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dc.contributor.authorChaudhary, Durgeshen
dc.contributor.authorAbedi, Vidaen
dc.contributor.authorLi, Jiangen
dc.contributor.authorSchirmer, Clemens M.en
dc.contributor.authorGriessenauer, Christoph J.en
dc.contributor.authorZand, Raminen
dc.date.accessioned2020-02-06T18:02:05Z
dc.date.available2020-02-06T18:02:05Z
dc.date.issued2019-11-12en
dc.identifier.issn1664-2295en
dc.identifier.other1106en
dc.identifier.urihttp://hdl.handle.net/10919/96745
dc.description.abstractIntroduction: Recurrent stroke has a higher rate of death and disability. A number of risk scores have been developed to predict short-term and long-term risk of stroke following an initial episode of stroke or transient ischemic attack (TIA) with limited clinical utilities. In this paper, we review different risk score models and discuss their validity and clinical utilities. Methods: The PubMed bibliographic database was searched for original research articles on the various risk scores for risk of stroke following an initial episode of stroke or TIA. The validation of the models was evaluated by examining the internal and external validation process as well as statistical methodology, the study power, as well as the accuracy and metrics such as sensitivity and specificity. Results: Different risk score models have been derived from different study populations. Validation studies for these risk scores have produced conflicting results. Currently, ABCD(2) score with diffusion weighted imaging (DWI) and Recurrence Risk Estimator at 90 days (RRE-90) are the two acceptable models for short-term risk prediction whereas Essen Stroke Risk Score (ESRS) and Stroke Prognosis Instrument-II (SPI-II) can be useful for prediction of long-term risk. Conclusion: The clinical risk scores that currently exist for predicting short-term and long-term risk of recurrent cerebral ischemia are limited in their performance and clinical utilities. There is a need for a better predictive tool which can overcome the limitations of current predictive models. Application of machine learning methods in combination with electronic health records may provide platform for development of new-generation predictive tools.en
dc.description.sponsorshipGeisinger Health Plan Quality; National Institute of Health (NIH)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R56HL116832]; Defense Threat Reduction Agency (DTRA)United States Department of DefenseDefense Threat Reduction Agency [HDTRA118-1-0008, 450557-19D03]en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectclinical risk scoresen
dc.subjectrecurrent stroke risken
dc.subjectpredictive modelingen
dc.subjectischemic strokeen
dc.subjectpredicting recurrenceen
dc.titleClinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Eventen
dc.typeArticle - Refereeden
dc.description.notesThis study was partially funded by Geisinger Health Plan Quality awarded to RZ. VA was partly supported by the National Institute of Health (NIH) Grant No. R56HL116832 to Sutter Health and sub-awarded to VA (Sub-PI, Geisinger) as well as funds from the Defense Threat Reduction Agency (DTRA) Grant No. HDTRA118-1-0008 to Virginia Tech and sub-awarded to VA (Sub-PI, Geisinger, sub-award No. 450557-19D03). The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.en
dc.title.serialFrontiers in Neurologyen
dc.identifier.doihttps://doi.org/10.3389/fneur.2019.01106en
dc.identifier.volume10en
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen
dc.identifier.pmid31781015en


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International