Development and external validation of an explainable machine-learning model for early complications following...

Complications occurring early after robot-assisted radical prostatectomy (RARP) can hinder postoperative recovery. Existing risk instruments, however, generally incorporate few predictors and assume linear associations. We aimed to derive and externally validate an interpretable machine-learning model that combines clinical, laboratory, and intraoperative information and to translate the model into an online risk calculator. Records were reviewed for 1,254 patients with prostate cancer (PCa) who received RARP at the First, Third, or Fifth Medical Center of the Chinese PLA General Hospital. Among them, 1,001 patients from the First and Fifth centers were randomly allocated 7:3 to training (n = 701) and internal testing (n = 300), while 253 patients from the Third center provided an independent external cohort. Sixty-four candidate variables underwent univariate screening and least absolute shrinkage and selection operator regression. The tested algorithms were gradient boosting decision trees (GBDT), random forest, logistic regression, support vector machine, AdaBoost, Gaussian naive Bayes, and multilayer perceptron. We examined discrimination with the area under the receiver operating characteristic curve (AUC), agreement between predicted and observed risk with calibration plots, and clinical usefulness with decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to interpret predictions. The leading model was also benchmarked against the Comorbidity Score for Robotic Surgery (CRS). Feature selection yielded eight predictors for model training. For the multilayer perceptron (MLP), the AUC was 0.880 on internal testing and 0.843 in the independent external cohort. Across both evaluations, this algorithm offered the most balanced combination of performance measures, good calibration, and favorable clinical net benefit. SHAP ranked crystalloid infusion as the leading predictor; operative time and thrombin time (TT) ranked next. In an exploratory comparison in the external cohort, MLP showed greater potential than CRS in discrimination, calibration, and net benefit. We visualized the final model and deployed it as a web-based calculator. The interpretable MLP model accurately estimated the likelihood of early postoperative complications among patients with PCa undergoing RARP. Its accompanying online calculator may assist clinicians in recognizing high-risk patients as soon as surgery is completed and may inform individualized risk classification, postoperative monitoring, and clinical care.