was independently reviewed by a second investigator. Routine validation checks were applied to assess data completeness, consistency, and temporal accuracy. An audit trail was systematically maintained throughout the data collection process to document all revisions and quality control measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.6. Appropriateness of Antibiotic Therapy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe appropriateness of antibiotic therapy was evaluated based on adherence to the hospital’s institutional antibiotic use guidelines. In instances where institutional guidelines were unavailable, recommendations from the Infectious Diseases Society of America (IDSA) were used as the reference standard. When uncertainty arose, the appropriateness of the antibiotic regimen was re-evaluated by a second investigator, and consensus was reached through deliberation.\u003c/p\u003e\n\u003cp\u003eDefinition of appropriateness were informed by criteria from the World Health Organization and antimicrobial stewardship frameworks and were further refined through a review of published studies employing comparable definitions within their research. Core elements of their definitions regarding appropriateness included indication, antibiotic selection, dosage, duration of therapy, and adherence to clinical guidelines (8,9,19,30,31).\u003c/p\u003e\n\u003cp\u003eInappropriate antibiotic use was defined as non-compliance with one or more key parameters, including appropriateness of indication, empiric therapy selection, antibiotic choice, dosage and necessary dose adjustment, route and frequency of administration, duration of therapy, consideration of microbiological susceptibility results, and monitoring of therapeutic drug levels when indicated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.7. Ethical Approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Institutional Ethics Committee of Gulf Medical University, Ajman, UAE (Reference Number: IRB-COP-STD-44-Oct-2024) and Tawam Hospital, Al Ain, UAE (Reference Number: MF2058-2025-1247). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (Clinical trial number: Not applicable). Patient confidentiality was strictly maintained throughout the study. No personal identifiers were recorded, and all data were stored in encrypted, password-protected electronic files accessible only to authorized members of the research team.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.8. Statistical Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analysis were performed using Microsoft Excel (Professional Plus 2016) and Statistical Package for Social Sciences (IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY, USA). Continuous variables were summarized as medians and interquartile ranges (IQR), while categorical variables were expressed as frequencies and percentages. Associations between adherence and categorical variables were analyzed using the Chi-square test or Fischer's exact test, as appropriate. Non-parametric continuous variables were analyzed using the Mann-Whitney U test. Factors independently associated with adherence were identified using logistic regression analysis. Univariable logistic regression analysis was initially conducted and all variables with a \u003cem\u003ep\u003c/em\u003e-value ≤0.25 in univariable analysis were considered for inclusion in the multivariable logistic regression model. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were calculated to determine the strength of association. A \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe initial dataset comprised 8,697 antibiotic prescription orders retrieved from the hospital database, including multiple entries per patient encounter. After consolidation of repeated orders within the same admission, 1,859 unique patient records were identified. During eligibility screening, 598 records were assessed; 98 were excluded due to age criteria (n = 55), ICU length of stay (LoS) outside the predefined range (n = 30), or antibiotic exposure outside the eligible duration (n = 13). A total of 500 patients met the inclusion criteria and were included in the final analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1.\u0026nbsp; Baseline Characteristics of ICU Patients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline demographic, clinical, laboratory, and microbiological characteristics of the study cohort stratified by the appropriateness of antibiotic therapy are presented in Table 1. Patients who received appropriate antibiotic therapy demonstrated indicators of greater baseline illness severity and poorer prognostic profiles at ICU admission compared with those who received inappropriate therapy. Significant differences were observed in age, reason for ICU admission, burden of comorbidities, recent health exposure, organ dysfunction, and microbiological findings. Patients in the appropriate therapy group were more likely to present with sepsis or septic shock, have prior positive cultures, and exhibit multidrug-resistant organism (MDRO) isolates.\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline characteristics of ICU patients stratified by appropriateness of antibiotic therapy\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 500)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAppropriate therapy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 320)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInappropriate therapy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 180)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, years, median (\u003cem\u003eIQR\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (35\u0026ndash;64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (36\u0026ndash;66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42 (33\u0026ndash;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, median (\u003cem\u003eIQR\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.3 (22.5\u0026ndash;31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.3 (22.5\u0026ndash;31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.1 (23.0\u0026ndash;31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLoS, days, median (\u003cem\u003eIQR\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.5 (4\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (4\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5(4\u0026ndash;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGender:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e239 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e144 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e261 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e176 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthnicity:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUAE nationals, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e136 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther Arab\u003csup\u003ea\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAsian\u003csup\u003eb\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e178 (35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e109 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAfrican\u003csup\u003ec\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOthers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReason for ICU admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSepsis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e207 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e160 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;47 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeptic shock, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostoperative, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e145 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrauma, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDKA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRenal impairment\u003csup\u003ed\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e104 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiovascular disorders, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e213 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e155 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeurological/neurodegenerative disorders, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGastrointestinal disorders, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEndocrine disorders, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e194 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143 (44.