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Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurol. | doi: 10.3389/fneur.2019.01106

Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event

 Durgesh Chaudhary1,  Vida Abedi2,  Jiang Li2, Clemens M. Schirmer1, Christoph J. Griessenauer1 and Ramin Zand1*
  • 1Neuroscience Institute, Geisinger Health System, United States
  • 2Biomedical and Translational Informatics Institute, Geisinger Health System, United States

Introduction:
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, ABCD2 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.

Keywords: Clinical risk scores, Recurrent Stroke Risk, Predictive Modeling, ischemic stroke, Predicting recurrence

Received: 07 Aug 2019; Accepted: 02 Oct 2019.

Copyright: © 2019 Chaudhary, Abedi, Li, Schirmer, Griessenauer and Zand. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Ramin Zand, Geisinger Health System, Neuroscience Institute, Danville, Pennsylvania, United States, rzand@geisinger.edu