Targeting delirium in gastric surgery: An interpretable predictive model and statistical analysis in critical care

Background Postoperative delirium is a serious complication in elderly ICU patients after gastric surgery, increasing mortality and healthcare burden. Early identification of high-risk patients may support timely prevention and optimise perioperative care. Objective To develop and externally validate an interpretable machine learning model for predicting postoperative delirium in elderly ICU patients undergoing gastric surgery. Methods This retrospective multicohort study used three perioperative and critical care databases. MIMIC-IV supported model development and internal validation, while eICU and MOVER supported external validation. Feature selection used LASSO regression, followed by comparison of six machine learning algorithms. Logistic regression was selected using the MIMIC-IV internal validation cohort based on discrimination, recall, and clinical interpretability; the locked model was then evaluated in eICU and MOVER. SHAP analysis quantified predictor contributions and enhanced model transparency. Results The final population included four cohorts: MIMIC-IV training ( N = 4316), MIMIC-IV internal validation ( N = 1851), eICU external testing ( N = 1745), and MOVER external testing ( N = 1152). The internally selected logistic regression model achieved AUCs of 0.819 in eICU and 0.827 in MOVER, with recalls of 0.827 and 0.936, respectively. SHAP analysis identified chloride, sodium, bicarbonate, and anion gap as dominant predictors across cohorts. Conclusion This study presents an interpretable postoperative delirium risk-prediction model in elderly ICU patients following gastric surgery. Although the model achieved high recall, marked class imbalance and low outcome prevalence were associated with low precision and a substantial false-positive burden. Prospective validation, threshold optimisation, and assessment of clinical utility are required before implementation.