2 min read

ID: 78

Short Link: https://gregory-ms.com/articles/78/

Discovery Date: 25 February 2021, 11:11:00 UTC

Published Date: 2021-01-04 00:00:00

Source: PubMed

Link: https://pubmed.ncbi.nlm.nih.gov/33626237/?utm_source=Other&utm_medium=rss&utm_campaign=pubmed-2&utm_content=10guX6I3SqrbUeeLKSTD6FCRM44ewnrN2MKKTQLLPMHB4xNsZU&fc=20210216052009&ff=20210224192934&v=2.1

Manual Selection: true

Machine Learning Gaussian Naive Bayes Model: true

Abstract

Ann Clin Transl Neurol. 2021 Feb 24. doi: 10.1002/acn3.51324. Online ahead of print.

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.

PMID:33626237 | DOI:10.1002/acn3.51324

Noun Phrases in Title

  • electronic health records data
  • multiple sclerosis disease activity
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