Impact of Clinical Flowcharts on Therapeutic Adherence, Treatment Duration, and Clinical Outcomes in Urinary Tract Infections: A Quasi-Experimental Study in a Colombian University Hospital

Impact of Clinical Flowcharts on Therapeutic Adherence, Treatment Duration, and Clinical Outcomes in Urinary Tract Infections: A Quasi-Experimental Study in a Colombian University Hospital | 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 Impact of Clinical Flowcharts on Therapeutic Adherence, Treatment Duration, and Clinical Outcomes in Urinary Tract Infections: A Quasi-Experimental Study in a Colombian University Hospital Ayleen Rivera-Tenorio, Cándida Rosa Díaz Brochero, Cindy Alejandra Bonilla-Sánchez, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7746024/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Urinary tract infections (UTIs) are a common cause of hospitalization and antimicrobial use. This study evaluated the impact of clinical flowcharts on adherence to empirical treatment and clinical outcomes in hospitalized patients with UTIs. Methods A quasi-experimental, ambispective before-and-after study was conducted at a tertiary university hospital in Bogotá, Colombia (January 2023–January 2025). Adult patients with community-acquired UTIs were included. Flowcharts based on guidelines, local susceptibility patterns, and drug availability were implemented. Outcomes included adherence (type, dose, duration), de-escalation, oral switch, clinical response, length of stay, and readmission. Analyses were adjusted using inverse probability of treatment weighting (IPTW). Results A total of 601 patients were analyzed (299 pre- and 302 post-intervention). Overall adherence improved from 38.1% to 51.6% (p < 0.001). Increases were also observed in adherence to antibiotic type (76.9% vs. 85.4%), dose (76.9% vs. 88.1%), and duration (48.8% vs. 59.9%). Post-intervention was independently associated with greater adherence (aOR: 1.61; 95% CI: 1.15–2.24; p = 0.005), without significant impact on clinical outcomes. Discussion Flowcharts improved adherence to empirical therapy without compromising safety or effectiveness, reinforcing their value in antimicrobial stewardship programs. Conclusions Clinical flowcharts are effective tools for standardizing empirical UTI management in high-resistance settings. Urinary Tract Infections Clinical Pathways Medication Adherence Anti-Bacterial Agents/therapeutic use Antimicrobial Stewardship Hospitalization Figures Figure 1 Background Urinary tract infections (UTIs) are a major cause of hospitalization and antimicrobial use globally, with over 404 million cases reported in 2019 and a rising incidence in Latin America and South America ¹-² . Although most UTIs are treatable with antibiotics, increasing antimicrobial resistance has become a critical public health issue worldwide ⁴ ⁵ . In response, various international and national guidelines have been developed to promote rational UTI management and mitigate resistance development ⁶- ⁸ . In Colombia, the situation is particularly concerning due to high resistance rates. Escherichia coli the most frequent uropathogen exhibits resistance rates of up to 49.3% to ciprofloxacin and trimethoprim-sulfamethoxazole, while ESBL production is present in 44% of Klebsiella pneumoniae and 27% of E. coli isolated from urine ⁵ ⁹ ¹⁰ . These resistance patterns challenge the effectiveness of empirical therapy and are associated with higher risks of treatment failure, inappropriate use of broad-spectrum antibiotics, increased healthcare costs, and prolonged hospital stays ¹¹-¹³ . Antimicrobial stewardship programs (ASPs), including the use of structured clinical tools such as treatment flowcharts, have demonstrated improved guideline adherence and reduced inappropriate prescribing ¹⁴-¹⁸ . However, evidence from Latin American hospital settings remains scarce. This study aimed to assess the impact of implementing locally adapted clinical flowcharts on adherence to empirical antibiotic management for community-acquired UTIs in a high-complexity university hospital in Bogotá, Colombia. Secondary outcomes included clinical response, hospital length of stay, antibiotic de-escalation, transition to oral therapy, and relapse-related readmissions. Methods A quasi-experimental, ambispective before-and-after study was conducted at Hospital Universitario San Ignacio (HUSI), a 270-bed tertiary academic hospital in Bogotá, Colombia. The pre-intervention period extended from January to November 2023, followed by a two-month implementation phase (December 2023 to February 2024), and a post-intervention period from March 2024 to January 2025. The study included hospitalized adults (≥ 18 years) diagnosed with urinary tract infection (UTI) (ICD-10 code N39.0) who received empirical antimicrobial therapy. Patients with nosocomial UTIs, early in-hospital mortality, or transfer within 48 hours of admission were excluded. The intervention consisted of clinical flowcharts for UTI management, developed by the institutional antimicrobial stewardship team comprising infectious disease physicians, pharmacists, and nurses based on national and international guidelines, local antimicrobial susceptibility data, and drug availability. These flowcharts provided recommendations for empirical antibiotic selection, dosing, treatment duration, and criteria for de-escalation or switch to oral therapy. They were disseminated in December 2023 through the institutional platform (ALMERA) and reinforced via educational sessions (Supplementary Figure S1 ). Variables Adherence to empirical antimicrobial therapy was defined as fulfillment of all three criteria specified in the institutional flowcharts: appropriate antibiotic selection, correct dosing, and recommended treatment duration. Empirical antibiotic adherence referred to administration of the recommended regimen for the specific clinical syndrome. Dosing adherence involved prescribing the correct daily dose per institutional guidelines, while duration adherence was defined as completing the protocol-indicated course. Treatment durations varied by syndrome: 7 days for uncomplicated pyelonephritis, 5 days for uncomplicated cystitis, and 5 days (women) or 7 days (men) for complicated cystitis. In complicated pyelonephritis, treatment was extended to 14 days in cases with clinical modifiers (e.g., male sex, immunosuppression, structural abnormalities). Clinical response was defined as sustained improvement without hospital readmission for UTI relapse within 28 days of diagnosis, based on RECAPTURE criteria ¹⁹ . Relapse-related readmission referred to recurrence of UTI symptoms requiring hospitalization within 28 days of resolution. De-escalation was defined as spectrum narrowing based on microbiological data. Oral switch was defined as transition from intravenous to oral antibiotics after clinical stabilization. Sample Size Calculation Based on a pilot review of 50 medical records, we estimated an increase in adherence from 80% to 90% after the intervention. Assuming a 95% confidence level and 80% power, a minimum of 202 patients per group (pre- and post-intervention) was required. The final sample included 601 patients. Cases were randomly selected from all 1,216 patients diagnosed with UTI at HUSI in 2023, evenly distributed across both study periods. Data were extracted from electronic medical records using a standardized Excel 2019 template. Statistical Analysis The dataset was reviewed to identify and correct outliers, transcription errors, and missing data using institutional records. Descriptive statistics were used to summarize demographic and clinical characteristics. Normality of continuous variables was assessed via the Kolmogorov–Smirnov test. Continuous variables were reported as measures of central tendency and dispersion; categorical variables were expressed as absolute and relative frequencies. Microbiological findings, including pathogen distribution and resistance patterns, were graphically represented. The primary exposure was the study period (pre- vs. post-intervention). The primary outcome was full adherence to the empirical antimicrobial algorithm, defined as appropriate antibiotic choice, correct dosing, and recommended treatment duration. Secondary outcomes included each adherence component, clinical response, 28-day relapse-related readmission, de-escalation, switch to oral therapy, and hospital length of stay. Covariates included age, sex, Charlson comorbidity index, admitting service, urosepsis, septic shock, bacteremia, heart failure, type 2 diabetes, chronic kidney disease, dementia, immunosuppression (hematologic malignancy, solid organ transplant, or AIDS), empirical antimicrobial regimen, and UTI classification (pyelonephritis vs. cystitis). Propensity Score Weighting (IPTW) To assess the association between the intervention period and complete adherence to the empirical antibiotic algorithm while minimizing confounding, inverse probability of treatment weighting (IPTW) was applied. Propensity scores were estimated via logistic regression, with exposure (pre- vs. post-intervention) as the dependent variable and all relevant covariates as predictors. Stabilized weights were used to estimate the average treatment effect (ATE), implemented through the WeightIt package in R. Covariate balance before and after weighting was evaluated using standardized mean differences (SMD), with values < 0.1 indicating adequate balance. Love plots generated with the cobalt package were used for visual assessment. Models for Binary Outcomes We used IPTW-weighted logistic regression models (R survey package) to evaluate associations between the post-intervention period and binary outcomes (overall adherence, individual components, clinical response, de-escalation, oral switch, and readmission). Models were adjusted for clinically relevant covariates or those with residual imbalance: diabetes, sex, age, pyelonephritis, extended hospital stay, immunosuppression, and bacteremia. We reported adjusted odds ratios (aOR) with 95% confidence intervals (CI), with significance defined as p < 0.05. Analysis of Continuous Outcomes For continuous outcomes such as hospital length of stay and time to oral switch we performed Kaplan–Meier survival analyses and compared groups using the log-rank test. We used IPTW-weighted linear regression to evaluate the association between full adherence and total hospital stay (in days). Clinically relevant covariates were included. Results were reported as beta coefficients (β) with 95% CIs. All analyses were conducted in R version 4.4.1. Results A total of 601 hospitalized patients diagnosed with urinary tract infection (UTI) were analyzed, distributed into two groups: 299 during the pre-intervention period (January–November 2023) and 302 during the post-intervention period (March 2024–January 2025). Clinical and Demographic Characteristics The mean age was comparable between groups (58.98 vs. 58.35 years), as was the proportion of female patients (63.21% vs. 61.92%). After applying inverse probability of treatment weighting (IPTW), excellent balance between groups was achieved, with standardized mean differences (SMD) <0.01 across all key variables, indicating strong comparability (see Supplementary Figure 2). The most prevalent comorbidities were type 2 diabetes mellitus (19.1%) and immunosuppression (24.1%). While a higher prevalence of diabetes and immunosuppression was initially observed in the post-intervention group, this imbalance was substantially corrected following IPTW adjustment. Other relevant conditions including chronic kidney disease, dementia, heart failure, and sepsis were similarly distributed across groups. The most common clinical presentation was pyelonephritis (87.3%), followed by cystitis (12.6%). Bacteremia was more frequently observed in the post-intervention group (12.6% vs. 8.0%), but this difference was also balanced after weighting. Regarding empirical treatment, the most commonly prescribed antibiotic was cefazolin (47.9%), followed by ertapenem (28.4%). Notably, the use of ertapenem as initial empirical therapy was more frequent in the post-intervention group (34.4% vs. 22.4%). Additionally, there was a marked increase in the use of hospital-at-home services during the post-intervention period (22.5% vs. 12.1%). Internal medicine was the primary specialty managing UTI cases, accounting for 69.7% of the cohort overall (68.8% in the pre-intervention group and 70.5% in the post-intervention group). Other specialties such as