Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 23, 2025
Open Peer Review Period: Jan 23, 2025 - Mar 20, 2025
Date Accepted: Apr 22, 2025
(closed for review but you can still tweet)
Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis
ABSTRACT
Background:
Background:
Hemorrhagic transformation (HT) is commonly detected in acute ischemic stroke (AIS), and often leads to poor outcomes. Currently, there is no ideal tool for early prediction of HT risk. Recently, machine learning has gained traction in stroke management, prompting the exploration of predictive models for HT. However, systematic evidence on these models is lacking. Therefore, we assessed the predictive capability of machine learning models for HT risk in AIS, aiming to inform the development of HT prediction tools.
Objective:
Hemorrhagic transformation (HT) is commonly detected in acute ischemic stroke (AIS), and often leads to poor outcomes. Currently, there is no ideal tool for early prediction of HT risk. Recently, machine learning has gained traction in stroke management, prompting the exploration of predictive models for HT. However, systematic evidence on these models is lacking. Therefore, we assessed the predictive capability of machine learning models for HT risk in AIS, aiming to inform the development of HT prediction tools.
Methods:
Methods:
We conducted a thorough search of medical databases, such as Web of Science, Embase, Cochrane, and PubMed up until October 2023. The risk of bias was determined through Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analysis was performed based on treatment backgrounds, diagnostic criteria, and types of HT.
Results:
Results:
Sixty eligible articles were included, containing 69 models and 68678 AIS patients with 6977 HT cases. There were 72 validation sets with a total c-index of 0.822 (95%CI: 0.800-0.844), sensitivity of 0.82 (95%CI: 0.79-0.85), and specificity of 0.77 (95%CI: 0.71-0.82). Subgroup analysis indicated that the combined model achieved superior prediction accuracy. Moreover, we also analyzed the predictive performance of six mature models.
Conclusions:
Conclusions:
Currently, although several prediction methods for HT have been developed, their predictive values are not satisfactory. Fortunately, our findings suggest that machine learning methods, particularly those combining clinical features and radiomics, hold promise for improving the predictive accuracy. Our meta-analysis may provide evidence-based guidance for subsequent development of more efficient clinical predictive models for HT. Clinical Trial: Trial Registration: Not applicable
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