Predicting Nephrectomy Risk in Patients with Renal Cancer Using Real-World Electronic Health Records

Nephrectomy, the surgical removal of a kidney, is a critical treatment for renal cancer, and predicting its likelihood can help guide clinical decision-making and optimize preoperative planning. This study utilized real-world electronic health record (EHR) data from the UF Health Integrated Data Repository (IDR) to evaluate machine learning (ML) models for nephrectomy risk prediction in patients with malignant renal tumors. Demographic, clinical, and laboratory data prior to diagnosis were used for model training and validation. Extreme gradient boosting (XGBoost) outperformed other models, achieving an F1 score of 0.638 and an AUC of 0.807. SHapley Additive exPlanations (SHAP) highlighted key predictors, with top factors including HbA1C, serum creatinine, blood urea nitrogen (BUN), BUN-to-creatinine ratio, and glucose levels. These findings illustrate the potential of ML and real-world EHR data in supporting nephrectomy risk prediction and personalized care strategies.