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRespiratory disorders, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMusculoskeletal disorders, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVascular disorders, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHematological disorders, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrinary disorders, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer-related conditions, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAutoimmune diseases, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRecurrent infections, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost COVID-19 syndrome, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecent risk factors (90 days before admission)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrior hospital admission, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e259 (51.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e180 (56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrior invasive procedure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e221 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e126 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrior antibiotic use, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e352 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e224 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e128 (71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrior positive culture, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e248 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e162 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUse of vasopressors\u003csup\u003ee\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e157 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e102 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55 (30.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUse of systemic corticosteroid therapy\u003csup\u003ef\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e250 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e168 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eRenal replacement therapy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRRT, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70(14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReceived enteral/parenteral nutrition, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e294 (58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e201 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUse of sedatives, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e214 (42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e133 (41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOrgan dysfunction, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e155 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e119 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicrobiology parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTesting performed, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e495 (99.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e319 (99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e176 (97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMDRO isolates, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-MDRO isolates, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e135 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e107 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e \u003csup\u003ea\u003c/sup\u003eOther Arabs included the following countries: Syria, Jordan, Palestine, Oman, Iraq, Bahrain, KSA, Lebanon, Yemen, Qatar. \u003csup\u003eb\u003c/sup\u003eAsia included the following countries: Philippines, India, Bangladesh, Pakistan, Nepal, Sri Lanka, Afghanistan, Indonesia, Iran. \u003csup\u003ec\u003c/sup\u003eAfrica included the following countries: Djibouti, Rwanda, Mauritania, Uganda, Egypt, Ethiopia, Somalia, Morocco, Tunisia, Nigeria, Sahrawi, South Africa, Comoros. \u003csup\u003ed\u003c/sup\u003eRenal Impairment included all patients with AKI, any stages of chronic kidney disease (CKD), End Stage Renal Disease (ESRD), or Hemodialysis (HD). \u003csup\u003ee\u003c/sup\u003eVasopressors refers to the following used: norepinephrine, epinephrine, dopamine, dobutamine, vasopressin and phenylephrine. \u003csup\u003ef\u003c/sup\u003eSteroids refers to the following used: dexamethasone, hydrocortisone, fludrocortisone, methylprednisolone and prednisolone all in intravenous form. \u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eBMI: Body Mass Index, CRRT: Continuous Renal Replacement Therapy, COVID-19: Coronavirus disease 2019, DKA: Diabetic Ketoacidosis, HD: Hemodialysis, ICU: Intensive Care Unit, LoS: Length of Stay, MDRO: Multidrug-Resistant Organism, UAE: United Arab Emirates. \u003cstrong\u003eTests:\u0026nbsp;\u003c/strong\u003eContinuous data were assessed using Mann-Whitney U test, while categorical data were assessed using chi-square test or fisher-exact test. \u003csup\u003e\u0026sect;\u003c/sup\u003eA \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 level was considered to be statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2. Appropriateness of Antibiotic Therapy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 500 ICU patients included, 320 (64%) received antibiotic therapy deemed appropriate based on clinical guideline adherence and microbiological evidence, whereas 180 (36%) patients received inappropriate antibiotic therapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2.1. Reasons for Inappropriate Antibiotic Use\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatterns of inappropriate antibiotic prescribing are summarized in Table 2. The most common reason was lack of indication or unjustified use (86.7%), followed by duration-related issues (35.6%), and deviations from guideline or stewardship principles (33.3%). Prescription of postoperative antibiotics without indication (44.4%), prolonged duration of therapy (26.1%), and unnecessary Methicillin Resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MRSA) coverage (20.6%) were the most frequently identified contributors to inappropriate use.\u003c/p\u003e\n\u003cp\u003eTable 2. Reasons for inappropriate antibiotic use\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory/Reason\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency, n (%)\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLack of indication/unjustified use (156; 86.