BACKGROUND: Postoperative urinary tract infection (UTI) is a common complication among older surgical patients and is associated with prolonged hospitalization, increased healthcare utilization, and adverse postoperative outcomes. Early identification of patients at high risk of postoperative UTI remains challenging in routine clinical practice. This study aimed to develop and internally validate a clinically interpretable dynamic postoperative risk prediction model for postoperative UTI in older adult surgical patients using routinely available perioperative and early postoperative data. METHODS: This retrospective cohort study included patients aged ≥ 65 years who underwent inpatient surgery at a tertiary teaching hospital in Southwest China between January 2019 and December 2024. The primary outcome was postoperative UTI occurring during hospitalization or within 30 days after surgery. The model was designed as a dynamic postoperative risk estimation tool rather than a strictly preoperative prediction model. Patients were randomly divided into a training cohort (n = 239) and a validation cohort (n = 103). Candidate predictors were selected a priori based on clinical relevance and existing literature. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, followed by multivariable logistic regression to construct the prediction model. Model discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. Model interpretability was evaluated using Shapley additive explanations (SHAP). RESULTS: Among 239 patients in the training cohort, 48 (20.1%) developed postoperative UTI. Six variables were retained in the final multivariable model: diabetes, ICU admission, urinary catheter duration, recatheterization, length of hospital stay, and readmission within 30 days. A nomogram was constructed to facilitate individualized risk estimation. The model demonstrated good discrimination in the training cohort (AUC 0.886, 95% CI 0.836-0.936) and maintained acceptable performance in the validation cohort (AUC 0.830, 95% CI 0.736-0.924). Calibration plots showed good agreement between predicted and observed risks in both cohorts. Decision curve analysis indicated potential clinical utility across a range of threshold probabilities. SHAP analyses highlighted length of hospital stay and catheter-related variables as the most influential contributors to predicted UTI risk. CONCLUSIONS: We developed and internally validated a clinically interpretable dynamic postoperative risk estimation model for postoperative UTI in older surgical patients using routinely available perioperative and postoperative data. The model demonstrated good discrimination, calibration, and potential clinical utility, and may support in-hospital postoperative risk stratification, catheter management review, intensified symptom surveillance, clinically indicated urine testing or physician reassessment, and reinforcement of modifiable preventive measures during postoperative care. Because several retained predictors are time-dependent postoperative variables, and because the outcome definition reflected routine clinical practice rather than a fully standardized surveillance definition, the model should not be interpreted as a strictly preoperative or early admission-time bedside prediction tool, but rather as a dynamic postoperative risk estimation framework for in-hospital risk stratification. Clinically actionable thresholds and implementation pathways require prospective validation before routine use.
Sun et al. (Thu,) studied this question.
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