Application of machine learning algorithm for the prediction of lupus nephritis using SNP data, polygenic risk score, and electronic health record

Background: Lupus nephritis (LN) flares raise the risks of renal failure and mortality in systemic lupus erythematosus (SLE) patients, making risk stratification and individualized care crucial. Our goal was to develop machine learning (ML) models to predict LN flares. Methods: A total of 1546 SLE patients were enrolled from a hospital-based cohort. Electronic health record (EHR), single nucleotide polymorphism (SNP), and polygenic risk score (PRS) were combined to construct ML models. SHapley Additive exPlanation (SHAP) values were calculated to assess each feature’s contribution. Results: Within 5 years, 448 patients developed LN. Of the 686,354 SNPs, 375 were used for PRS computation. The model combining EHR, SNP, and PRS achieved the highest AUROC of 0.9512 and AUPRC of 0.8902 in validation, while the XGB-based hybrid model reached an AUPRC of 0.9021 in testing. The SHAP summary plot highlighted the top 20 features predicting LN flares. Conclusions: This hybrid model combining SNP, PRS, and EHR predicts active LN and requires validation.