Leveraging electronic health records data to predict multiple sclerosis disease activity

Abstract

OBJECTIVE: No relapse risk prediction tool is currently available to guide treatment selection for multiple sclerosis (MS). Leveraging electronic health record (EHR) data readily available at the point of care, we developed a clinical tool for predicting MS relapse risk. METHODS: -regularized logistic regression (LASSO) to phenotype past 1-year relapse status from contemporaneous EHR data, (2) LASSO to predict future 1-year relapse risk using imputed prior 1-year relapse status and other algorithm-selected features. RESULTS: The final model, comprising age, disease duration, and imputed prior 1-year relapse history, achieved a predictive AUC and F score of 0.707 and 0.307, respectively. The performance was significantly better than the baseline model (age, sex, race/ethnicity, and disease duration) and noninferior to a model containing actual prior 1-year relapse history. The predicted risk probability declined with disease duration and age. CONCLUSION: Our novel machine-learning algorithm predicts 1-year MS relapse with accuracy comparable to other clinical prediction tools and has applicability at the point of care. This EHR-based two-stage approach of outcome prediction may have application to neurological disease beyond MS.

Ähnliche Arbeiten