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 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) L1‐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.

Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis

“…To fill the knowledge gap due to the absence of randomized clinical trials comparing commonly prescribed DMTs, this study contributes to the growing literature leveraging real-world evidence. Two important aspects differentiate our study from prior observational studies of comparative effectiveness of DMTs in MS. First, building on our group’s prior works integrating EHR and registry data, 25 , 26 , 27 , 28 this study incorporated within the high-dimensional models the additional confounders from the EHR data that are not typically available in registry data. Our comparison of natalizumab and rituximab particularly illustrated the utility of incorporating the high-dimensional EHR features to balance patient characteristics.…”

The Application of Projection Word Embeddings on Medical Records Scoring System

“…Because the medical record is the historical record of the patient’s health care; it is also the basis of care, and its content records the patient’s condition during the care process, the reason and result of the inspection, and the treatment method and result. In recent studies, it is feasible to use electronic health records (EHR) to predict disease risk, such as atrial fibrillation (AF) [ 49 ], coronary heart disease in patients with hypertension [ 50 ], fall risk [ 51 ], multiple sclerosis disease [ 52 ], and cervical cancer [ 53 ]. Over the past two decades, the investigation of genetic variation underlying disease susceptibility has increased considerably.…”

Validation of a machine learning approach to estimate expanded disability status scale scores for multiple sclerosis

“…Machine learning offers a new approach to addressing the lack of disease activity scores captured in real-world data sources. 5,6 Machine learning models have been developed to predict MS relapse risk, 7 disease progression, 8 disability progression, 9 and EDSS scores at a future timepoints using clinical and other data. 10–12 These efforts highlight the potential of machine learning approaches in MS and demonstrate that it is feasible to predict EDSS scores using routinely recorded clinical data.…”