Development of a prediction model for postoperative urinary tract infection in ureteral stone patients based on automated machine learning models
Abstract
Objective This study aimed to develop a predictive model for postoperative urinary tract infection (PO-UTI) in ureteral stone patients, addressing limitations of traditional research methods and advancing perioperative infection management from experience-driven to data-driven transformation. Methods A retrospective cohort design was employed, enrolling 826 ureteral stone surgery patients (January 2020 to January 2024) with data on demographics, disease characteristics, and hematological indicators collected via structured electronic medical records. Feature selection was optimized using an improved ISequoiaOA meta-heuristic algorithm to enhance model optimization stability; the SMOTE-ENN hybrid sampling technique was applied to balance class distribution; an AutoML framework integrating SHAP interpretability analysis was constructed to quantify feature contribution and visualize interaction effects; and a clinical decision support system was developed. Results (1) optimal performance of the AutoML model on the test set (ROC-AUC=0.9251, PR-AUC=0.8712), significantly outperforming traditional algorithms such as XGBoost and LightGBM; (2) key predictors identified via SHAP analysis included preoperative urinary retention, low serum albumin (ALB), diabetes, double-J stent indwelling time, postoperative catheter indwelling time, and age, with interaction effects revealing a nonlinear synergistic surge in infection risk when stone size exceeded 6 mm and catheter indwelling time…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
Read the paper
Related studies
- Association of lower urinary tract symptoms and OAB severity with quality of life and mental health in China, Taiwan and South Korea: results from a cross-sectional, population-based study
- Ten-Day Quadruple Therapy Comprising Low-Dose Rabeprazole, Bismuth, Amoxicillin, and Tetracycline Is an Effective and Safe First-Line Treatment for Helicobacter pylori Infection in a Population with High Antibiotic Resistance: a Prospective, Multicenter, Randomized, Parallel-Controlled Clinical Trial in China
- The Role of the NLR and PLR in Urinary Tract Infection
- Threat of Antimicrobial Resistance among Pilgrims with Infectious Diseases during Hajj: Lessons Learnt from COVID-19 Pandemic
- Leclercia adecarboxylata urinary tract infection in a patient with bladder cancer and recurrent hematuria
- A Case Series of Staphylococcus lugdunensis Infection in Cancer Patients at an Academic Cancer Institute in the United States
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.