Highlights
- •Data available at the time of admission may aid in stroke mortality prediction.
- •Machine learning has achieved great performance for stroke mortality prediction.
- •Age, high BMI and high NIHSS score are the most important predictors for mortality.
- •Deep learning has the potential to play an emerging role in stroke prognostication.
Abstract
Background and aims
Accurate prognostication of stroke may help in appropriate therapy and rehabilitation
planning. In the past few years, several machine learning (ML) algorithms were applied
for prediction of stroke outcomes. We aimed to examine the performance of machine
learning–based models for the prediction of mortality after stroke, as well as to
identify the most prominent factors for mortality.
Materials and methods
We searched MEDLINE/PubMed and Web of Science databases for original publications
on machine learning applications in stroke mortality prediction, published between
January 1, 2011, and October 27, 2022. Risk of bias and applicability were evaluated
using the tailored QUADAS-2 tool.
Results
Of the 1015 studies retrieved, 28 studies were included. Twenty-Five studies were
retrospective. The ML models demonstrated a favorable range of AUC for mortality prediction
(0.67–0.98). In most of the articles, the models were applied for short-term post
stroke mortality. The number of explanatory features used in the models to predict
mortality ranged from 5 to 200, with substantial overlap in the variables included.
Age, high BMI and high NIHSS score were identified as important predictors for mortality.
Almost all studies had a high risk of bias in at least one category and concerns regarding
applicability.
Conclusion
Using machine learning, data available at the time of admission may aid in stroke
mortality prediction. Notwithstanding, current research is based on few preliminary
works with high risk of bias and high heterogeneity. Thus, future prospective, multicenter
studies with standardized reports are crucial to firmly establish the usefulness of
the algorithms in stroke prognostication.
Keywords
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Article info
Publication history
Published online: December 20, 2022
Accepted:
December 18,
2022
Received in revised form:
November 30,
2022
Received:
July 21,
2022
Identification
Copyright
© 2022 Elsevier B.V. All rights reserved.