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo indication for postoperative antibiotics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo indication for MRSA coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo indication for antibiotic therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMRSA not confirmed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTreatment of colonization rather than infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGuideline or stewardship deviations (60; 33.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEscalation without clinical indication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEscalation despite documented resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnwarranted escalation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMissed de-escalation opportunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot in accordance with clinical guidelines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration-related issues (64; 35.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProlonged duration of therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncomplete duration of therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo justification for early discontinuation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTherapy discontinued despite positive culture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug/dose/route errors (35; 19.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOmission of loading dose for vancomycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of TDM monitoring for vancomycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncorrect dosing frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFailure to switch from intravenous to oral route\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInappropriate selection/coverage (14; 7.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInappropriate antibiotic selection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInadequate spectrum of coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResistance to prescribed antibiotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuplication (4; 2.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDuplicate coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDuplicate therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003e\u003csup\u003e*\u003c/sup\u003ePercentages are calculated using the total number of patients with inappropriate antibiotic therapy (n = 180). \u003csup\u003e\u0026dagger;\u003c/sup\u003ePatients may have had more than one reason recorded; therefore, percentages do not sum to 100%. \u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eMRSA: Methicillin-resistant Staphylococcus aureus, TDM: Therapeutic Drug Monitoring\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1 presents the distribution of antibiotic prescribing practices across medical, surgical, and ICU specialties. Results are stratified by specialty, appropriateness of empirical antibiotic initiation, culture positivity, and overall inappropriateness of antibiotic therapy prescribed by the primary physician or intensivist. Among patients managed primarily by the ICU team, the rate of appropriate empirical antibiotic initiation was high (89.3%), while the proportion receiving inappropriate therapy was relatively low (32.1%). In contrast, surgical specialties overseeing patients during ICU admission demonstrated less favorable prescribing patterns, with the highest rates of inappropriate antibiotic prescribing observed in thoracic surgery (72.7%), neurosurgery (62.7%), and plastic surgery (62.5%). Nephrology and urology exhibited exemplary antimicrobial stewardship, with no cases of inappropriate therapy documented. Lower rates of inappropriateness were also noted in oncology (21.2%) and neurology (12.5%). A detailed summary of infection indications, empiric initiation, culture positivity, and inappropriate antibiotic therapy across specialties is provided in Supplementary Table S1.\u003c/p\u003e\n\u003cp\u003e3.2.2. Appropriateness of Frequently Prescribed Antibiotics\u003c/p\u003e\n\u003cp\u003eA total of 1,605 antibiotic prescription orders were recorded among the study cohort. High rates of appropriate use were observed for amikacin (96.9%), meropenem (92.2%), and piperacillin/tazobactam (87.1%), indicating strong adherence to prescribing guidelines. Linezolid (86.2%) also demonstrated a high appropriateness rate. In contrast, cefepime (48.2%), clindamycin (37.1%), metronidazole (36.2%), and cefazolin (33.9%) exhibited the highest proportions of inappropriate use. A complete breakdown of prescribed antibiotics and corresponding rates of inappropriate use is provided in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 3.\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Complete list of prescribed antibiotics and proportion of inappropriate use\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"738\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotic class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eATC code\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of prescriptions (n)\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInappropriate use, n (%)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eCephalosporins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCefuroxime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01DC02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e17 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e13 (76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCefepime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01DE01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e85 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e41 (48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCefazolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01DB04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e56 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e19 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCeftriaxone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01DD04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e59 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e11 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCefiderocol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01DI04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCeftazidime/avibactam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01DD52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e19 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eNitroimidazoles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eMetronidazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01XD01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e69 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eLincosamides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eClindamycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01FF01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e35 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e13 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eGlycopeptides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eVancomycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01XA01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e296 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e76 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eMacrolides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAzithromycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01FA10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e119 