Machine learning-based prediction of 30-day unplanned readmission risk in day surgery lung cancer patients after lobectomy or sublobectomy: a real-world study.

Background: Unplanned readmission within 30 days after lobectomy or sublobectomy for early stage lung cancer adversely affects patient recovery and healthcare costs. While machine-learning (ML) approaches offer potential for improved prediction, few models have been developed for day-surgery settings. This study aimed to develop and validate an ML-based model to predict 30-day unplanned readmission in lung cancer patients undergoing ambulatory lung resection. Methods: We included patients who underwent lobectomy or sublobectomy in a day-surgery pathway between December 2022 and January 2025. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Data were split into training (70%) and validation (30%) sets. Nine ML algorithms were trained and evaluated using area under the receiver-operating-characteristic curve (ROC-AUC), precision-recall AUC (PR-AUC), accuracy, decision-curve analysis (DCA), and calibration curves. Model interpretability was assessed with SHapley Additive exPlanations (SHAP). Results: After propensity-score matching, 380 patients were analyzed, including 111 with unplanned readmission. LASSO identified 12 predictive features: age, payment category, prothrombin time (PT), white-blood-cell count (WBC), hemoglobin, intraoperative blood loss, surgical approach, pathological diagnosis, tumor count, tumor size, occupational category, and forced expiratory volume in 1 s (FEV Conclusion: An RF-based model effectively predicted 30-day unplanned readmission after lung-cancer day surgery. The identified risk factors provide a basis for early stratification and targeted intervention, supporting optimized perioperative care in ambulatory settings.