Development and Validation of an Interpretable Machine-Learning Model for Predicting Suboptimal Clinical Response After Sleeve Gastrectomy: A Multicenter Study

Suboptimal clinical response (SCR) after sleeve gastrectomy (SG) remains a major clinical challenge, yet reliable preoperative risk stratification is lacking. Therefore, we developed and externally validated an interpretable machine-learning model for predicting SCR after SG using multicenter data from three tertiary hospitals in China. Overall, 1312 patients were included, comprising a derivation cohort (n = 1094) and two external validation cohorts (n = 84 and n = 134). SCR, defined as percentage of total weight loss < 25% at 1 year, occurred in 374 (34.2%), 34 (40.5%), and 45 (33.6%) patients, respectively. Among six machine-learning algorithms, XGB achieved the best performance. The final model incorporated eight routinely available predictors. In internal validation, the model showed excellent discrimination, with an AUC of 0.821 (95% confidence interval [CI], 0.752-0.871), sensitivity of 0.803, and specificity of 0.714. External validation confirmed satisfactory discrimination (AUC 0.761; 95% CI, 0.688-0.823), good calibration, and clinical benefit. Notably, the XGB model significantly outperformed 10 clinicians across experience levels and remained superior in clinically challenging cases. SHAP analysis enhanced interpretability, with predictor importance ranked as BMI, age, fasting glucose, type 2 diabetes, vitamin B12, calcium, total cholesterol, and gamma-glutamyl transferase. This interpretable XGB model may enable precise preoperative risk stratification and support personalized management for patients undergoing SG.