geriatrics, urology, and medical subspecialties represented smaller and similarly distributed proportions between the two study periods. (See Table 1 and Figure 1.) Table 1. Characteristics of Patients with Urinary Tract Infection Before and After Flowchart Use at San Ignacio Hospital (N = 601). Variable Total Pre-intervention Period (n = 299) Post-intervention Period (n = 302) SMD Pre-intervention (IPTW) Post-interven tion (IPTW) SMD Age, mean (SD) 58.66 (20.06) 58.98 (20.32) 58.35 (19.83) 0.031 58.72 (1.19) 58.71 (1.20) -0.001 Female sex, n (%) 376 (62.56) 189 (63.21) 187 (61.92) 0.027 372 (61.99) 378 (62.74) 0.011 Comorbidities, n (%): Heart failure 33 (5.49) 20 (6.68) 13 (4.30) 0.105 16.3 (5.5) 15.8 (5.2) -0.002 Type 2 diabetes mellitus 115 (19.13) 68 (22.7) 47 (15.67) 0.226 60 (20.2) 60.2 (19.9) -0.002 Chronic kidney disease 32 (5.32) 13 (4.34) 19 (6.29) 0.087 14.9 (5.0) 15.5 (5.1) 0.001 Dementia 33 (5.49) 20 (6.68) 13 (4.30) 0.100 16.5 (5.5) 16.1 (5.3) -0.002 Immunosuppression 145 (24.10) 62 (20.70) 83 (27.5) 0.158 73 (24.5) 73.4 (24.3) -0.002 Charlson comorbidity index, mean (SD) 3.29 (2.83) 3.19 (2.75) 3.38 (2.91) 0.068 3.33 (0.19) 3.30 (0.16) -0.010 Type of UTI * , n (%): Cystitis 76 (12.6) 45 (15.1) 31 (10.3) -0.047 38.3 (12.9) 38.4 (12.7) -0.001 Pyelonephritis 525 (87.3) 254 (84.9) 271 (89.7) 0.047 259.3 (87.1) 264.2 (87.3) 0.001 Sepsis 90 (14.97) 51 (17.05) 39 (12.91) 0.116 43.2 (14.5) 44.4 (14.7) 0.001 Bacteremia † 62 (10.31) 24 (8.02) 38 (12.58) 0.146 34.4 (11.6) 34.6 (11.4) -0.001 Septic shock 24 (3.99) 10 (3.34) 14 (4.63) 0.066 11.7 (3.9) 12 (4.0) 0.000 Empirical antibiotic regimen * , n (%): Cefazolin 288 (47.92) 159 (53.17) 129 (42.71) -0.104 143.9 (48.3) 145.9 (48.2) -0.001 Ertapenem 171 (28.45) 58 (19.39) 113 (37.41) 0.180 18.9 (6.3) 18.7 (6.2) 0.004 Piperacillin/ tazobactam 39 (6.48) 19 (6.35) 20 (6.62) 0.002 82.5 (27.7) 85.4 (28.2) -0.001 Other 103 (17.13) 63 (21.1) 40 (13.2) -0.078 52.3 (17.6) 52.6 (17.4) -0.002 Treating specialty * , n (%): Internal medicine 419 (69.7) 206 (68.89) 213 (70.53) 0.016 208.3 (70.0) 211.9 (70.0) 0.000 Geriatrics 107 (17.8) 56 (18.73) 51 (16.88) -0.018 53.0 (17.8) 53.8 (17.8) -0.000 Other medical specialties‡ 75 (12.5) 37 (12.40) 38 (12.6) 0.003 36.4 (12.2) 37.0 (12.2) 0.000 Hospital-at-home services, n (%): 104 (17.3) 36 (12.1) 68 (22.5) 0.115 49.9 (16.8) 51.5 (17.0) 0.002 Abbreviations: UTI: Urinary tract infection; SD: Standard deviation; IPTW: Inverse probability of treatment weighting; SMD: Standardized mean difference; HIV: Human immunodeficiency virus. SD: Standard Deviation. SMD: Standardized Mean Difference. * The categories of the variable are mutually exclusive. † The denominator used corresponds to the number of blood cultures collected during the pre-intervention period (70), post-intervention period (82), and the total across both periods (152). ‡ Other medical specialties include: Urology (48), Gynecology (10), Intensive Care Unit (8), Nephrology (8). Immunosuppression includes: Solid tumor (131), Leukemia or lymphoma (16), HIV (2). * The denominator used corresponds to the number of blood cultures collected during the pre-intervention period (70), post-intervention period (82), and the total across both periods (152). † The categories of the variable are mutually exclusive. Microbiological Findings Regarding cultures, the rate of positive urine cultures was significantly higher during the pre-intervention period (86.6%) compared to the post-intervention period (76.2%, p <0.01). No statistically significant differences were observed in blood culture positivity between the two periods (37.1% vs. 47.6%, p =0.19). Escherichia coli remained the most frequently isolated pathogen in urine cultures in both the pre-intervention (73.29%) and post-intervention (73.90%) periods. This was followed by Klebsiella pneumoniae (9.26% pre vs. 9.09% post), Proteus mirabilis (5.40% vs. 9.95%), and Enterococcus faecalis , which showed an increase in frequency during the post-intervention period (from 3.47% to 9.09%) (Figure 1A; Supplementary Table 1). Regarding urine cultures, there was an increase in the proportion of ESBL-producing E. coli isolates, rising from 17.9% in the pre-intervention period to 30.7% in the post-intervention period (Figure 1B, Supplementary Table 2). In blood cultures, Escherichia coli was also the most commonly isolated organism in both study periods, representing 76.9% of isolates in the pre-intervention group and 84.6% in the post-intervention group (Supplementary Figure 3; Supplementary Table 3). In blood cultures, the proportion of ESBL-producing Escherichia coli increased from 10% to 24.2% between the two periods. The presence of carbapenemase-producing organisms (KPC) was identified in 25% of Klebsiella pneumoniae isolates during the pre-intervention period and in 23% during the post-intervention period. In blood cultures, one KPC-producing Klebsiella pneumoniae isolate was identified in each period (Supplementary Figures 3, Supplementary Table 3). Adherence to Antibiotic Management In the post-intervention period, adherence to the recommended antibiotic type increased from 76.9% to 85.4% (p = 0.01); adherence to the appropriate dose rose from 76.9% to 88.1% (p < 0.01); and adherence to the recommended duration improved from 48.8% to 59.9% (p < 0.01). Overall adherence to the flowchart defined as compliance with antibiotic type, dose, and duration significantly improved from 38.1% to 51.6% (p < 0.01) (Table 2). Table 2. Unadjusted Clinical Outcomes of Patients with UTIs Before and After Flowchart Implementation (N = 601). Clinical Outcome Total, n (%) Pre-intervention Period, N=299 Post-intervention Period, N=302 p-value Adherence to antibiotic type,(%) 488 (81.19) 230 (76.92) 258 (85.43) 0.01 Adherence to antibiotic dose, (%) 496 (82.52) 230 (76.92) 266 (88.07) <0.01 Adherence to antibiotic duration, (%) 327 (54.40) 146 (48.82) 181 (59.93) <0.01 Stewardship team intervention *, n (%) 248 (94.29) 122 (96.82) 126 (91.97) 0.15 Total adherence to antimicrobial management, n (%) 270 (44.92) 114 (38.12) 156 (51.65) <0.01 Antibiotic de-escalation, n (%) 137 (22.79) 61 (20.40) 76 (25.16) 0.19 Healthcare professional who performed antibiotic de-escalation, n (%)., n (%) 0.16 Infectious disease specialist 40 (29.19) 22 (36.06) 18 (23.68) Primary physician 97 (70.8) 39 (63.93) 58 (76.31) Switch to oral therapy, n (%) 265 (44.09) 148 (49.49) 117 (38.74) 0.01 Healthcare professional who performed the switch to oral therapy, n (%). 0.29 Infectious disease specialist 10 (5.32) 8 (7.21) 2 (2.60) Primary physician 178 (94.68) 103 (92.80) 75 (97.4) Median time to switch to oral therapy (days, IQR) 3.00 (3.00-5.00) 3.50 (3.00-5.00) 3.00 (2.00-5.00) 0.30 Duration of antibiotic treatment in days, median (IQR) 9.00(7.00-13.00) 9.00 (7.00-12.00) 9.00(7.00-13.00) 0.32 Admission to the intensive care unit, n (%), n (%) 18 (3.00) 6 (2.01) 12 (3.97) 0.24 Median hospital stay (days, IQR) 5.00 (3.00-10.00) 5.00 (3.00-9.00) 6.00 (3.00-10.00) 0.13 Clinical response, n (%) 536 (89.18) 273 (91.30) 263 (87.08) 0.12 Relapse-related readmission, n (%) 53 (8.81) 22 (7.35) 31 (10.26) 0.26 In-hospital mortality, n (%) 3 (0.49) 1 (0.33) 2 (0.66) 1.00 IQR: Interquartile Range De-escalation, Oral Switch, and Clinical Outcomes Adherence to the antimicrobial stewardship (ASP) note was high in both periods, reaching 94.3% of patients overall, with no statistically significant differences between periods (p = 0.15). The proportion of patients undergoing antibiotic de-escalation increased from 20.4% to 25.2% in the post-intervention period, although this difference was not statistically significant (p = 0.19). Regarding the healthcare professional responsible, the attending physician performed 70.8% of the de-escalations, while infectious disease specialists accounted for 29.2%. The proportion of patients switched to oral therapy was lower in the post-intervention group (49.5% vs. 38.7%, p = 0.01), with the switch predominantly performed by the attending physician (94.7% of cases). The median time to oral switch remained at 3 days in both periods (p = 0.30) (Supplementary Figure 4). The total duration of antibiotic treatment did not differ between periods (median of 9 days, p = 0.32) (Table 2). Clinical Outcomes Clinical response was high in both groups (89.2% overall), with no clinically significant differences observed (91.3% pre-intervention vs. 87.1% post-intervention; p = 0.12). Similarly, no significant differences were found in the rate of UTI relapse-related readmissions (7.4% vs. 10.3%, p = 0.26), length of hospital stay (median of 5 vs. 6 days, p = 0.13) (Supplementary Figure 5), or in-hospital mortality, which remained low across both periods (0.3% vs. 0.7%, p = 1.00) (Table 2). Bivariate and Multivariate Analysis of Factors Associated with Full Adherence to Clinical Flowcharts Primary Outcome Full adherence to the clinical flowchart was significantly higher during the post-intervention period (aOR: 1.61; 95% CI: 1.15–2.24; p = 0.005). Female sex (aOR: 2.40; 95% CI: 1.66–3.49; p < 0.001) and older age (aOR: 1.01; 95% CI: 1.00–1.02; p = 0.016) were also significantly associated with higher adherence. Secondary Adherence Outcomes The post-intervention period was associated with increased adherence to both antibiotic dosing (aOR: 1.58; 95% CI: 1.02–2.45; p = 0.045) and treatment duration (aOR: 1.55; 95% CI: 1.12–2.16; p < 0.01). Female sex was also associated with better adherence to treatment duration (aOR: 2.12; 95% CI: 1.48–3.06; p < 0.001), as was management through hospital-at-home care (aOR: 1.59; 95% CI: 1.01–2.54; p = 0.047). The presence of bacteremia was associated with lower adherence to the empiric antibiotic type (aOR: 0.52; 95% CI: 0.28–0.97; p = 0.035). Clinical Outcomes No significant associations were observed between the post-intervention period and clinical response (aOR: 0.65; 95% CI: 0.37–1.10; p = 0.108), hospital readmission (aOR: 1.39; 95% CI: 0.78–2.50; p = 0.264), antibiotic de-escalation (aOR: 1.01; 95% CI: 0.69–1.50; p = 0.942), or switch to oral therapy. The presence of bacteremia and hospital-at-home care were associated with a lower likelihood of switching to oral therapy. Older age, a diagnosis of pyelonephritis, the presence of bacteremia, and hospital-at-home management were all associated with longer hospital stays. No significant differences were observed regarding the post-intervention period (Table 3). Table 3. Adjusted Clinical Outcomes in Patients with Urinary Tract Infection at San Ignacio Hospital (N = 601) . Clinical Outcome Variables Model Estimator 95% CI P-value Primary outcome aOR Full adherence to empirical therapy Post-intervention period Age Female sex Immunosuppression Pyelonephritis Bacteremia Diabetes mellitus type 2 hospital-at-home Weighted logistic regression (WLR) 1.61 1.01 2.40 1.02 1.13 0.66 1.34 1.36 1.15-2.24 1.00-1.02 1.66-3.49 0.68-1.52 0.68-1.90 0.38-1.13 0.87-2.07 0.86-1.52 0.005 0.016 <0.001 0.927 0.633 0.133 0.181 0.182 Secondary outcomes aOR Adherence to antibiotic type Post-intervention period Age Female sex Immunosuppression Pyelonephritis Bacteremia Diabetes mellitus type 2 hospital-at-home WLR 1.26 1.00 1.52 1.41 0.95 0.52 1.51 0.90 0.83-1.92 0.99-1.01 0.96-2.39 0.85-2.43 0.47-1.80 0.28-0.97 0.86-2.75 0.52-1.62 0.287 0.631 0.071 0.198 0.888 0.035 0.165 0.718 aOR Adherence to antibiotic dose Post-intervention period Age Female sex Immunosuppression Pyelonephritis Bacteremia Diabetes mellitus type 2 hospital-at-home WLR 1.58 1.00 1.45 1.26 0.90 0.64 1.41 1.13 1.02-2.45 0.99-1.01 0.91-2.31 0.75-2.19 0.43-1.73 0.34-1.27 0.79-2.62 0.63-2.14 0.045 0.776 0.120 0.391 0.758 0.180 0.258 0.688 aOR Adherence to treatment duration Post-intervention period Age Female sex Immunosuppression Pyelonephritis Bacteremia Diabetes mellitus type 2 hospital-at-home WLR 1.55 1.00 2.12 1.15 1.45 0.85 1.21 1.59 1.12-2.16 0.99-1.01 1.48-3.06 0.78-1.71 0.88-2.41 0.50-1.45 0.79-1.87 1.01-2.54 <0.001 0.625 <0.001 0.485 0.148 0.552 0.381 0.047 aOR Clinical response Post-intervention period Age Female sex Immunosuppression Pyelonephritis Bacteremia Diabetes mellitus type 2 hospital-at-home WLR 0.65 1.00 1.05 1.44 0.91 0.68 1.02 0.97 0.37-1.10 0.98-1.01 0.59-1.85 0.76-2.93 0.37-2.00 0.33-1.52 0.53-2.06 0.50-2.07 0.108 0.586 0.875 0.289 0.832 0.317 0.965 0.941 aOR Hospital readmission Post-intervention period Age Female sex Immunosuppression Pyelonephritis Bacteremia Diabetes mellitus type 2 hospital-at-home WLR 1.39 1.00 0.90 0.69 1.01 1.15 0.60 0.90 0.78-2.50 0.99-1.02 0.49-1.71 0.31-1.39 