Explainable electronic medical record-based machine learning to predict 1-year incident Protein-Energy wasting in non-dialysis chronic kidney disease: A single-center development study.

BACKGROUND: Protein-energy wasting (PEW) is common in chronic kidney disease (CKD) and is linked to poor outcomes. Early risk stratification may enable timely nutritional intervention.

OBJECTIVE: To develop an electronic medical record (EMR)-based machine-learning model for 1-year incident CKD-PEW and report transparent, model-agnostic performance.

METHODS: We retrospectively analyzed 500 adults with CKD managed between July 2019 and May 2021. Patients with PEW (defined by ISRNM criteria) at baseline were excluded. Thirty-one routine clinical/laboratory variables were used to train and evaluate five algorithms-logistic regression (LR), support vector machine (SVM), decision tree, random forest (RF), and LightGBM-under a random 75/25 split (382 training, 118 validation). Model performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI), Brier score, calibration plots, and decision curve analysis (DCA). Shapley additive explanations (SHAP) were used for interpretability.

RESULTS: On the hold-out validation set, the optimized Light Gradient Boosting Machine (LightGBM) achieved the highest discrimination with an AUC of 0.95 (95% CI: 0.91-0.99). The model also demonstrated excellent probability calibration with a Brier score of 0.095.

CONCLUSIONS: Using routine EMR data, we developed an explainable model that demonstrates high discrimination for predicting 1-year incident CKD-PEW on a hold-out validation set. The workflow provides a pragmatic foundation for subsequent independent validation, calibration assessment, and decision-curve analysis before clinical use.

CLINICAL TRIAL NUMBER: ChiCTR2100046679.