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Acta Neurologica Belgica

07-11-2024 | Cerebral Ischemia | Review

The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients

Authors: Mohammad Amin Habibi, Farhang Rashidi, Ehsan Mehrtabar, Mohammad Reza Arshadi, Mohammad Sadegh Fallahi, Nikan Amirkhani, Bardia Hajikarimloo, Milad Shafizadeh, Shahram Majidi, Adam A. Dmytriw

Published in: Acta Neurologica Belgica

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Abstract

Background

Stroke is a leading cause of death and disability worldwide. Approximately one-third of patients with stroke experienced a second stroke. This study investigates the predictive value of machine learning (ML) algorithms for recurrent stroke.

Method

This study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. PubMed, Scopus, Embase, and Web of Science (WOS) were searched until January 1, 2024. The quality assessment of studies was conducted using the QUADAS-2 tool. The diagnostic meta-analysis was conducted to calculate the pooled sensitivity, specificity, diagnostic accuracy, positive and negative diagnostic likelihood ratio (DLR), diagnostic accuracy, diagnostic odds ratio (DOR), and area under of the curve (AUC) by the MIDAS package in STATA V.17.

Results

Twelve studies, comprising 24,350 individuals, were included. The meta-analysis revealed a sensitivity of 71% (95% CI 0.64–0.78) and a specificity of 88% (95% confidence interval (CI) 0.76–0.95). Positive and negative DLR were 5.93 (95% CI 3.05–11.55) and 0.33 (95% CI 0.28–0.39), respectively. The diagnostic accuracy and DOR was 2.89 (95% CI 2.32–3.46) and 18.04 (95% CI 10.21–31.87), respectively. The summary ROC curve indicated an AUC of 0.82 (95% CI 0.78–0.85).

Conclusion

ML demonstrates promise in predicting recurrent strokes, with moderate to high sensitivity and specificity. However, the high heterogeneity observed underscores the need for standardized approaches and further research to enhance the reliability and generalizability of these models. ML-based recurrent stroke prediction can potentially augment clinical decision-making and improve patient outcomes by identifying high-risk patients.
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Metadata
Title
The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients
Authors
Mohammad Amin Habibi
Farhang Rashidi
Ehsan Mehrtabar
Mohammad Reza Arshadi
Mohammad Sadegh Fallahi
Nikan Amirkhani
Bardia Hajikarimloo
Milad Shafizadeh
Shahram Majidi
Adam A. Dmytriw
Publication date
07-11-2024
Publisher
Springer International Publishing
Published in
Acta Neurologica Belgica
Print ISSN: 0300-9009
Electronic ISSN: 2240-2993
DOI
https://doi.org/10.1007/s13760-024-02682-y

Keynote series | Spotlight on menopause

Menopause can have a significant impact on the body, with effects ranging beyond the endocrine and reproductive systems. Learn about the broader systemic effects of menopause, so you can help patients in your clinics through the transition.

Launching: Thursday 12th December 2024
 

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