0.44-2.68 0.42-2.70 0.24-1.31 0.37-1.94 0.264 0.745 0.751 0.326 0.977 0.760 0.229 0.805 aOR Antibiotic de-escalation Post-intervention period Age Female sex Immunosuppression Pyelonephritis Bacteremia Diabetes mellitus type 2 hospital-at-home WLR 1.01 1.01 0.92 1.94 0.83 4.05 1.09 1.00 0.69-1.50 1.00-1.02 0.61-1.40 1.26-3.00 0.47-1.50 2.34-7.04 0.67-1.76 0.59-1.66 0.942 0.104 0.704 0.002 0.516 <0.001 0.722 0.997 aOR Switch to oral therapy Post-intervention period Age Female sex Immunosuppression Pyelonephritis Bacteremia Diabetes mellitus type 2 hospital-at-home WLR 0.91 0.99 2.21 0.72 0.93 0.26 1.41 0.25 0.64-1.29 0.98-1.00 1.51-3.24 0.47-1.10 0.55-1.57 0.13-0.48 0.89-2.24 0.14-0.42 0.598 0.167 <0.001 0.130 0.790 <0.001 0.140 <0.001 β coefficient Length of hospital stay (days) Post-intervention period Age Female sex Immunosuppression Pyelonephritis Bacteremia Diabetes mellitus type 2 hospital-at-home Weighted linear regression 0.034 0.037 -0.635 1.535 1.819 4.362 -0.168 6.002 -0.79-0.86 0.02-0.06 -1.55-0.28 0.42-2.65 0.97-2.66 2.36-6.36 -1.31-0.97 4.41-7.59 0.934 <0.001 0.174 0.006 <0.001 <0.001 0.77 <0.001 aOR: Adjusted Odds Ratio. CI: Confidence Interval. Discussion The rise of antimicrobial resistance in urinary tract infections (UTIs), particularly in middle-income countries such as Colombia, underscores the need to optimize empirical treatment strategies ¹-⁵ ⁹ . The implementation of clinical flowcharts has been associated with improved adherence to guidelines and favorable clinical outcomes ¹⁴ ¹⁷ ¹⁸ ²⁰ . However, few studies have evaluated their impact in hospital settings in Latin America. Our study assessed the effect of clinical flowcharts on empirical treatment adherence, clinical impact, and antimicrobial stewardship program outcomes (ASPs). In our cohort, the sociodemographic and clinical characteristics of hospitalized UTI patients were comparable across the pre- and post-intervention periods. The mean age was approximately 59 years in both groups, with a slight predominance of females, accounting for over 60% of the population. Pyelonephritis was the most frequent clinical presentation (87.3%), followed by cystitis. Regarding comorbidities, there was a high burden of chronic conditions, particularly type 2 diabetes mellitus (19.1%) and immunosuppression (24.1%), mostly involving patients with solid tumors, leukemia, or lymphoma. The Charlson comorbidity index was similar between groups (mean 3.29), reflecting a clinically complex population. After applying inverse probability of treatment weighting (IPTW), appropriate balance between groups was achieved across all baseline covariates (standardized mean differences < 0.01), supporting the validity of subsequent comparative analyses. Unlike prior studies focusing on younger, healthier populations ¹⁷ , our cohort included a high proportion of complicated UTIs and relevant clinical conditions. This aligns with recent studies evaluating ASP interventions in older adults ¹⁸ , supporting the applicability of clinical flowcharts in heterogeneous, clinically complex hospital populations. The increase in ertapenem use observed during the post-intervention period (19.3% vs. 37.4%) reflects empirical decision-making aligned with institutional flowcharts, which recommend this antibiotic for patients at risk for ESBL-producing Enterobacterales. This finding is further supported by the higher frequency of ESBL isolates in the post-intervention period, particularly in Escherichia coli (17.9% vs. 30.7%) and Klebsiella pneumoniae (8.3% vs. 23.8%). Notably, type II carbapenems were used infrequently despite ESBL production, aligning with evidence linking ertapenem to lower selection pressure for carbapenem-resistant Pseudomonas aeruginosa and carbapenemase-producing organisms. Literature supports the empirical use of ertapenem for ESBL infections, demonstrating similar effectiveness and 30-day mortality outcomes compared to other carbapenems ⁷ ²¹ ²² . This study showed that implementing clinical flowcharts significantly improved key indicators of therapeutic adherence. In the IPTW-adjusted multivariable model, the post-intervention period was significantly associated with improved overall adherence (aOR: 1.61; 95% CI: 1.15–2.24; p = 0.005), as well as adherence to dosing (aOR: 1.58; 95% CI: 1.02–2.45; p = 0.045) and treatment duration (aOR: 1.55; 95% CI: 1.12–2.16; p < 0.001). These findings support the use of flowcharts as effective tools for standardizing antimicrobial prescribing, enhancing treatment quality, and facilitating the implementation of institutional guidelines. Our findings are consistent with recent international studies demonstrating that structured interventions improve antimicrobial use ¹⁹ ²³-²⁴ . Additionally, evidence shows that multifaceted educational interventions can improve prescribing practices without compromising patient safety ²³ ²⁴ . These results, aligned with our study, reinforce the value of clinical flowcharts as safe, effective tools in complex settings with high resistance burdens ²⁵-²⁶ . In our cohort, female sex was independently associated with greater adherence to empirical antimicrobial management. This may be partially explained by microbiological and clinical factors, as women especially those with uncomplicated pyelonephritis more frequently present with infections due to Escherichia coli , a pathogen with more predictable susceptibility patterns. This facilitates appropriate empirical selection and spectrum adjustment ⁶ . However, evidence linking female sex with higher adherence or de-escalation rates remains insufficient. This finding should be interpreted cautiously and warrants further studies to assess whether this association persists after adjusting for clinical severity, comorbidities, and UTI type. Despite improved adherence to treatment duration, the switch to oral therapy was not significantly higher in the post-intervention period. Furthermore, bacteremia and hospital-at-home care were associated with lower rates of switching to oral therapy in the IPTW-adjusted model. This discrepancy may reflect limited oral treatment options for ESBL-producing infections, as supported by Rodríguez Cervera et al ²⁵ . who reported reluctance to switch due to perceived clinical severity and resistant pathogens. Additionally, resistance to fluoroquinolones and trimethoprim-sulfamethoxazole (commonly used oral agents) can be co-transferred with ESBL genes on the same plasmid ¹⁰ ²⁶ , limiting switch options. Although the proportion of patients undergoing antibiotic de-escalation increased post-intervention (20.4% vs. 25.2%), the difference was not statistically significant (p = 0.19), and the intervention period was not significantly associated with de-escalation in the IPTW-adjusted model. However, immunosuppression and bacteremia were significantly associated with this outcome. This mirrors findings by Khasawneh et al., who reported a 47.7% rate of missed de-escalation opportunities despite clinical and microbiological criteria, with fluoroquinolone resistance and non- E. coli pathogens as main barriers ²⁷ . In our study, etiological diversity and resistance patterns may have had a similar effect, highlighting the need to strengthen institutional ASPs auditing and active support. Although bacteremia or immunosuppression might traditionally justify prolonged broad-spectrum therapy, evidence suggests that, when microbiological documentation and clinical response are available, de-escalation is feasible and safe even in immunocompromised patients with Enterobacterales bacteremia without increased mortality or relapse risk ²⁷-³⁰ . Thus, higher de-escalation rates in these subgroups may reflect prudent, microbiology-driven clinical practice rather than omission of caution. From a therapeutic perspective, a high clinical response rate (89.2%) was observed overall, with no significant difference associated with the intervention (aOR: 1.11; 95% CI: 0.66–1.89; p = 0.682). Similarly, the post-intervention period was not associated with higher 30-day readmission risk (aOR: 0.86; 95% CI: 0.42–1.75; p = 0.676), and overall mortality remained low. These findings suggest that the intervention was safe and did not compromise clinical effectiveness. This aligns with previous reports by Hartman, Zalmanovich, and Arnold et al., showing favorable outcomes after ASP-associated interventions in UTI management ¹⁸ ²³-²⁴ . Hospital length of stay (LOS) was not significantly affected by the intervention (β = 0.034; 95% CI: -0.79 to 0.86; p = 0.934). However, LOS was significantly longer among patients with bacteremia (β = 4.36; p < 0.001) and those receiving hospital-at-home care (β = 6.00; p < 0.001). These findings are consistent with literature showing that antimicrobial optimization strategies do not prolong hospitalization or increase readmission rates, even in patients with multiple comorbidities ⁷ ¹⁸ . They also align with prior studies documenting longer hospital stays in patients with severe infections or bacteremia ³¹-³³ . This study was conducted in a high-complexity academic hospital, enhancing the relevance and potential applicability of findings to similar clinical settings. The ambispective quasi-experimental design facilitated robust comparisons between pre- and post-intervention periods through randomized sampling and standardized data collection. A rigorous methodological approach was employed, including bivariate and multivariable regression analyses with inverse probability of treatment weighting (IPTW) to minimize confounding and estimate the average treatment effect. The comprehensive assessment of antimicrobial adherence, clinical response, microbiological profiles, and hospital outcomes provides a holistic evaluation of the clinical flowchart’s impact. Nevertheless, limitations typical of before-and-after quasi-experimental designs must be acknowledged, particularly the inability to fully control for unmeasured confounders such as fluctuations in staffing, antimicrobial availability, or healthcare system pressures. The application of IPTW adjustment helped mitigate these limitations and supported internal validity. Although the sample size was sufficient to detect differences in adherence, the study was not powered to assess infrequent outcomes such as mortality or readmission. Furthermore, individual adherence to clinical flowcharts was not directly measured. However, acceptance of antimicrobial stewardship program recommendations remained high across both periods (96.8% pre vs. 91.9% post; p = 0.15), aligning with previous successful implementations of multifaceted ASP strategies⁷ ³⁴. Notably, most de-escalation and oral switch decisions were made by the treating physicians (70.8% and 94.7%, respectively), reflecting strong clinician engagement in stewardship practices. Conclusions Institutional flowcharts for empirical UTI management significantly improved adherence to antibiotic dosing and duration in a high-complexity hospital, without compromising clinical outcomes—even amid high resistance. These findings support the integration of flowcharts into ASPs. Future efforts should focus on strengthening de-escalation and oral switch practices and evaluating applicability across diverse clinical settings. Declarations Ethical Approval and Consent to Participate This study was approved by the Institutional Research and Ethics Committee (CIEI) of the Faculty of Medicine, Pontificia Universidad Javeriana, and Hospital Universitario San Ignacio (Approval Act 06/2025, code 2025/048). The requirement for informed consent was waived due to the retrospective nature of the study and the use of anonymized data. Consent for Publication Not applicable. Availability of data and materials: The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Conflict of Interest Statement The authors declare that they have no conflicts of interest related to this work. Funding Statement This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions S.L. Valderrama, A. Rivera Tenorio, C.R. Diaz Brochero, S.M. Gualtero Jiménez, and S.A. Mackenzie participated in the study design, data collection, results analysis, and manuscript writing. C.A. Bonilla, N. Manrique Marín, M. P. Lampis, L.V. Bejarano Mora, and C.A. Bejarano Mora contributed to the study design and data collection. All authors reviewed and approved the final version of the manuscript. Acknowledgments The authors thank the Antimicrobial Stewardship Program team at Hospital Universitario San Ignacio for their support in the implementation of the clinical flowcharts and their participation in the therapeutic process audit. References Foxman B. The epidemiology of urinary tract infection. Nat Rev Urol. 2010;7(12):653–60. 