Evaluation of Machine Learning and Statistical Models for Predicting Long Term Gastrostomy Tube Dependency in Patients Undergoing Major Oral Cavity Cancer Surgery With Free Flap Reconstruction

BACKGROUND: Swallowing dysfunction and long-term gastrostomy tube dependence are common morbidities after oral cavity cancer resection with free flap reconstruction, highlighting the need for preoperative risk prediction. METHODS: We performed a multicenter retrospective pooled cohort study of patients with non-metastatic oral cavity cancer undergoing free flap reconstruction. Firth's penalized logistic regression, LASSO, and random forest models were trained using 10-fold cross-validation and predictive performance was assessed via area under the receiver operator curve (AUC). RESULTS: Among 500 patients (mean age 61.7 years), 78 (16%) were gastrostomy tube dependent at 12 months. The random forest model was most accurate in predicting 12-month gastrostomy tube dependency (AUC; 0.77), with comparable performance from LASSO (AUC; 0.71) and Firth's penalized logistic regression (AUC; 0.70). Key predictors included TNM stage IV, T4 disease, unilateral neck dissection, reconstruction site, and no tongue resection. CONCLUSIONS: The three models predicted 12-month gastrostomy tube dependency after oral cavity free-flap surgery with fair accuracy and novel predictors; larger multicenter datasets may further improve predictive performance.