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e27 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eClarithromycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01FA09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eTetracyclines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eDoxycycline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01AA02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e18 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTigecycline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01AA12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e7 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003ePenicillins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCo-Amoxiclav\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01CR02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e19 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eFlucloxacillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01CF05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eSulfonamides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTMP/SMX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01EE01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e15 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026beta;-lactam/\u0026beta;-lactamase inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003ePiperacillin/tazobactam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01CR05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e295 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e38 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eOxazolidinones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eLinezolid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01XX08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e58 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e8 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eFluroquinolones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eLevofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01MA12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e9 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eCarbapenems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eMeropenem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01DH02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e218 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e17 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eMonobactams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAztreonam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01DF01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e16 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eAminoglycosides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAmikacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eJ01GB06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e64 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eOther\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eRifaximin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eA07AA11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e16 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003e\u003csup\u003e*\u003c/sup\u003ePercentages for prescriptions are calculated using the total number of prescriptions across all antibiotics (N = 1,605). \u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003ePercentages use the number of prescriptions for each drug as the denominator. \u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eATC: Anatomical Therapeutic Chemical, TMP/SMX: Trimethoprim/sulfamethoxazole\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2.3. Clinical Pathway of Antibiotic Prescribing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmpirical antibiotic therapy was initiated in 475 (95.0%) patients. Culture and susceptibility testing (CST) was performed in 402 (84.6%) of these patients, and antibiotic therapy was subsequently modified in 377 (93.8%) patients based on microbiological results. Modifications included escalation, de-escalation, optimization, or appropriate discontinuation, reflecting widespread adoption of culture-guided antimicrobial management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3. \u0026nbsp;Predictors Associated with Antibiotic Appropriateness\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults of univariable and multivariable logistic regression analysis are shown in Table 4. In univariable analysis, multiple factors were significantly associated with antibiotic appropriateness, including cardiovascular diseases, endocrine disorders, renal impairment, recent hospitalization or invasive surgery, receipt of enteral/parenteral nutrition, RRT, positive culture results, bacteremia, persistent infection, sepsis, septic shock, organ failure, and MDRO status. After multivariable adjustment, three factors remained independently associated with appropriate antibiotic therapy: Presence of a central line (AOR = 0.79, 95% CI = 0.68\u0026ndash;1.08, \u003cem\u003ep\u003c/em\u003e = 0.004), positive microbiological culture results (AOR = 1.25, 95% CI = 1.13\u0026ndash;1.38, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and sepsis (AOR = 1.18, 95% CI = 1.06\u0026ndash;1.32, \u003cem\u003ep\u003c/em\u003e = 0.003). Presence of a central line was associated with approximately 21% lower odds of receiving appropriate antibiotic therapy. Conversely, patients with positive microbiological cultures and those with sepsis had approximately 25% and 18% higher odds, respectively, of receiving appropriate antibiotic treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 4.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eLogistic regression analysis of therapy-related variables associated with antibiotic appropriateness\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.93 (0.86\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.94 (0.86\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eBMI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.00 (0.99\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.00 (0.99\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eLoS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.80 (1.70\u0026ndash;1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.97 (0.86\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eEndocrine disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.20 (1.10\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.09 (0.98\u0026ndash;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eRenal impairment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.22 (1.10\u0026ndash;1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.08 (0.95\u0026ndash;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eRecurrent infections\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.14 (0.92\u0026ndash;1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.12 (0.91\u0026ndash;1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eHospitalization within past 90 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.12 (1.03\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.01 (0.93\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eInvasive surgery within past 90 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.88 (0.81\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.96 (0.88\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eAntibiotic