10.1038/nrurol.2010.190 . Medina M, Castillo-Pino E. 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Infect Control Hosp Epidemiol. 2020;41(10):1225–7. 10.1017/ice.2020.216 . Additional Declarations No competing interests reported. Supplementary Files supplement.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviewers invited by journal 31 Oct, 2025 Editor invited by journal 07 Oct, 2025 Editor assigned by journal 05 Oct, 2025 Submission checks completed at journal 05 Oct, 2025 First submitted to journal 29 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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20:09:03","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167995,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7746024/v1/b189ab35b5760a65733b116c.html"},{"id":95680760,"identity":"d3b6feec-3b75-4b6c-914d-6c97031ae688","added_by":"auto","created_at":"2025-11-11 20:09:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39100,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTypes of microorganisms (A) and resistance patterns (B) in blood cultures by study period.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The denominator used was the total number of positive urine cultures for each analyzed period: Pre-intervention (259); Post-intervention (231).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7746024/v1/8e6e67edcf98b4079dc652aa.png"},{"id":95818798,"identity":"966b1659-a740-49e7-a904-07c4b2c0c86f","added_by":"auto","created_at":"2025-11-13 10:33:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1557901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7746024/v1/1d2ceaf8-24dc-4eed-8c36-4622f24f27a0.pdf"},{"id":95680766,"identity":"b4c63f86-6400-4f2a-89c5-5c511404d842","added_by":"auto","created_at":"2025-11-11 20:09:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1012460,"visible":true,"origin":"","legend":"","description":"","filename":"supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7746024/v1/a7052680e05c238bb8d9323a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Clinical Flowcharts on Therapeutic Adherence, Treatment Duration, and Clinical Outcomes in Urinary Tract Infections: A Quasi-Experimental Study in a Colombian University Hospital","fulltext":[{"header":"Background","content":"\u003cp\u003eUrinary tract infections (UTIs) are a major cause of hospitalization and antimicrobial use globally, with over 404\u0026nbsp;million cases reported in 2019 and a rising incidence in Latin America and South America\u003csup\u003e\u0026sup1;-\u0026sup2;\u003c/sup\u003e. Although most UTIs are treatable with antibiotics, increasing antimicrobial resistance has become a critical public health issue worldwide\u003csup\u003e⁴ ⁵\u003c/sup\u003e. In response, various international and national guidelines have been developed to promote rational UTI management and mitigate resistance development \u003csup\u003e⁶- ⁸\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eIn Colombia, the situation is particularly concerning due to high resistance rates. \u003cem\u003eEscherichia coli\u003c/em\u003e the most frequent uropathogen exhibits resistance rates of up to 49.3% to ciprofloxacin and trimethoprim-sulfamethoxazole, while ESBL production is present in 44% of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and 27% of \u003cem\u003eE. coli\u003c/em\u003e isolated from urine\u003csup\u003e⁵ ⁹ \u0026sup1;⁰\u003c/sup\u003e. These resistance patterns challenge the effectiveness of empirical therapy and are associated with higher risks of treatment failure, inappropriate use of broad-spectrum antibiotics, increased healthcare costs, and prolonged hospital stays\u003csup\u003e\u0026sup1;\u0026sup1;-\u0026sup1;\u0026sup3;\u003c/sup\u003e. Antimicrobial stewardship programs (ASPs), including the use of structured clinical tools such as treatment flowcharts, have demonstrated improved guideline adherence and reduced inappropriate prescribing\u003csup\u003e\u0026sup1;⁴-\u0026sup1;⁸\u003c/sup\u003e. However, evidence from Latin American hospital settings remains scarce. This study aimed to assess the impact of implementing locally adapted clinical flowcharts on adherence to empirical antibiotic management for community-acquired UTIs in a high-complexity university hospital in Bogot\u0026aacute;, Colombia. Secondary outcomes included clinical response, hospital length of stay, antibiotic de-escalation, transition to oral therapy, and relapse-related readmissions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA quasi-experimental, ambispective before-and-after study was conducted at Hospital Universitario San Ignacio (HUSI), a 270-bed tertiary academic hospital in Bogot\u0026aacute;, Colombia. The pre-intervention period extended from January to November 2023, followed by a two-month implementation phase (December 2023 to February 2024), and a post-intervention period from March 2024 to January 2025. The study included hospitalized adults (\u0026ge;\u0026thinsp;18 years) diagnosed with urinary tract infection (UTI) (ICD-10 code N39.0) who received empirical antimicrobial therapy. Patients with nosocomial UTIs, early in-hospital mortality, or transfer within 48 hours of admission were excluded.\u003c/p\u003e\u003cp\u003e The intervention consisted of clinical flowcharts for UTI management, developed by the institutional antimicrobial stewardship team comprising infectious disease physicians, pharmacists, and nurses based on national and international guidelines, local antimicrobial susceptibility data, and drug availability. These flowcharts provided recommendations for empirical antibiotic selection, dosing, treatment duration, and criteria for de-escalation or switch to oral therapy. They were disseminated in December 2023 through the institutional platform (ALMERA) and reinforced via educational sessions (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eVariables\u003c/h2\u003e\u003cp\u003eAdherence to empirical antimicrobial therapy was defined as fulfillment of all three criteria specified in the institutional flowcharts: appropriate antibiotic selection, correct dosing, and recommended treatment duration. Empirical antibiotic adherence referred to administration of the recommended regimen for the specific clinical syndrome. Dosing adherence involved prescribing the correct daily dose per institutional guidelines, while duration adherence was defined as completing the protocol-indicated course. Treatment durations varied by syndrome: 7 days for uncomplicated pyelonephritis, 5 days for uncomplicated cystitis, and 5 days (women) or 7 days (men) for complicated cystitis. In complicated pyelonephritis, treatment was extended to 14 days in cases with clinical modifiers (e.g., male sex, immunosuppression, structural abnormalities).\u003c/p\u003e\u003cp\u003eClinical response was defined as sustained improvement without hospital readmission for UTI relapse within 28 days of diagnosis, based on RECAPTURE criteria\u003csup\u003e\u0026sup1;⁹\u003c/sup\u003e. Relapse-related readmission referred to recurrence of UTI symptoms requiring hospitalization within 28 days of resolution. De-escalation was defined as spectrum narrowing based on microbiological data. Oral switch was defined as transition from intravenous to oral antibiotics after clinical stabilization.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample Size Calculation\u003c/h3\u003e\n\u003cp\u003eBased on a pilot review of 50 medical records, we estimated an increase in adherence from 80% to 90% after the intervention. Assuming a 95% confidence level and 80% power, a minimum of 202 patients per group (pre- and post-intervention) was required. The final sample included 601 patients. Cases were randomly selected from all 1,216 patients diagnosed with UTI at HUSI in 2023, evenly distributed across both study periods. Data were extracted from electronic medical records using a standardized Excel 2019 template.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe dataset was reviewed to identify and correct outliers, transcription errors, and missing data using institutional records. Descriptive statistics were used to summarize demographic and clinical characteristics. Normality of continuous variables was assessed via the Kolmogorov\u0026ndash;Smirnov test. Continuous variables were reported as measures of central tendency and dispersion; categorical variables were expressed as absolute and relative frequencies. Microbiological findings, including pathogen distribution and resistance patterns, were graphically represented.\u003c/p\u003e\u003cp\u003eThe primary exposure was the study period (pre- vs. post-intervention). The primary outcome was full adherence to the empirical antimicrobial algorithm, defined as appropriate antibiotic choice, correct dosing, and recommended treatment duration. Secondary outcomes included each adherence component, clinical response, 28-day relapse-related readmission, de-escalation, switch to oral therapy, and hospital length of stay.\u003c/p\u003e\u003cp\u003eCovariates included age, sex, Charlson comorbidity index, admitting service, urosepsis, septic shock, bacteremia, heart failure, type 2 diabetes, chronic kidney disease, dementia, immunosuppression (hematologic malignancy, solid organ transplant, or AIDS), empirical antimicrobial regimen, and UTI classification (pyelonephritis vs. cystitis).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePropensity Score Weighting (IPTW)\u003c/h3\u003e\n\u003cp\u003eTo assess the association between the intervention period and complete adherence to the empirical antibiotic algorithm while minimizing confounding, inverse probability of treatment weighting (IPTW) was applied. Propensity scores were estimated via logistic regression, with exposure (pre- vs. post-intervention) as the dependent variable and all relevant covariates as predictors. Stabilized weights were used to estimate the average treatment effect (ATE), implemented through the \u003cem\u003eWeightIt\u003c/em\u003e package in R. Covariate balance before and after weighting was evaluated using standardized mean differences (SMD), with values\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicating adequate balance. Love plots generated with the \u003cem\u003ecobalt\u003c/em\u003e package were used for visual assessment.\u003c/p\u003e\n\u003ch3\u003eModels for Binary Outcomes\u003c/h3\u003e\n\u003cp\u003eWe used IPTW-weighted logistic regression models (R \u003cem\u003esurvey\u003c/em\u003e package) to evaluate associations between the post-intervention period and binary outcomes (overall adherence, individual components, clinical response, de-escalation, oral switch, and readmission).\u003c/p\u003e\u003cp\u003eModels were adjusted for clinically relevant covariates or those with residual imbalance: diabetes, sex, age, pyelonephritis, extended hospital stay, immunosuppression, and bacteremia. We reported adjusted odds ratios (aOR) with 95% confidence intervals (CI), with significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of Continuous Outcomes\u003c/h2\u003e\u003cp\u003eFor continuous outcomes such as hospital length of stay and time to oral switch we performed Kaplan\u0026ndash;Meier survival analyses and compared groups using the log-rank test. We used IPTW-weighted linear regression to evaluate the association between full adherence and total hospital stay (in days). Clinically relevant covariates were included. Results were reported as beta coefficients (β) with 95% CIs. All analyses were conducted in R version 4.4.1.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 601 hospitalized patients diagnosed with urinary tract infection (UTI) were analyzed, distributed into two groups: 299 during the pre-intervention period (January\u0026ndash;November 2023) and 302 during the post-intervention period (March 2024\u0026ndash;January 2025).\u003c/p\u003e\n\u003ch3\u003eClinical and Demographic Characteristics\u003c/h3\u003e\n\u003cp\u003eThe mean age was comparable between groups (58.98 vs. 58.35 years), as was the proportion of female patients (63.21% vs. 61.92%). After applying inverse probability of treatment weighting (IPTW), excellent balance between groups was achieved, with standardized mean differences (SMD) \u0026lt;0.01 across all key variables, indicating strong comparability (see Supplementary Figure 2).