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Leveraging electronic health records data to predict multiple sclerosis disease activity
Author(s) -
Ahuja Yuri,
Kim Nicole,
Liang Liang,
Cai Tianrun,
Dahal Kumar,
Seyok Thany,
Lin Chen,
Finan Sean,
Liao Katherine,
Savovoa Guergana,
Chitnis Tanuja,
Cai Tianxi,
Xia Zongqi
Publication year - 2021
Publication title -
annals of clinical and translational neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.824
H-Index - 42
ISSN - 2328-9503
DOI - 10.1002/acn3.51324
Subject(s) - medicine , logistic regression , electronic health record , disease , machine learning , lasso (programming language) , proportional hazards model , artificial intelligence , health care , computer science , economics , economic growth , world wide web
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 Using data from a clinic‐based research registry and linked EHR system between 2006 and 2016, we developed models predicting relapse events from the registry in a training set ( n  = 1435) and tested the model performance in an independent validation set of MS patients ( n  = 186). This iterative process identified prior 1‐year relapse history as a key predictor of future relapse but ascertaining relapse history through the labor‐intensive chart review is impractical. We pursued two‐stage algorithm development: (1) L 1 ‐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.

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