use within past 90 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.99 (0.90\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMDR colonization within past 90 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.92 (0.79\u0026ndash;1.07)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eTotal number of MDR risk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.02 (0.99\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.03 (0.99\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003ePresence of central line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.85 (0.72\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.79 (0.68\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMechanical ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.03 (0.94\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eIntubation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.00 (0.92\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eVasopressor use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.01 (0.93\u0026ndash;1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eRRT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.15 (1.03\u0026ndash;1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.94 (0.82\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eEnteral/parenteral nutrition received\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.13 (1.03\u0026ndash;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.02 (0.93\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003ePositive culture result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.33 (1.23\u0026ndash;1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.25 (1.13\u0026ndash;1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eBacteremia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.22 (1.07\u0026ndash;1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.03 (0.90\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003ePersistent infection despite therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.24 (1.13\u0026ndash;1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.10 (0.99\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSepsis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.25 (1.15\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.18 (1.06\u0026ndash;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSeptic shock\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.14 (1.03\u0026ndash;1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.94 (0.83\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eOrgan failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.20 (1.10\u0026ndash;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.04 (0.93\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSteroid use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.07 (0.98\u0026ndash;1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.06 (0.97\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eSedative use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.97 (0.89\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMDRO status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.11 (1.05\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.99 (0.93\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003eVariables with a p-value \u0026le;0.25 in univariate analysis were considered for inclusion in the multivariable logistic regression model.\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003eA \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 level was considered to be statistically significant. \u003cstrong\u003eAbbreviations:\u003c/strong\u003e AOR: Adjusted Odds Ratio, BMI: Body Mass Index, COR: Crude Odds Ratio, LoS: Length of Stay, MDRO: Multidrug-Resistant Organism, MDR: Multidrug-Resistant, RRT: Renal Replacement Therapy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4. Impact of Antibiotic Therapy Appropriateness on Clinical Outcomes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssociations between antibiotic therapy appropriateness and clinical outcomes are summarized in Table 5. Patients who received appropriate antibiotic therapy had higher crude rates of nosocomial infections (29.1% vs. 10.6%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), persistent infection despite therapy (30.6% vs. 13.3%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and organ dysfunction (37.2% vs. 20.0%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). These findings likely reflects greater baseline illness severity and poor prognostic indicators of patients in the appropriate therapy group, as shown in the baseline characteristics, rather than an adverse effect of appropriate therapy. No statistically significant differences were observed between groups in ICU LoS (median 6 vs. 5 days, \u003cem\u003ep\u003c/em\u003e = 0.289), in-hospital mortality (12.8% vs. 9.4%, \u003cem\u003ep\u003c/em\u003e = 0.433), transfer to ward (74.7% vs. 75.6%, \u003cem\u003ep\u003c/em\u003e = 0.823), or discharge status (12.5% vs. 15.0%, \u003cem\u003ep\u003c/em\u003e = 0.436).\u003c/p\u003e\n\u003cp id=\"_Toc210585563\"\u003eTable 5. Association of clinical outcomes with the appropriateness of antibiotic therapy\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal, n (%)\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAppropriate therapy, n (%)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInappropriate therapy, n (%)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eLoS, days, median (\u003cem\u003eIQR\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5.5 (4\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6 (4\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e5(4\u0026ndash;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAdverse drug events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e50 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e31 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e19 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eIn-hospital mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e58 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e41 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e17 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eTransferred to ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e375 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e239 (74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e136 (75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eDischarged alive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e67 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e40 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e27 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e \u003csup\u003e*\u003c/sup\u003ePercentages are calculated relative to the overall cohort (N = 500).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003ePercentages are calculated using their respective denominators (n = 320 and n = 180).\u003cstrong\u003e\u0026nbsp;Abbreviations:\u0026nbsp;\u003c/strong\u003eLoS: Length of Stay\u003cstrong\u003e\u0026nbsp;Tests:\u003c/strong\u003e Continuous data was assessed with Mann-Whitney U test, and categorical data was assessed with chi-square test.\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003eA \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 level was considered to be statistically significant.\u003c/p\u003e"},{"header":"4.