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Predicting Clinical Outcomes in Acute Ischemic Stroke Patients Undergoing Endovascular Thrombectomy with Machine Learning

A Systematic Review and Meta-analysis

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Abstract

Purpose

Conventional predictive models are based on a combination of clinical and neuroimaging parameters using traditional statistical approaches. Emerging studies have shown that the machine learning (ML) prediction models with multiple pretreatment clinical variables have the potential to accurately prognosticate the outcomes in acute ischemic stroke (AIS) patients undergoing thrombectomy, and hence identify patients suitable for thrombectomy. This article summarizes the published studies on ML models in large vessel occlusion AIS patients undergoing thrombectomy.

Methods

We searched electronic databases including PubMed from 1 January 2000 up to 14 October 2019 for studies that evaluated ML algorithms for the prediction of outcomes in stroke patients undergoing thrombectomy. We then used random-effects bivariate meta-analysis models to summarize the studies.

Results

We retained a total of five studies that evaluated ML (4 support vector machine, 1 decision tree model) with a combined cohort of 802 patients. The prevalence of good functional outcome defined by 90-day mRS of 0–2 when available. Random effects model demonstrated that the AUC was 0.846 (95% confidence interval, CI 0.686–0.902). A pooled diagnostic odds ratio of 12.6 was computed. The pooled sensitivity and specificity were 0.795 (95% CI 0.651–0.889) and 0.780 (95% CI 0.634–0.879), respectively.

Conclusion

ML may be useful as an adjunct to clinical assessment to predict functional outcomes in AIS patients undergoing thrombectomy, and hence identify suitable patients for treatment. Further studies validating ML models in large multicenter cohorts are necessary to explore this further.

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Abbreviations

AIS:

Acute ischemic stroke

ASPECTS:

Alberta Stroke Program Early CT Score

DC:

Discharge

DOR:

Diagnostic odds ratio

EVT:

Endovascular thrombectomy

ML:

Machine learning

mRS:

Modified Rankin score

NIHSS:

National Institutes of Health Stroke Scale

NPV:

Negative predictive value

PICOS:

Population, intervention, comparison, outcome, and study design

PPV:

Positive predictive value

PRISMA:

Preferred reporting items of systematic reviews and meta-analyses

ROC:

Receiver operator curves

SVM:

Support vector machine

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Authors and Affiliations

  1. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Yao Hao Teo, Isis Claire Z. Y. Lim, Fan Shuen Tseng, Yao Neng Teo, Cheryl Shumin Kow, Zi Hui Celeste Ng, Nyein Chan Ko Ko, Ching-Hui Sia, Aloysius S. T. Leow, Wesley Yeung, Vijay K. Sharma, Leonard L. L. Yeo & Benjamin Y. Q. Tan

  2. Department of Neurology, Detroit Medical Centre, Michigan, USA

    Wan Yee Kong

  3. Division of Neurology, Department of Medicine, National University Health System, 1E Kent Ridge Road, NUHS Tower Block Level 11, 119228, Singapore, Singapore

    Bernard P. L. Chan, Vijay K. Sharma, Leonard L. L. Yeo & Benjamin Y. Q. Tan

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  1. Yao Hao Teo
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Contributions

YHT, ICZYL, TFS, LLLY, and BYQT designed the study and developed the study protocol and tools. YHT, ICZYL, TFS, YNT, CSK, ZHCN, and NCKK were responsible for data collection. YHT, ICZYL, TFS, LLLY, and BYQT analyzed data and wrote the manuscript. All authors contributed to the conceptualization of the research questions, interpretation of the results, and manuscript writing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Leonard L. L. Yeo.

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Conflict of interest

Y.H. Teo, I.C.Z.Y. Lim, F.S. Tseng, Y.N. Teo, C.S. Kow, Z.H.C. Ng, N. Chan Ko Ko, C.-H. Sia, A.S.T. Leow, W. Yeung, W.Y. Kong, B.P.L. Chan, V.K. Sharma, L.L.L. Yeo and B.Y.Q. Tan declare that they have no competing interests.

Additional information

The authors Y.H. Teo and I.C.Z.Y. Lim contributed equally to this work.

Availability of data and material

Data used for this study can be accessed upon request from the principal investigator (Dr. Benjamin YQ Tan) at benjaminyqtan@gmail.com.

Code availability

R version 3.6.2 (R Foundation)

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The abstract of this paper has been accepted for presentation at the European Stroke Organisation-World Stroke Organization (ESO-WSO) Virtual Conference.

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Teo, Y.H., Lim, I.C.Z.Y., Tseng, F.S. et al. Predicting Clinical Outcomes in Acute Ischemic Stroke Patients Undergoing Endovascular Thrombectomy with Machine Learning. Clin Neuroradiol 31, 1121–1130 (2021). https://doi.org/10.1007/s00062-020-00990-3

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  • DOI: https://doi.org/10.1007/s00062-020-00990-3

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