An Interpretable Machine Learning Survival Model for Predicting Long-term Kidney Outcomes in IgA Nephropathy

IgA nephropathy (IgAN) is common worldwide and has heterogeneous phenotypes. Predicting long-term outcomes is important for clinical decision-making. As right-censored patients become common during the long-term follow-up, either excluding these patients from the cohort or labeling them as control will bias the risk estimation. Thus, we constructed a survival model using EXtreme Gradient Boosting for survival (XSBoost-Surv), to accurately predict the prognosis of IgAN patients by taking the time-to-event information into the modeling procedure. Shapley Additive exPlanations (SHAP) was employed to interpret the individual predicted result and the non-linear relationships between the predictors and outcome. Experiments on real-world data showed our model achieved superior discrimination performance over other conventional survival methods. By providing insights into the exact changes in risk induced by certain characteristics of the patients, this explainable and accurate survival model can help improve the clinical understanding of renal progression and benefit the therapies for the IgAN patients.