\tDiscussion","content":"\u003cp\u003eIn this cohort, 64% of critically ill patients received antibiotic therapy that was deemed appropriate. This rate is comparable to findings from South Africa, where an appropriateness rate of 70.2% was reported (20). In contrast, substantially lower rates have been documented in studies from Pakistan (14%) (18) and Sudan (28%) (32), where the absence of in-house infection management guidelines and ASP were identified as key contributors. The relatively higher appropriateness observed in our study likely reflects the influence of a national ASP framework (33,34), the presence of an active institutional ASP committee aligned with IDSA guidelines, and the integration of clinical pharmacists within the ICU multidisciplinary team. These factors have previously been associated with improved guideline adherence and reduced antimicrobial misuse (28,35,36).\u003c/p\u003e\n\u003cp\u003eThe leading driver of inappropriate antibiotic use in more than three-quarters of cases was lack of indication or unjustified therapy, with postoperative antibiotics prescribed without indication accounting for nearly half of all inappropriate prescriptions. Duration-related issues, particularly prolonged therapy (26.1%), were also frequently observed. Similar findings have been reported in Türkiye, where one study identified unnecessary antibiotic use in 38.9% of patients and prolonged surgical prophylaxis in 34.1% (10), while another reported lacked indication in 47% and prolonged therapy in 26% of cases (9). Conversely, a Brazilian study highlighted different contributors to inappropriateness. In 80.2% of empirical treatments, the most frequently reported reasons were failure to request cultures and bacterial resistance, whereas in 58.3% of sensitivity-guided treatments, resistance to the chosen antibiotics and excessive cost were the predominant factors (19). These variations likely reflect differences in case mix, admission profiles, and institutional practices across study settings. In the present study, 29% of ICU admissions were postoperative, and prolonged prophylaxis beyond the recommended 24-hour period was common. Robust evidence indicates that extending postoperative antibiotic prophylaxis beyond this timeframe offers no additional clinical benefit (37–40). Duration-related problems were also observed among non-surgical patients, reinforcing previous evidence that shorter antibiotic courses can reduce AMR and nosocomial infections (41), while still allowing for individualized decision-making based on clinical and laboratory assessments (33,42).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough intensivists were primarily responsible for ICU patient management, antibiotic prescribing involved multiple medical and surgical specialties. Surgical services exhibited the highest rates of inappropriate antibiotic use, ranging from 62.7% to 72.7% across disciplines. Comparable findings have been reported in Türkiye, where inappropriate prescribing was more frequent in surgical ICUs than in medical ICUs (51.4% vs 30.7%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (9). In contrast, a South African study reported high antibiotic overuse among cardiology patients, with 78.5% lacking a documented indication (20). These discrepancies may stem from differences in admission rates, patient demographics, case mix, study methodologies, and availability of stewardship resources, such as infectious disease consultations and clinical pharmacist involvement. Our findings emphasize the need to strengthen stewardship interventions within surgical departments, including regular audits and targeted feedback, which have been shown to improve prescribing practices (29).\u003c/p\u003e\n\u003cp\u003eAmong antibiotic classes, cephalosporins demonstrated the highest rates of inappropriateness, particularly cefuroxime (76.5%), cefepime (48.2%), and cefazolin (33.9%). These patterns were especially evident among surgical specialties, suggesting suboptimal prophylactic prescribing practices in these departments. Comparable trends have been reported in Kenya, where cefuroxime (93.3%), ceftriaxone (83%), and cefazolin (85.7%) were inappropriately used (43). This is concerning given the intrinsic resistance of organisms such as \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e and \u003cem\u003eEnterococcus\u003c/em\u003e species to first and second generation cephalosporins (44). Furthermore, the UAE National AMR Surveillance report (2023) documented rising resistance to third and fourth generation cephalosporins among \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eKlebsiella pneumoniae\u0026nbsp;\u003c/em\u003e(23).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe CST plays a critical role in supporting appropriate antimicrobial decision making (37,38). In this study, empirical therapy was initiated in 95% of patients, CST was performed in 84.6%, and antibiotic therapy was subsequently modified in 93.8% of those tested. In contrast, a South African study reported lower CST rates (61.2%) and de-escalation (13.1%) (20), while a Sudanese study showed 80.4% empirical initiation but only 18.6% CST performance and 8.8% therapy modification (8). These differences highlight the importance of timely microbiological testing and effective interpretation of results to facilitate culture-guided therapy, optimize antimicrobial selection, and reduce unnecessary exposure to broad-spectrum agents (17,33).\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression analysis identified three independent predictors of appropriate antibiotic therapy: the presence of a central line, positive microbiology culture, and sepsis. Patients with positive cultures had nearly 25% higher odds of receiving appropriate antibiotic therapy (AOR = 1.25, 95% CI = 1.13–1.38,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001). This finding is consistent with a systematic review and meta-analysis by Raman et al., which demonstrated significantly lower treatment failure when antimicrobial therapy was guided by microbiological data (OR 0.22, 95 % CI 0.14–0.35) (46). Similarly, a study from Singapore evaluating microbiology culture status in patients with severe sepsis reported that individuals with positive cultures were more likely to receive tailored and optimized therapy once results became available (11). Collectively, these findings support the role of timely pathogen identification and effective interpretation of microbiological results in enhancing antibiotic appropriateness and reinforcing culture-guided prescribing practices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients with sepsis had approximately 18% higher odds of receiving appropriate antibiotic therapy (AOR = 1.18, 95% CI = 1.06–1.32, \u003cem\u003ep\u003c/em\u003e = 0.003). This aligns with a systematic review by Paul et al., which reported improved survival among patients who received appropriate antimicrobial therapy within the first 48 hours of sepsis management (95% CI = 1.37–1.86) (47). A study from Indonesia further demonstrated a significant association between inappropriate antibiotic dosing and poorer mortality outcomes among patients with sepsis (\u003cem\u003ep\u003c/em\u003e = 0.034) (48). Although the Surviving Sepsis Campaign outlines stepwise management strategies, the severity and uncertainty associated with early sepsis may prompt physicians to overprescribe broad-spectrum antibiotics or combination regimens to cover potential unconfirmed infections, practices that may not always align with guideline recommendations (31,49).