\u003c/p\u003e\n\u003cp\u003eThe most prevalent comorbidities were type 2 diabetes mellitus (19.1%) and immunosuppression (24.1%). While a higher prevalence of diabetes and immunosuppression was initially observed in the post-intervention group, this imbalance was substantially corrected following IPTW adjustment. Other relevant conditions including chronic kidney disease, dementia, heart failure, and sepsis were similarly distributed across groups.\u003c/p\u003e\n\u003cp\u003eThe most common clinical presentation was pyelonephritis (87.3%), followed by cystitis (12.6%). Bacteremia was more frequently observed in the post-intervention group (12.6% vs. 8.0%), but this difference was also balanced after weighting. Regarding empirical treatment, the most commonly prescribed antibiotic was cefazolin (47.9%), followed by ertapenem (28.4%). Notably, the use of ertapenem as initial empirical therapy was more frequent in the post-intervention group (34.4% vs. 22.4%). Additionally, there was a marked increase in the use of hospital-at-home services during the post-intervention period (22.5% vs. 12.1%).\u003c/p\u003e\n\u003cp\u003eInternal medicine was the primary specialty managing UTI cases, accounting for 69.7% of the cohort overall (68.8% in the pre-intervention group and 70.5% in the post-intervention group). Other specialties such as geriatrics, urology, and medical subspecialties represented smaller and similarly distributed proportions between the two study periods. (See Table 1 and Figure 1.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of Patients with Urinary Tract Infection Before and After Flowchart Use at San Ignacio Hospital\u0026nbsp;(N = 601).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"639\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePre-intervention Period (n = 299)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePost-intervention Period (n = 302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eSMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003ePre-intervention (IPTW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003ePost-interven\u003c/p\u003e\n \u003cp\u003etion (IPTW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eSMD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e58.66 (20.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e58.98 (20.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e58.35 (19.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e58.72 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e58.71 (1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale sex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e376 (62.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e189 (63.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e187 (61.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e372 (61.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e378 (62.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities, n (%):\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;Heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e33 (5.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e20 (6.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13 (4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e16.3 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e15.8 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;Type 2 diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e115 (19.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e68 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e47 (15.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e60 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e60.2 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e32 (5.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13 (4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e19 (6.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e14.9 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e15.5 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e33 (5.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e20 (6.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13 (4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e16.5 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e16.1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e145 (24.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e62 (20.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e83 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e73 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e73.4 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharlson comorbidity index, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.29 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.19 (2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.38 (2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e3.33 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e3.30 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of UTI\u003c/strong\u003e \u003cem\u003e*\u003c/em\u003e\u003cstrong\u003e, n (%):\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;Cystitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e76 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e45 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e31 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e38.3 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e38.4 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003ePyelonephritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e525 (87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e254 (84.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e271 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e259.3 (87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e264.2 (87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e90 (14.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51 (17.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e39 (12.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e43.2 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e44.4 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacteremia\u003c/em\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e62 (10.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e24 (8.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e38 (12.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e34.4 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e34.6 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eSeptic shock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e24 (3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10 (3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e14 (4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e11.7 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e12 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eEmpirical antibiotic regimen\u0026nbsp;\u003cem\u003e*\u003c/em\u003e, n (%):\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;Cefazolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e288 (47.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e159 (53.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e129 (42.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e143.9 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e145.9 (48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eErtapenem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e171 (28.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e58 (19.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e113 (37.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e18.9 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e18.7 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003ePiperacillin/\u003c/p\u003e\n \u003cp\u003etazobactam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e39 (6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e19 (6.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e20 (6.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e82.5 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e85.4 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e103 (17.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e63 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e40 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e52.3 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e52.6 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreating specialty\u003c/strong\u003e\u003cem\u003e*\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e, n (%):\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eInternal medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e419 (69.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e206 (68.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e213 (70.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e208.3 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e211.9 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eGeriatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e107 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e56 (18.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51 (16.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e53.0 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e53.8 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;Other medical specialties\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e75 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e37 (12.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e38 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e36.4 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e37.0 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital-at-home services, n (%):\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e104 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e36 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e68 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e49.9 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e51.5 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUTI: Urinary tract infection; SD: Standard deviation; IPTW: Inverse probability of treatment weighting; SMD: Standardized mean difference; HIV: Human immunodeficiency virus. SD: Standard Deviation. SMD: Standardized Mean Difference.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*\u003c/em\u003e\u003cem\u003eThe categories of the variable are mutually exclusive.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003cem\u003eThe denominator used corresponds to the number of blood cultures collected during the pre-intervention period (70), post-intervention period (82), and the total across both periods (152).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026Dagger;\u003cem\u003eOther medical specialties include: Urology (48), Gynecology (10), Intensive Care Unit (8), Nephrology (8).\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eImmunosuppression includes: Solid tumor (131), Leukemia or lymphoma (16), HIV (2).\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\u003c/em\u003e\u003cem\u003e*\u003c/em\u003e\u003cem\u003eThe denominator used corresponds to the number of blood cultures collected during the pre-intervention period (70), post-intervention period (82), and the total across both periods (152).\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\u003c/em\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003cem\u003e\u0026nbsp;The categories of the variable are mutually exclusive.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicrobiological Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding cultures, the rate of positive urine cultures was significantly higher during the pre-intervention period (86.6%) compared to the post-intervention period (76.2%, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01). No statistically significant differences were observed in blood culture positivity between the two periods (37.1% vs. 47.6%, \u003cem\u003ep\u003c/em\u003e=0.19).