Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis.

IF 5.8 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research Pub Date : 2025-05-23 DOI:10.2196/71654

Benqiao Wang, Bohao Jiang, Dan Liu, Ruixia Zhu

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引用次数: 0

Abstract

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.

Objective: In this study, we assessed the predictive capability of machine learning models for HT risk in AIS, aiming to inform the development of HT prediction tools.

Methods: We conducted a thorough search of medical databases, such as Web of Science, Embase, Cochrane, and PubMed up until March 2025. The risk of bias was determined through the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analysis was performed based on treatment backgrounds, diagnostic criteria, and types of HT.

Results: A total of 83 eligible articles were included, containing 106 models and 88,197 patients with AIS with 9323 HT cases. There were 104 validation sets with a total c-index of 0.832 (95% CI 0.814-0.849), sensitivity of 0.82 (95% CI 0.79-0.84), and specificity of 0.78 (95% CI 0.74-0.81). Subgroup analysis indicated that the combined model achieved superior prediction accuracy. Moreover, we also analyzed the predictive performance of 6 mature models.

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 predictive accuracy. Our meta-analysis may provide evidence-based guidance for the subsequent development of more efficient clinical predictive models for HT.

Trial registration: PROSPERO CRD42024498997; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024498997.

查看原文本刊更多论文

机器学习对急性缺血性卒中出血转化的早期预测准确性：系统回顾和荟萃分析。

背景：出血性转化（HT）在急性缺血性卒中（AIS）中很常见，往往导致预后不良。目前，尚无理想的早期预测HT风险的工具。最近，机器学习在脑卒中管理中获得了关注，促使人们探索脑卒中的预测模型。然而，这些模型缺乏系统的证据。目的：在本研究中，我们评估了机器学习模型对AIS中HT风险的预测能力，旨在为HT预测工具的开发提供信息。方法：对截至2025年3月的Web of Science、Embase、Cochrane、PubMed等医学数据库进行全面检索。通过预测模型偏倚风险评估工具（PROBAST）确定偏倚风险。根据治疗背景、诊断标准和HT类型进行亚组分析。结果：共纳入83篇符合条件的文献，共纳入106个模型，88197例AIS患者，其中9323例HT。共有104个验证集，总c指数为0.832 (95% CI 0.814-0.849)，敏感性为0.82 (95% CI 0.79-0.84)，特异性为0.78 （95% CI 0.74-0.81）。亚组分析表明，组合模型具有较好的预测精度。此外，我们还分析了6个成熟模型的预测性能。结论：目前，虽然开发了几种高温天气的预测方法，但其预测值并不令人满意。幸运的是，我们的研究结果表明，机器学习方法，特别是那些结合临床特征和放射组学的方法，有望提高预测的准确性。我们的荟萃分析可能为后续开发更有效的HT临床预测模型提供循证指导。试验注册：PROSPERO CRD42024498997；https://www.crd.york.ac.uk/PROSPERO/view/CRD42024498997。

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来源期刊

Journal of Medical Internet Research 医学-卫生保健

CiteScore

14.40

自引率

5.40%

发文量

654

审稿时长

1 months

期刊介绍： The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.