Development and internal validation of a prediction model for healthcare-associated infections in women with gestational diabetes mellitus undergoing delivery.

objectiveWomen with gestational diabetes mellitus (GDM) undergoing delivery face a significantly higher risk of healthcare-associated infections (HAIs) due to metabolic disorders and impaired immune function compared with healthy pregnant women, making them a key population of concern in HAI prevention and control. However, effective early warning tools specifically tailored to this population are still lacking in clinical practice. Therefore, this study aimed to develop a risk prediction model for HAIs in women with GDM undergoing delivery, with the goal of identifying high-risk individuals and facilitating early intervention.

methodsModel development and validation were conducted using a sequential approach. In the development phase, a 10‑repeated 5‑fold cross‑validation was performed on the training set (n = 690; January 2022 - December 2024). To address severe class imbalance, the random over‑sampling examples method was applied within each training fold, followed by random forest (RF) modeling. This procedure was repeated 10 times with different random seeds, yielding 50 performance estimates. Candidate variables selected in at least 70% of the 50 folds were retained as stable predictors. Using this selected feature set, both logistic regression (LR) and RF models were developed. Model performance was then evaluated on an independent temporal validation set (n = 294; January - December 2025), which was completely withheld from the model development process.

resultsA 10‑repeated 5‑fold cross‑validation on the training set identified five stable predictors selected in ≥ 70% of the 50 model fits: mode of delivery, Group B Streptococcus colonization, artificial rupture of membranes, misoprostol administration, and balloon catheter for cervical ripening. Using these predictors, both RF and LR models were developed. In the temporal validation set (n = 294; positive rate 5.78%), the RF model achieved a PR‑AUC of 0.407 (95% CI: 0.145-0.632) and an ROC‑AUC of 0.789 (95% CI: 0.646-0.911). The LR model yielded a PR‑AUC of 0.345 (95% CI: 0.124-0.563) and an ROC‑AUC of 0.803 (95% CI: 0.665-0.920). The Brier score was 0.0472 for LR and 0.1134 for RF.

conclusionThe RF model demonstrated superior performance in identifying positive cases and exhibited greater stability, whereas LR achieved better probability calibration. Given the clinical priority of minimizing false negatives, RF may be preferable when sensitivity is prioritized, while LR may be more suitable when well-calibrated probabilities are required. Both models showed moderate discriminative ability and require further external validation before clinical implementation. Decision curve analyses supported their potential clinical utility. CLINICAL TRIAL NUMBER: Not applicable.