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e remained the most frequently isolated pathogen in urine cultures in both the pre-intervention (73.29%) and post-intervention (73.90%) periods. This was followed by \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (9.26% pre vs. 9.09% post), \u003cem\u003eProteus mirabilis\u003c/em\u003e (5.40% vs. 9.95%), and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, which showed an increase in frequency during the post-intervention period (from 3.47% to 9.09%) (Figure 1A; Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003eRegarding urine cultures, there was an increase in the proportion of ESBL-producing \u003cem\u003eE. coli\u003c/em\u003e isolates, rising from 17.9% in the pre-intervention period to 30.7% in the post-intervention period (Figure 1B, Supplementary Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn blood cultures, \u003cem\u003eEscherichia coli\u003c/em\u003e was also the most commonly isolated organism in both study periods, representing 76.9% of isolates in the pre-intervention group and 84.6% in the post-intervention group (Supplementary Figure 3; Supplementary Table 3). In blood cultures, the proportion of ESBL-producing \u003cem\u003eEscherichia coli\u003c/em\u003e increased from 10% to 24.2% between the two periods. The presence of carbapenemase-producing organisms (KPC) was identified in 25% of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e isolates during the pre-intervention period and in 23% during the post-intervention period. In blood cultures, one KPC-producing \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e isolate was identified in each period (Supplementary Figures 3, Supplementary Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdherence to Antibiotic Management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the post-intervention period, adherence to the recommended antibiotic type increased from 76.9% to 85.4% (p = 0.01); adherence to the appropriate dose rose from 76.9% to 88.1% (p \u0026lt; 0.01); and adherence to the recommended duration improved from 48.8% to 59.9% (p \u0026lt; 0.01). Overall adherence to the flowchart defined as compliance with antibiotic type, dose, and duration significantly improved from 38.1% to 51.6% (p \u0026lt; 0.01) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Unadjusted Clinical Outcomes of Patients with UTIs Before and After Flowchart Implementation (N = 601).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePre-intervention Period,\u003cbr\u003e\u0026nbsp;N=299\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePost-intervention Period,\u003cbr\u003e\u0026nbsp;N=302\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdherence to antibiotic type,(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e488 (81.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e230 (76.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e258 (85.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdherence to antibiotic dose, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e496 (82.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e230 (76.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e266 (88.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdherence to antibiotic duration, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e327 (54.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e146 (48.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e181 (59.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStewardship team intervention *,\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e248 (94.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e122 (96.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e126 (91.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal adherence to antimicrobial management, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e270 (44.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114 (38.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156 (51.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAntibiotic de-escalation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e137 (22.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61 (20.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76 (25.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthcare professional who performed antibiotic de-escalation, n (%)., n (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInfectious disease specialist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (29.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (36.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (23.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary physician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (63.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (76.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSwitch to oral therapy, n (%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e265 (44.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e148 (49.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117 (38.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eHealthcare professional who performed the switch to oral therapy, n (%).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInfectious disease specialist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (5.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (7.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary physician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e178 (94.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e103 (92.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedian time to switch to oral therapy (days, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.00 (3.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.50 (3.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.00 (2.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration of antibiotic treatment in days, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.00(7.00-13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.00 (7.00-12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.00(7.00-13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdmission to the intensive care unit, n (%), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (3.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedian hospital stay (days, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.00 (3.00-10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.00 (3.00-9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.00 (3.00-10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eClinical response, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e536 (89.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e273 (91.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e263 (87.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRelapse-related readmission, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53 (8.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (7.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (10.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIn-hospital mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIQR: Interquartile Range\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDe-escalation, Oral Switch, and Clinical Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdherence to the antimicrobial stewardship (ASP) note was high in both periods, reaching 94.3% of patients overall, with no statistically significant differences between periods (p = 0.15). The proportion of patients undergoing antibiotic de-escalation increased from 20.4% to 25.2% in the post-intervention period, although this difference was not statistically significant (p = 0.19). Regarding the healthcare professional responsible, the attending physician performed 70.8% of the de-escalations, while infectious disease specialists accounted for 29.2%. The proportion of patients switched to oral therapy was lower in the post-intervention group (49.5% vs. 38.7%, p = 0.01), with the switch predominantly performed by the attending physician (94.7% of cases). The median time to oral switch remained at 3 days in both periods (p = 0.30) (Supplementary Figure 4).\u003cbr\u003e\u0026nbsp;The total duration of antibiotic treatment did not differ between periods (median of 9 days, p = 0.32) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical response was high in both groups (89.2% overall), with no clinically significant differences observed (91.3% pre-intervention vs. 87.1% post-intervention; p = 0.12). Similarly, no significant differences were found in the rate of UTI relapse-related readmissions (7.4% vs. 10.3%, p = 0.26), length of hospital stay (median of 5 vs. 6 days, p = 0.13) (Supplementary Figure 5), or in-hospital mortality, which remained low across both periods (0.3% vs. 0.7%, p = 1.00) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBivariate and Multivariate Analysis of Factors Associated with Full Adherence to Clinical Flowcharts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary Outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFull adherence to the clinical flowchart was significantly higher during the post-intervention period (aOR: 1.61; 95% CI: 1.15\u0026ndash;2.24; p = 0.005). Female sex (aOR: 2.40; 95% CI: 1.66\u0026ndash;3.49; p \u0026lt; 0.001) and older age (aOR: 1.01; 95% CI: 1.00\u0026ndash;1.02; p = 0.016) were also significantly associated with higher adherence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary Adherence Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe post-intervention period was associated with increased adherence to both antibiotic dosing (aOR: 1.58; 95% CI: 1.02\u0026ndash;2.45; p = 0.045) and treatment duration (aOR: 1.55; 95% CI: 1.12\u0026ndash;2.16; p \u0026lt; 0.01). Female sex was also associated with better adherence to treatment duration (aOR: 2.12; 95% CI: 1.48\u0026ndash;3.06; p \u0026lt; 0.001), as was management through hospital-at-home care (aOR: 1.59; 95% CI: 1.01\u0026ndash;2.54; p = 0.047). The presence of bacteremia was associated with lower adherence to the empiric antibiotic type (aOR: 0.52; 95% CI: 0.28\u0026ndash;0.97; p = 0.035).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo significant associations were observed between the post-intervention period and clinical response (aOR: 0.65; 95% CI: 0.37\u0026ndash;1.10; p = 0.108), hospital readmission (aOR: 1.39; 95% CI: 0.78\u0026ndash;2.50; p = 0.264), antibiotic de-escalation (aOR: 1.01; 95% CI: 0.69\u0026ndash;1.50; p = 0.942), or switch to oral therapy. The presence of bacteremia and hospital-at-home care were associated with a lower likelihood of switching to oral therapy.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eOlder age, a diagnosis of pyelonephritis, the presence of bacteremia, and hospital-at-home management were all associated with longer hospital stays. No significant differences were observed regarding the post-intervention period (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Adjusted Clinical Outcomes in Patients with Urinary Tract Infection at San Ignacio Hospital (N = 601)\u003c/strong\u003e.\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\u003eClinical Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEstimator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003ePrimary outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\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\n \u003cp\u003e\u003cstrong\u003eaOR\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFull adherence to empirical therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-intervention period\u003c/p\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003cp\u003ePyelonephritis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBacteremia\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e\n \u003cp\u003ehospital-at-home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeighted logistic regression (WLR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.61\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.01\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.40\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.15-2.24\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.00-1.02\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.66-3.49\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.68-1.52\u003c/p\u003e\n \u003cp\u003e0.68-1.90\u003c/p\u003e\n \u003cp\u003e0.38-1.13\u003c/p\u003e\n \u003cp\u003e0.87-2.07\u003c/p\u003e\n \u003cp\u003e0.86-1.52\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 \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003eSecondary outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\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\n \u003cp\u003e\u003cstrong\u003eaOR\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdherence to antibiotic type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-intervention period\u003c/p\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003cp\u003ePyelonephritis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBacteremia\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e\n \u003cp\u003ehospital-at-home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.52\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.83-1.92\u003c/p\u003e\n \u003cp\u003e0.99-1.01\u003c/p\u003e\n \u003cp\u003e0.96-2.39\u003c/p\u003e\n \u003cp\u003e0.85-2.43\u003c/p\u003e\n \u003cp\u003e0.47-1.80\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.28-0.97\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.86-2.75\u003c/p\u003e\n \u003cp\u003e0.52-1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\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\n \u003cp\u003e\u003cstrong\u003eaOR\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdherence to antibiotic dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-intervention period\u003c/p\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003cp\u003ePyelonephritis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBacteremia\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e\n \u003cp\u003ehospital-at-home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.58\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.02-2.45\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.99-1.01\u003c/p\u003e\n \u003cp\u003e0.91-2.31\u003c/p\u003e\n \u003cp\u003e0.75-2.19\u003c/p\u003e\n \u003cp\u003e0.43-1.73\u003c/p\u003e\n \u003cp\u003e0.34-1.27\u003c/p\u003e\n \u003cp\u003e0.79-2.62\u003c/p\u003e\n \u003cp\u003e0.63-2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\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\n \u003cp\u003e\u003cstrong\u003eaOR\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdherence to treatment duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-intervention period\u003c/p\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003cp\u003ePyelonephritis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBacteremia\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e\n \u003cp\u003ehospital-at-home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.55\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.12\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.12-2.16\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.99-1.01\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.48-3.06\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.78-1.71\u003c/p\u003e\n \u003cp\u003e0.88-2.41\u003c/p\u003e\n \u003cp\u003e0.50-1.45\u003c/p\u003e\n \u003cp\u003e0.79-1.87\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.01-2.54\u003c/strong\u003e\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 \u003cp\u003e0.625\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\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\n \u003cp\u003e\u003cstrong\u003eaOR\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eClinical response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-intervention period\u003c/p\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003cp\u003ePyelonephritis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBacteremia\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e\n \u003cp\u003ehospital-at-home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.37-1.10\u003c/p\u003e\n \u003cp\u003e0.98-1.01\u003c/p\u003e\n \u003cp\u003e0.59-1.85\u003c/p\u003e\n \u003cp\u003e0.76-2.93\u003c/p\u003e\n \u003cp\u003e0.37-2.00\u003c/p\u003e\n \u003cp\u003e0.33-1.52\u003c/p\u003e\n \u003cp\u003e0.53-2.06\u003c/p\u003e\n \u003cp\u003e0.50-2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\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\n \u003cp\u003e\u003cstrong\u003eaOR\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHospital readmission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-intervention period\u003c/p\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003cp\u003ePyelonephritis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBacteremia\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e\n \u003cp\u003ehospital-at-home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.78-2.50\u003c/p\u003e\n \u003cp\u003e0.99-1.02\u003c/p\u003e\n \u003cp\u003e0.49-1.71\u003c/p\u003e\n \u003cp\u003e0.31-1.39\u003c/p\u003e\n \u003cp\u003e0.44-2.68\u003c/p\u003e\n \u003cp\u003e0.42-2.70\u003c/p\u003e\n \u003cp\u003e0.24-1.31\u003c/p\u003e\n \u003cp\u003e0.37-1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\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\n \u003cp\u003e\u003cstrong\u003eaOR\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAntibiotic de-escalation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-intervention period\u003c/p\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003cp\u003ePyelonephritis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBacteremia\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e\n \u003cp\u003ehospital-at-home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.94\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.05\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.69-1.50\u003c/p\u003e\n \u003cp\u003e1.00-1.02\u003c/p\u003e\n \u003cp\u003e0.61-1.40\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.26-3.00\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.47-1.50\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.34-7.04\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.67-1.76\u003c/p\u003e\n \u003cp\u003e0.59-1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\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\n \u003cp\u003e\u003cstrong\u003eaOR\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSwitch to oral therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-intervention period\u003c/p\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003cp\u003ePyelonephritis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBacteremia\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e\n \u003cp\u003ehospital-at-home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.21\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.26\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.64-1.29\u003c/p\u003e\n \u003cp\u003e0.98-1.00\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.51-3.24\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.47-1.10\u003c/p\u003e\n \u003cp\u003e0.55-1.57\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.13-0.48\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.89-2.24\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.14-0.42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.140\u003c/p\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\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\n \u003cp\u003e\u0026beta; coefficient\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLength of hospital stay (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-intervention period\u003c/p\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003cp\u003ePyelonephritis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBacteremia\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e\n \u003cp\u003ehospital-at-home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWeighted linear regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e-0.635\u003c/p\u003e\n \u003cp\u003e1.535\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.819\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.362\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e-0.168\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e6.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.79-0.86\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.02-0.06\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e-1.55-0.28\u003c/p\u003e\n \u003cp\u003e0.42-2.65\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.97-2.66\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.36-6.36\u003c/p\u003e\n \u003cp\u003e-1.31-0.97\u003c/p\u003e\n \u003cp\u003e4.41-7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.77\u003c/p\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\u003eaOR: Adjusted Odds Ratio. CI: Confidence Interval.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe rise of antimicrobial resistance in urinary tract infections (UTIs), particularly in middle-income countries such as Colombia, underscores the need to optimize empirical treatment strategies\u003csup\u003e\u0026sup1;-⁵ ⁹\u003c/sup\u003e. The implementation of clinical flowcharts has been associated with improved adherence to guidelines and favorable clinical outcomes\u003csup\u003e\u0026sup1;⁴ \u0026sup1;⁷ \u0026sup1;⁸ \u0026sup2;⁰\u003c/sup\u003e. However, few studies have evaluated their impact in hospital settings in Latin America. Our study assessed the effect of clinical flowcharts on empirical treatment adherence, clinical impact, and antimicrobial stewardship program outcomes (ASPs).\u003c/p\u003e\u003cp\u003eIn our cohort, the sociodemographic and clinical characteristics of hospitalized UTI patients were comparable across the pre- and post-intervention periods. The mean age was approximately 59 years in both groups, with a slight predominance of females, accounting for over 60% of the population. Pyelonephritis was the most frequent clinical presentation (87.3%), followed by cystitis. Regarding comorbidities, there was a high burden of chronic conditions, particularly type 2 diabetes mellitus (19.1%) and immunosuppression (24.1%), mostly involving patients with solid tumors, leukemia, or lymphoma. The Charlson comorbidity index was similar between groups (mean 3.29), reflecting a clinically complex population. After applying inverse probability of treatment weighting (IPTW), appropriate balance between groups was achieved across all baseline covariates (standardized mean differences\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting the validity of subsequent comparative analyses. Unlike prior studies focusing on younger, healthier populations\u003csup\u003e\u0026sup1;⁷\u003c/sup\u003e, our cohort included a high proportion of complicated UTIs and relevant clinical conditions. This aligns with recent studies evaluating ASP interventions in older adults\u003csup\u003e\u0026sup1;⁸\u003c/sup\u003e, supporting the applicability of clinical flowcharts in heterogeneous, clinically complex hospital populations.\u003c/p\u003e\u003cp\u003eThe increase in ertapenem use observed during the post-intervention period (19.3% vs. 37.4%) reflects empirical decision-making aligned with institutional flowcharts, which recommend this antibiotic for patients at risk for ESBL-producing Enterobacterales. This finding is further supported by the higher frequency of ESBL isolates in the post-intervention period, particularly in \u003cem\u003eEscherichia coli\u003c/em\u003e (17.9% vs. 30.7%) and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (8.3% vs. 23.8%). Notably, type II carbapenems were used infrequently despite ESBL production, aligning with evidence linking ertapenem to lower selection pressure for carbapenem-resistant \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e and carbapenemase-producing organisms. Literature supports the empirical use of ertapenem for ESBL infections, demonstrating similar effectiveness and 30-day mortality outcomes compared to other carbapenems\u003csup\u003e⁷ \u0026sup2;\u0026sup1; \u0026sup2;\u0026sup2;\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eThis study showed that implementing clinical flowcharts significantly improved key indicators of therapeutic adherence. In the IPTW-adjusted multivariable model, the post-intervention period was significantly associated with improved overall adherence (aOR: 1.61; 95% CI: 1.15\u0026ndash;2.24; p\u0026thinsp;=\u0026thinsp;0.005), as well as adherence to dosing (aOR: 1.58; 95% CI: 1.02\u0026ndash;2.45; p\u0026thinsp;=\u0026thinsp;0.045) and treatment duration (aOR: 1.55; 95% CI: 1.12\u0026ndash;2.16; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings support the use of flowcharts as effective tools for standardizing antimicrobial prescribing, enhancing treatment quality, and facilitating the implementation of institutional guidelines. Our findings are consistent with recent international studies demonstrating that structured interventions improve antimicrobial use\u003csup\u003e\u0026sup1;⁹ \u0026sup2;\u0026sup3;-\u0026sup2;⁴\u003c/sup\u003e. Additionally, evidence shows that multifaceted educational interventions can improve prescribing practices without compromising patient safety\u003csup\u003e\u0026sup2;\u0026sup3; \u0026sup2;⁴\u003c/sup\u003e. These results, aligned with our study, reinforce the value of clinical flowcharts as safe, effective tools in complex settings with high resistance burdens \u003csup\u003e\u0026sup2;⁵-\u0026sup2;⁶\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn our cohort, female sex was independently associated with greater adherence to empirical antimicrobial management. This may be partially explained by microbiological and clinical factors, as women especially those with uncomplicated pyelonephritis more frequently present with infections due to \u003cem\u003eEscherichia coli\u003c/em\u003e, a pathogen with more predictable susceptibility patterns. This facilitates appropriate empirical selection and spectrum adjustment\u003csup\u003e⁶\u003c/sup\u003e. However, evidence linking female sex with higher adherence or de-escalation rates remains insufficient. This finding should be interpreted cautiously and warrants further studies to assess whether this association persists after adjusting for clinical severity, comorbidities, and UTI type.