\u003c/p\u003e\n\u003cp\u003eIn contrast, patients with a central line had around 21% lower odds of receiving appropriate therapy compared with those without (AOR = 0.79, 95% CI = 0.68–1.08, \u003cem\u003ep\u003c/em\u003e = 0.003). Yokota et al. reported that although 81% of patients in their study received appropriate antibiotic therapy, none of the variables assessed were significantly associated with inappropriate of therapy among patients with central line-associated bloodstream infections (50). Evidence examining the relationship between central line use and antibiotic appropriateness remains limited. Most existing studies primarily focus on infection incidence, prevention, or mortality related to central line use rather than appropriateness of antibiotic therapy across the full treatment course, limiting direct comparison with present findings.\u003c/p\u003e\n\u003cp\u003eAnalysis of clinical outcomes revealed that patients receiving appropriate antibiotic therapy had higher crude rates of nosocomial infections (29.1% vs. 10.6%), persistent infections (30.6% vs. 13.3%), and organ dysfunction (37.2% vs. 20.0%), while LoS and mortality were comparable between groups. These findings likely reflect greater baseline severity and poor prognostic indicators among patients receiving appropriate therapy, rather than an adverse effect of appropriate antimicrobial management. In contrast, several studies have reported increased infection persistence, higher mortality, (8,18) and prolonged ICU stay among patients receiving inappropriate antibiotic therapy (19,45). Differences across studies may be attributable to higher patient survival, which is associated with prolonged ICU stays and increased exposure to invasive devices and hospital flora, thereby predisposing patients to nosocomial infections and persistent infections despite ongoing therapy. In addition, robust infection-control measures and effective ASPs can optimize therapeutic regimens and support improved survival among critically ill patients, even those admitted with severe disease or poor prognostic profiles.\u003c/p\u003e\n\u003cp\u003eThis study has few limitations. Its retrospective design relied on the accuracy and completeness of EMRs, and poor documentation occasionally hindered data extraction, resulting in the exclusion of some patients. In addition, some patients were admitted to the ICU with multiple concurrent diagnosis, which made it challenging to determine antibiotic appropriateness prior to confirmation of infection. As this was a single-center study, the generalizability of the findings may be limited. Furthermore, the limited availability of institution-specific antibiotic guidelines necessitated the use of IDSA guidelines to assess appropriateness. Although this approach ensured consistency with established evidence-based recommendations, it may not have fully captured local AMR patterns, formulary restrictions, or institution specific prescribing practices.\u003c/p\u003e\n\u003cp\u003eSeveral real-world systemic and operational barriers were also identified. Limited availability of infectious disease consultants restricted consistent daily follow-up and timely reassessment of ICU cases, particularly those requiring rapid escalation due to MDROs. Communication gaps and suboptimal integration among multidisciplinary teams occasionally resulted in overlapping or conflicting antibiotic decisions. Additionally, gaps in adherence to local guideline-based therapy and limited awareness of resistance patterns persisted among prescribers, despite the presence of an established ASP in the hospital. Poor documentation further constrained real-time stewardship activities. Finally, defensive prescribing often driven by concern over missing life-threatening infections in clinically deteriorating patients may have contributed to unnecessary continuation of broad-spectrum empirical therapy.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study demonstrated a relatively lower rate of inappropriate antibiotic use among critically ill patients compared with several published reports. Nevertheless, persistent challenges, such as unjustified postoperative antibiotic use and prolonged treatment durations, remains evident even in the presence of an established ASP. Overuse was influenced by entrenched prescribing habits within certain surgical specialties, such as neurosurgical teams managing patients with external ventricular drains (EVD) for their patients post neurosurgical procedures, and was further compounded by multidisciplinary involvement that occasionally led to inconsistent application of stewardship recommendations.\u003c/p\u003e\n\u003cp\u003eTo enhance antimicrobial stewardship effectiveness, interventions must extend beyond traditional stewardship models. Key strategies include strengthening interprofessional communication, clearly defining stewardship roles across subspecialties, enforcing thorough documentation of antibiotic indications, reviewing timelines and stop dates (e.g. 72-hour time-out), providing continuous education for physicians and residents, reinforcing the role of clinical pharmacists through bedside stewardship audits, and ensuring timely infectious disease consultation and follow-ups. Routine evaluation of stewardship program performance and implementation of tailored strategies to improve prescriber adherence and responsiveness to local resistance patterns are also essential. Collectively, these measures may curb inappropriate antibiotic use and improve clinical outcomes among critically ill patients in the ICU.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAMR- Antimicrobial Resistance\u003c/p\u003e\n\u003cp\u003eAOR- Adjusted Odds Ratio\u003c/p\u003e\n\u003cp\u003eASP- Antimicrobial Stewardship Program\u003c/p\u003e\n\u003cp\u003eBMI- Body Mass Index\u003c/p\u003e\n\u003cp\u003eCI- Confidence Interval\u003c/p\u003e\n\u003cp\u003eCOVID-19- Coronavirus Disease 2019\u003c/p\u003e\n\u003cp\u003eCRRT- Continuous Renal Replacement Therapy\u003c/p\u003e\n\u003cp\u003eCST- Culture and Susceptibility Testing\u003c/p\u003e\n\u003cp\u003eDKA- Diabetic Ketoacidosis\u003c/p\u003e\n\u003cp\u003eEMR- Electronic Medical Record\u003c/p\u003e\n\u003cp\u003eHD- Hemodialysis\u003c/p\u003e\n\u003cp\u003eICU- Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eIDSA- Infectious Diseases Society of America\u003c/p\u003e\n\u003cp\u003eIQR- Interquartile Range\u003c/p\u003e\n\u003cp\u003eLoS- Length of Stay\u003c/p\u003e\n\u003cp\u003eMDRO- Multidrug-Resistant Organism\u003c/p\u003e\n\u003cp\u003eMDR- Multidrug-Resistant\u003c/p\u003e\n\u003cp\u003eMRSA- Methicillin Resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRRT- Renal Replacement Therapy\u003c/p\u003e\n\u003cp\u003eTMP/SMX- Trimethoprim/Sulfamethoxazole\u003c/p\u003e\n\u003cp\u003eUAE- United Arab Emirates\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.1. Ethics Approval and Consent to Participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Institutional Review Board of Gulf Medical University, Ajman, UAE (Reference Number: IRB-COP-STD-44-Oct-2024) and Tawam Hospital, Al Ain, UAE (Reference Number: MF2058-2025-1247), ensuring compliance with institutional data protection policies, adherence to ethical standards, and prioritization of participant confidentiality and safety (Clinical trial number: Not applicable). