Optimization of initial empiric therapy for urinary tract infections in hospitalized patients based on local microbiological surveillance

Introduction. Urinary tract infections (UTIs) remain among the most common bacterial infections in hospitalized patients. The increasing antimicrobial resistance, particularly among Escherichia coli strains, significantly complicates the selection of effective empiric therapy.

Objective. To improve the effectiveness of initial UTI treatment in hospitalized patients based on local microbiological surveillance and current clinical guidelines.

Methods. An analytical review of international (IDSA, EAU) and Russian clinical guidelines (2023–2025) was conducted, along with a retrospective analysis of 40 medical records of inpatients with confirmed UTIs. Demographic characteristics, pathogen distribution, antimicrobial resistance profiles (according to EUCAST/CLSI criteria), and initial empiric therapy were assessed.

Results. The predominant pathogen was E. coli (≈70 % of isolates), demonstrating 100 % resistance to ciprofloxacin and cefotaxime, while retaining susceptibility to nitrofurantoin, amoxicillin/clavulanate, aminoglycosides, and carbapenems. Ampicillin resistance was observed in 46 % of E. coli strains. Cases of inadequate empiric therapy were identified, particularly in infections caused by ESBL-producing strains. The mean patient age was 72 years, with the majority having multiple comorbidities (hypertension — 95 %, diabetes mellitus — 32.5 %). Based on the findings, a structured clinical algorithm was developed, including risk stratification, diagnostic steps, empiric antibiotic selection rules, and criteria for specialist consultation. The algorithm has been integrated into a registered Telegram-based antimicrobial therapy decision-support chatbot (certificate No. RU 2024614994).

Conclusion. The implementation of local microbiological surveillance and a structured algorithm improves the rationale of empiric antibacterial therapy for UTIs, reduces the likelihood of inappropriate prescriptions, and promotes rational antimicrobial use in hospital settings. The proposed approach aligns with current clinical guidelines and can be recommended for integration into clinical decision support systems.

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