Stroke mortality prediction using machine learning: systematic review

Lihi Schwartz

Keywords: Stroke, Machine learning, Mortality prediction, Artificial intelligence, Mortality prognostic factors

Background:

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

Research questions:

1. Evaluation of the performance of machine learning–based models for predicting mortality after stroke.
2. What are the independent predictors of stroke outcomes, and can they be utilized in future models?

Method:

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

Conclusions:

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

Points for discussion:

Age, high BMI and high NIHSS score are the most important predictors for mortality attributed to stroke

Machine learning has achieved great performance for stroke mortality prediction

Deep learning has the potential to play an emerging role in stroke prognostication

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