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. As this was a retrospective study involving the review of existing medical records, the requirement for informed consent was waived by the Institutional Review Board, as the research posed minimal risk to participants and did not involve any direct patient contact or intervention. All data were fully anonymized and de-identified prior to analysis to ensure confidentiality. Access to patient information was restricted to authorized study investigators, and data were stored on secure, password protected systems in accordance with institutional and national protection regulations. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.2. \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eConsent for Publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript and consent to its submission and publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.3. \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eAvailability of Data and Materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this article will be made available by the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.4. \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial or personal relationships that could inappropriately influence or bias the content of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.5. \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.6. \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eAuthors’ contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIK was extensively involved in data collection, data curation, and data analysis. MAB and SHHR contributed to the methodological framework data curation and validation, participated in data interpretation, and provided limited supervision of the research process. SMG, JML, and KCM contributed to data interpretation. VM conceptualized and designed the study, contributed to data curation, data analysis, and data interpretation, and supervised the entire research process. All authors contributed to manuscript drafting and critical revision and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.7. \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the support of the participating institution in facilitating access to medical records and assisting in the conduct of this study. Each author contributed substantially to the conception and design of the study, data collection and interpretation, and the drafting and critical revision of the manuscript. All authors approved the final version of the manuscript for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. Antimicrobial resistance EURO [Internet]. [cited 2025 Sept 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/europe/health-topics/antimicrobial-resistance\u003c/span\u003e\u003cspan address=\"https://www.who.int/europe/health-topics/antimicrobial-resistance\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaghavi M, Vollset SE, Ikuta KS, Swetschinski LR, Gray AP, Wool EE, et al. 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Hosp Pediatr. 2022;12(2):125\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBin Akresh A, Alzeet DS, Alshalan NZ, Alhawsawi MM, Alsaleh NA, Binsuwaidan RA, et al. The impact of antibiotic prophylaxis appropriateness, duration and associated cost on the rate of surgical site infection at a tertiary hospital in Riyadh, Saudi Arabia: A retrospective study. Saudi Med J. 2025 June;46(6):688\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOppelaar MC, Zijtveld C, Kuipers S, Ten Oever J, Honings J, Weijs W et al. Evaluation of Prolonged vs Short Courses of Antibiotic Prophylaxis Following Ear, Nose, Throat, and Oral and Maxillofacial Surgery: A Systematic Review and Meta-analysis. JAMA Otolaryngol Neck Surg. 2019 July 1;145(7):610.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayashi Y, Paterson DL. Strategies for Reduction in Duration of Antibiotic Use in Hospitalized Patients. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://journal.frontiersin.org/article/\u003c/span\u003e\u003cspan address=\"http://journal.frontiersin.org/article/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2016.00284/full\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2016.00284/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Anti-Bacterial Agents, Antimicrobial Stewardship, Guideline Adherence, Intensive Care Units, Treatment Outcome","lastPublishedDoi":"10.21203/rs.3.rs-8481307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8481307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInappropriate antibiotic prescribing is a major driver of antimicrobial resistance and preventable harm, with patients in intensive care units being particularly vulnerable due to frequent and complex antibiotic exposure. Despite expanding antimicrobial stewardship efforts, contemporary evidence evaluating prescribing quality and its clinical implications in intensive care settings within the United Arab Emirates remains limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A retrospective cohort study was conducted in the intensive care unit of a tertiary care hospital. Adult patients admitted between January 2023 and December 2024, with a length of stay exceeding 24 hours but less than 30 days, who received at least one systemic antibiotic and had complete medical records, were included. Data were extracted from electronic medical records and a regional health information exchange platform. Antibiotic appropriateness was evaluated against institutional or Infectious Diseases Society of America guidelines, assessing indication, empiric choice, agent selection, dose, route and frequency, duration, microbiologic concordance, and therapeutic drug monitoring.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 8,697 antibiotic orders retrieved, 1,859 unique records were identified, of which 500 patient records met the eligibility criteria and were analyzed. Overall, 36% of patients received inappropriate antibiotic therapy. The most common reason for inappropriateness was the absence of a clear indication, particularly related to postoperative prophylaxis (44.4%). At the agent level, cefuroxime (76.5%), cefepime (48.2%), and clindamycin (37.1%) exhibited the highest rates of inappropriate use. Multivariable logistic regression identified positive culture results (AOR 1.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and sepsis (AOR 1.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0025) as predictors of appropriate antibiotic use, whereas the presence of a central line was inversely associated (AOR 0.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe overall rate of inappropriate antibiotic prescribing was relatively low, reflecting strengths in culture-guided therapy and infection-focused management. Nevertheless, persistent gaps were identified in postoperative prophylaxis and surgical prescribing practices. These findings highlight the need for targeted antimicrobial stewardship interventions within surgical specialties to further optimize antibiotic use in critically ill patients.\u003c/p\u003e","manuscriptTitle":"Appropriateness of Antibiotic Therapy and its Association with Clinical Outcomes among Critically Ill Patients: A Retrospective Study from the United Arab Emirates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 03:06:18","doi":"10.21203/rs.3.rs-8481307/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":"1949de22-4863-4dca-b5f1-dfed6a9f2cb9","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T09:11:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 03:06:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8481307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8481307","identity":"rs-8481307","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}