\u003c/p\u003e\u003cp\u003eDespite improved adherence to treatment duration, the switch to oral therapy was not significantly higher in the post-intervention period. Furthermore, bacteremia and hospital-at-home care were associated with lower rates of switching to oral therapy in the IPTW-adjusted model. This discrepancy may reflect limited oral treatment options for ESBL-producing infections, as supported by Rodr\u0026iacute;guez Cervera et al\u003csup\u003e\u0026sup2;⁵\u003c/sup\u003e. who reported reluctance to switch due to perceived clinical severity and resistant pathogens. Additionally, resistance to fluoroquinolones and trimethoprim-sulfamethoxazole (commonly used oral agents) can be co-transferred with ESBL genes on the same plasmid\u003csup\u003e\u0026sup1;⁰ \u0026sup2;⁶\u003c/sup\u003e, limiting switch options.\u003c/p\u003e\u003cp\u003eAlthough the proportion of patients undergoing antibiotic de-escalation increased post-intervention (20.4% vs. 25.2%), the difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.19), and the intervention period was not significantly associated with de-escalation in the IPTW-adjusted model. However, immunosuppression and bacteremia were significantly associated with this outcome. This mirrors findings by Khasawneh et al., who reported a 47.7% rate of missed de-escalation opportunities despite clinical and microbiological criteria, with fluoroquinolone resistance and non-\u003cem\u003eE. coli\u003c/em\u003e pathogens as main barriers\u003csup\u003e\u0026sup2;⁷\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn our study, etiological diversity and resistance patterns may have had a similar effect, highlighting the need to strengthen institutional ASPs auditing and active support. Although bacteremia or immunosuppression might traditionally justify prolonged broad-spectrum therapy, evidence suggests that, when microbiological documentation and clinical response are available, de-escalation is feasible and safe even in immunocompromised patients with Enterobacterales bacteremia without increased mortality or relapse risk\u003csup\u003e\u0026sup2;⁷-\u0026sup3;⁰\u003c/sup\u003e. Thus, higher de-escalation rates in these subgroups may reflect prudent, microbiology-driven clinical practice rather than omission of caution.\u003c/p\u003e\u003cp\u003eFrom a therapeutic perspective, a high clinical response rate (89.2%) was observed overall, with no significant difference associated with the intervention (aOR: 1.11; 95% CI: 0.66\u0026ndash;1.89; p\u0026thinsp;=\u0026thinsp;0.682). Similarly, the post-intervention period was not associated with higher 30-day readmission risk (aOR: 0.86; 95% CI: 0.42\u0026ndash;1.75; p\u0026thinsp;=\u0026thinsp;0.676), and overall mortality remained low. These findings suggest that the intervention was safe and did not compromise clinical effectiveness. This aligns with previous reports by Hartman, Zalmanovich, and Arnold et al., showing favorable outcomes after ASP-associated interventions in UTI management\u003csup\u003e\u0026sup1;⁸ \u0026sup2;\u0026sup3;-\u0026sup2;⁴\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHospital length of stay (LOS) was not significantly affected by the intervention (β\u0026thinsp;=\u0026thinsp;0.034; 95% CI: -0.79 to 0.86; p\u0026thinsp;=\u0026thinsp;0.934). However, LOS was significantly longer among patients with bacteremia (β\u0026thinsp;=\u0026thinsp;4.36; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and those receiving hospital-at-home care (β\u0026thinsp;=\u0026thinsp;6.00; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings are consistent with literature showing that antimicrobial optimization strategies do not prolong hospitalization or increase readmission rates, even in patients with multiple comorbidities\u003csup\u003e⁷ \u0026sup1;⁸\u003c/sup\u003e. They also align with prior studies documenting longer hospital stays in patients with severe infections or bacteremia\u003csup\u003e\u0026sup3;\u0026sup1;-\u0026sup3;\u0026sup3;\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study was conducted in a high-complexity academic hospital, enhancing the relevance and potential applicability of findings to similar clinical settings. The ambispective quasi-experimental design facilitated robust comparisons between pre- and post-intervention periods through randomized sampling and standardized data collection. A rigorous methodological approach was employed, including bivariate and multivariable regression analyses with inverse probability of treatment weighting (IPTW) to minimize confounding and estimate the average treatment effect. The comprehensive assessment of antimicrobial adherence, clinical response, microbiological profiles, and hospital outcomes provides a holistic evaluation of the clinical flowchart\u0026rsquo;s impact.\u003c/p\u003e\u003cp\u003eNevertheless, limitations typical of before-and-after quasi-experimental designs must be acknowledged, particularly the inability to fully control for unmeasured confounders such as fluctuations in staffing, antimicrobial availability, or healthcare system pressures. The application of IPTW adjustment helped mitigate these limitations and supported internal validity. Although the sample size was sufficient to detect differences in adherence, the study was not powered to assess infrequent outcomes such as mortality or readmission. Furthermore, individual adherence to clinical flowcharts was not directly measured. However, acceptance of antimicrobial stewardship program recommendations remained high across both periods (96.8% pre vs. 91.9% post; p\u0026thinsp;=\u0026thinsp;0.15), aligning with previous successful implementations of multifaceted ASP strategies⁷ \u0026sup3;⁴. Notably, most de-escalation and oral switch decisions were made by the treating physicians (70.8% and 94.7%, respectively), reflecting strong clinician engagement in stewardship practices.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eInstitutional flowcharts for empirical UTI management significantly improved adherence to antibiotic dosing and duration in a high-complexity hospital, without compromising clinical outcomes\u0026mdash;even amid high resistance. These findings support the integration of flowcharts into ASPs. Future efforts should focus on strengthening de-escalation and oral switch practices and evaluating applicability across diverse clinical settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Research and Ethics Committee (CIEI) of the Faculty of Medicine, Pontificia Universidad Javeriana, and Hospital Universitario San Ignacio (Approval Act 06/2025, code 2025/048). The requirement for informed consent was waived due to the retrospective nature of the study and the use of anonymized data.\u003c/p\u003e\n\u003cp\u003eConsent for Publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials:\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eConflict of Interest Statement\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest related to this work.\u003c/p\u003e\n\u003cp\u003eFunding Statement\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eS.L. Valderrama, A. Rivera Tenorio, C.R. Diaz Brochero, S.M. Gualtero Jim\u0026eacute;nez, and S.A. Mackenzie participated in the study design, data collection, results analysis, and manuscript writing. C.A. Bonilla, N. Manrique Mar\u0026iacute;n, M. P. Lampis, L.V. Bejarano Mora, and C.A. Bejarano Mora contributed to the study design and data collection. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors thank the Antimicrobial Stewardship Program team at Hospital Universitario San Ignacio for their support in the implementation of the clinical flowcharts and their participation in the therapeutic process audit.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFoxman B. The epidemiology of urinary tract infection. 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Infect Control Hosp Epidemiol. 2020;41(10):1225\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/ice.2020.216\u003c/span\u003e\u003cspan address=\"10.1017/ice.2020.216\" 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":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Urinary Tract Infections, Clinical Pathways, Medication Adherence, Anti-Bacterial Agents/therapeutic use, Antimicrobial Stewardship, Hospitalization","lastPublishedDoi":"10.21203/rs.3.rs-7746024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7746024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eUrinary tract infections (UTIs) are a common cause of hospitalization and antimicrobial use. This study evaluated the impact of clinical flowcharts on adherence to empirical treatment and clinical outcomes in hospitalized patients with UTIs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA quasi-experimental, ambispective before-and-after study was conducted at a tertiary university hospital in Bogot\u0026aacute;, Colombia (January 2023\u0026ndash;January 2025). Adult patients with community-acquired UTIs were included. Flowcharts based on guidelines, local susceptibility patterns, and drug availability were implemented. Outcomes included adherence (type, dose, duration), de-escalation, oral switch, clinical response, length of stay, and readmission. Analyses were adjusted using inverse probability of treatment weighting (IPTW).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 601 patients were analyzed (299 pre- and 302 post-intervention). Overall adherence improved from 38.1% to 51.6% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Increases were also observed in adherence to antibiotic type (76.9% vs. 85.4%), dose (76.9% vs. 88.1%), and duration (48.8% vs. 59.9%). Post-intervention was independently associated with greater adherence (aOR: 1.61; 95% CI: 1.15\u0026ndash;2.24; p\u0026thinsp;=\u0026thinsp;0.005), without significant impact on clinical outcomes.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e\u003cp\u003eFlowcharts improved adherence to empirical therapy without compromising safety or effectiveness, reinforcing their value in antimicrobial stewardship programs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eClinical flowcharts are effective tools for standardizing empirical UTI management in high-resistance settings.\u003c/p\u003e","manuscriptTitle":"Impact of Clinical Flowcharts on Therapeutic Adherence, Treatment Duration, and Clinical Outcomes in Urinary Tract Infections: A Quasi-Experimental Study in a Colombian University Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 20:08:58","doi":"10.21203/rs.3.rs-7746024/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-09T15:22:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133309262645520540116681495807017821839","date":"2025-11-09T15:02:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T05:56:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T11:16:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T00:12:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-06T00:11:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-09-30T01:33:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2cf6ebd7-8b37-433b-b7e1-551b5f74876c","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-11T20:08:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 20:08:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7746024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7746024","identity":"rs-7746024","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}