Risk of bias assessment of post-stroke mortality machine learning predictive models: Systematic review

Background

Stroke is a major cause of mortality and permanent disability worldwide. Precise prediction of post-stroke mortality is essential for guiding treatment decisions and rehabilitation planning. The ability of Machine learning models to process large amounts of data, offer a promising alternative for improving mortality prediction in stroke patients. In this review, we aim to evaluate the risk of bias in different machine learning models used for predicting post-stroke mortality.

Methods

This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Relevant articles were retrieved from Cochrane Library, Scopus, PubMed, and Web of Science databases.

Results

A total of 9 studies were included, with an aggregate patient population of 669,424. Six studies used publicly available datasets, and four used hospital data with a follow up duration ranging from 7 days to 18 months. The range of area under the curve (AUC) for mortality prediction across the studies ranged from 0.81 to 0.95. All studies were determined to have a high overall risk of bias.

Conclusion

Machine learning models demonstrated great potential in predicting post-stroke mortality. However, implementation of these models in clinical practice is limited by high risk of bias. Future studies should focus on reducing this bias and enhancing the applicability of these models to improve the reliability of stroke mortality predictions.