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>CME/CNE ARTICLE: Severity Grading in Multiple Sclerosis: A Proposal.

ABSTRACT: Currently used classification schemes for multiple sclerosis (MS) have not taken into account disease severity, instead focusing on disease phenotype (ie, relapsing vs. progressive). In this article, we argue that disease severity adds a crucial dimension to the clinical picture and may help guide treatment decisions. We outline a practical, easy-to-implement, and comprehensive scheme for severity grading in MS put forward by our mentor, Professor Joseph Herbert. We believe that severity grading may help to better prognosticate individual disease course, formulate and test rational treatment algorithms, and enhance research efforts in MS.

SUBMITTER: Charlson R

PROVIDER: S-EPMC5087582 | biostudies-literature | 2016 Sep-Oct

REPOSITORIES: biostudies-literature

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>CME/CNE ARTICLE: Severity Grading in Multiple Sclerosis: A Proposal.

Charlson Robert R Herbert Joshua J Kister Ilya I

International journal of MS care 20160901 5

Currently used classification schemes for multiple sclerosis (MS) have not taken into account disease severity, instead focusing on disease phenotype (ie, relapsing vs. progressive). In this article, we argue that disease severity adds a crucial dimension to the clinical picture and may help guide treatment decisions. We outline a practical, easy-to-implement, and comprehensive scheme for severity grading in MS put forward by our mentor, Professor Joseph Herbert. We believe that severity grading ...[more]

PMID: 27803642

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Project description:ObjectiveTo optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.MethodsIn a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).ResultsThe EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)).ConclusionIncorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

Dataset Information

>CME/CNE ARTICLE: Severity Grading in Multiple Sclerosis: A Proposal.

Publications

>CME/CNE ARTICLE: Severity Grading in Multiple Sclerosis: A Proposal.

Similar Datasets

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