Infection and drug resistance · 2026
Risk Stratification for ESBL-Producing Enterobacterales in Elderly Diabetic Patients with Urinary Tract Infections: A Multicenter Model to Support Empirical Antibiotic Decision-Making.
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Huang Tingting, Qin Jibao, Jiang Xi, Hu Ming, Wang Jiaping, Chen Kaiyong, Yang Fumeng, Gu Jin, Wu Huiyi, Chen Xinkuan
Аннотация
OBJECTIVE: Antimicrobial resistance among elderly diabetic patients with urinary tract infections (UTIs) poses a significant challenge for empirical antibiotic therapy. Delayed availability of microbiological susceptibility results often leads to treatment mismatch and inappropriate broad-spectrum antibiotic use. This study aimed to develop and validate a clinically interpretable risk stratification model to estimate the probability of extended-spectrum β-lactamase (ESBL)-producing Enterobacterales isolation prior to microbiological confirmation, thereby supporting early empirical antibiotic decision-making.
METHODS: We conducted a multicenter retrospective cohort study including elderly (≥60 years) patients with type 2 diabetes and positive urine cultures. Multivariable logistic regression was used to construct a prediction model for ESBL-positive isolation. Model discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUC), Brier score, calibration plots, and bootstrap internal validation. Internal-external cross-validation and independent external validation were performed to assess model transportability. Decision curve analysis (DCA) was applied to evaluate clinical net benefit across threshold probabilities.
RESULTS: A total of 612 patients were included, of whom 364 (58.6%) had ESBL-positive isolates. Independent predictors included diabetes duration ≥10 years, HbA1c ≥8.5%, recent antibiotic exposure, urinary tract device use, and low-level pyuria (<5 WBC/HPF). The model demonstrated stable discrimination (AUC 0.81 in the training set; 0.79 in internal validation; 0.81 in external validation) and good calibration (Brier score 0.18-0.19). Decision curve analysis showed meaningful clinical net benefit across a wide range of threshold probabilities (10%-65%). Exploratory analysis indicated that empirical therapy mismatch was associated with prolonged hospitalization and increased sepsis incidence, underscoring the potential clinical relevance of early resistance risk identification.
CONCLUSION: This multicenter risk stratification model provides a practical tool for early estimation of ESBL-producing Enterobacterales risk in elderly diabetic patients with UTIs. By integrating routinely available clinical and laboratory variables, the model may support antimicrobial stewardship efforts and improve empirical antibiotic decision-making before susceptibility results become available. Prospective validation in diverse healthcare settings is warranted to confirm its clinical impact.
METHODS: We conducted a multicenter retrospective cohort study including elderly (≥60 years) patients with type 2 diabetes and positive urine cultures. Multivariable logistic regression was used to construct a prediction model for ESBL-positive isolation. Model discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUC), Brier score, calibration plots, and bootstrap internal validation. Internal-external cross-validation and independent external validation were performed to assess model transportability. Decision curve analysis (DCA) was applied to evaluate clinical net benefit across threshold probabilities.
RESULTS: A total of 612 patients were included, of whom 364 (58.6%) had ESBL-positive isolates. Independent predictors included diabetes duration ≥10 years, HbA1c ≥8.5%, recent antibiotic exposure, urinary tract device use, and low-level pyuria (<5 WBC/HPF). The model demonstrated stable discrimination (AUC 0.81 in the training set; 0.79 in internal validation; 0.81 in external validation) and good calibration (Brier score 0.18-0.19). Decision curve analysis showed meaningful clinical net benefit across a wide range of threshold probabilities (10%-65%). Exploratory analysis indicated that empirical therapy mismatch was associated with prolonged hospitalization and increased sepsis incidence, underscoring the potential clinical relevance of early resistance risk identification.
CONCLUSION: This multicenter risk stratification model provides a practical tool for early estimation of ESBL-producing Enterobacterales risk in elderly diabetic patients with UTIs. By integrating routinely available clinical and laboratory variables, the model may support antimicrobial stewardship efforts and improve empirical antibiotic decision-making before susceptibility results become available. Prospective validation in diverse healthcare settings is warranted to confirm its clinical impact.