Machine learning models predict the progression of long-term renal insufficiency in patients with renal cancer after radical nephrectomy.

Abstract

Background: Chronic Kidney Disease (CKD) is a common severe complication after radical nephrectomy in patients with renal cancer. The timely and accurate prediction of the long-term progression of renal function post-surgery is crucial for early intervention and ultimately improving patient survival rates.

Objective: This study aimed to establish a machine learning model to predict the likelihood of long-term renal dysfunction progression after surgery by analyzing patients' general information in depth.

Methods: We retrospectively collected data of eligible patients from the Affiliated Hospital of Qingdao University. The primary outcome was upgrading of the Chronic Kidney Disease stage between pre- and 3-year post-surgery. We constructed seven different machine-learning models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (Lightgbm), Gaussian Naive Bayes (GaussianNB), and K-Nearest Neighbors (KNN). The performance of all predictive models was evaluated using the area under the receiver operating characteristic curve (AUC), precision-recall curves, confusion matrices, and calibration curves.

Results: Among 360 patients with renal cancer who underwent radical nephrectomy included in this study, 185 (51.3%) experienced an upgrade in Chronic Kidney Disease stage 3-year post-surgery. Eleven predictive variables were selected for further construction of the machine learning models. The logistic regression model provided the most accurate prediction, with the highest AUC (0.8154) and an accuracy of 0.787.