Interpretable Machine Learning Model for Predicting Prolonged Postoperative Length of Stay in Lung Cancer Patients Undergoing Day Surgery: A Retrospective Cohort Study.

[BACKGROUND] Prolonged postoperative length of stay (p-LOS) in lung cancer patients is associated with poorer prognosis and increased healthcare burden, highlighting the need for early identification of high risk individuals. This study aimed to develop and validate a machine learning model to predict p-LOS in patients undergoing day surgery for lung cancer.

[METHODS] A retrospective analysis was conducted on 1009 patients who underwent day surgery for lung cancer. Prolonged p-LOS was defined as hospitalization exceeding 48 hours. Feature selection was performed using Lasso regression, and predictive models were developed using 6 machine learning algorithms with internal validation. Model performance was evaluated using AUC and other metrics, and the SHAP method was applied to interpret feature contributions and individual predictions.

[RESULTS] Among the included patients, 128 (12.69%) experienced prolonged p-LOS. The LightGBM model demonstrated optimal performance, achieving an AUC of 0.947 (0.919-0.974). Key predictive factors included fatigue, subcutaneous emphysema, pain, and cough.

[CONCLUSIONS] The developed machine learning model accurately predicts prolonged p-LOS risk in lung cancer patients following day surgery, with high discriminative ability and interpretability. It shows potential for supporting early clinical intervention and optimizing postoperative care.