Machine learning-based prediction of 30-day unplanned readmission risk in day surgery lung cancer patients after
Nafei Han1, Chuanbo An2, Huadi Yuan1
1Department of Nursing, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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 (FEV1). The random forest (RF) model performed best in the validation set (ROC-AUC = 0.939, accuracy = 0.825), showed favorable net benefit across threshold probabilities of 10-80%, and was well-calibrated. SHAP analysis indicated WBC, PT, hemoglobin, intraoperative blood loss, and "unknown" occupational category as the top five predictors; WBC, PT, and blood loss were positively associated with readmission risk.
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.
