Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event

Abstract

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.

Pharmacist intervention and anti‐platelet medication monitoring in patients following stroke and transient ischemic attack

“…Traditionally, the ABCD 2 score has been used to predict ischemic stroke after TIA only. 42 While patient groups were well-match in terms of most characteristics measured, including age, sex, and ABCD 2 score, differences existed between the two groups in type of qualifying event (more strokes in non-PFT group vs more TIAs in PFT group) and prevalence of diabetes and PAD (both higher in PFT group). Furthermore, the impact of PFT cannot be distinguished from the impact of the pharmacist in the context of this study, because PFT was always accompa-…”

Prediction of Long-Term Stroke Recurrence Using Machine Learning Models

“…To date several predictive models of recurrent stroke, using regression or other statistical methods, have been developed; however, the clinical utility of these models tends to be limited due to the narrow scope of variables used in these models [ 6 ]. In a recent study, multivariable logistic models of 1-year stroke recurrence, developed based on 332 patients, using clinical and retinal characteristics (using 20 variables) have shown promising results with an area under the receiver operating characteristic (AUROC) curve of 0.71–0.74 [ 7 ].…”

Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework

“…[17][18][19] Scoring systems for the diagnosis of stroke and recurrent stroke do not have a high sensitivity to diagnose the posterior circulation stroke. 20,21 Furthermore, these tools are also not automatic, and require that the physicians suspect stroke as a differential diagnosis to apply the scoring system. Artificial intelligence (AI), a computational framework meaning to emulate human insight, is one of the most transformative technologies.…”