Risk Score Prediction Model for Treatment Response in SLE Nephritis

Music City Center 

This study quantifies the predictive value of clinical, genetic, and molecular data in forecasting treatment responses in Systemic Lupus Erythematosus (SLE) nephritis through a data-agnostic, statistically rigorous modeling approach that integrates traditional regression-based methods and machine learning techniques. While existing risk models are limited and often lack thorough validation, this study expands upon them by incorporating a genetic-based risk score, allowing for a direct comparison of its contribution to predictive performance relative to a base model without genetic features. Model performance has been assessed using the corrected C-index and B-score across different specifications and covariate selections, with subgroup analyses evaluating variations in predictive accuracy. Internal validation was performed via bootstrapping, while external validation is ongoing with multicenter datasets. Multiple imputation techniques have addressed missing data, enhancing the robustness of findings and refining the predictive utility of clinical and genetic factors in treatment response.

Genetic Risk Score Modeling

Predictive Analysis

Internal & External Validation

Machine Learning Techniques 

Section on Statistics in Genomics and Genetics