Abstract
Abstract
Background
Postoperative hypotension (POH) is a common and serious complication in patients with type 2 diabetes mellitus (T2DM) undergoing non‑cardiac surgery, yet predictive tools tailored to this high‑risk population remain scarce.
Methods
This single‑center cohort study developed and validated a machine learning (ML) model to predict the risk of postoperative hypotension (POH) occurring during the post‑anaesthesia care unit (PACU) stay, defined as systolic blood pressure
Results
Random Forest achieved the best overall performance, with AUCs of 0.843 (95% CI 0.837–0.849) on training, 0.854 (95% CI 0.848–0.860) on internal validation, and 0.847 (95% CI 0.840–0.854) on prospective validation. External validation on an independent hospital cohort (
n
= 2156) yielded an AUC of 0.822 (95% CI 0.805–0.839), confirming generalisability. It demonstrated high sensitivity (0.932) and reliable calibration. SHAP analysis identified intraoperative blood loss, age, heart failure, obstructive sleep apnoea, and body mass index as the top predictors, providing transparent global and local explanations for individual risk.
Conclusion
An interpretable ML model based on routinely collected clinical data accurately predicts POH risk in T2DM patients after non‑cardiac surgery. The model combines strong discriminative performance with clinical explainability, suggesting its potential as a practical tool for preoperative risk stratification and personalized postoperative monitoring in T2DM patients within similar clinical settings.