机器学习对急性缺血性中风中的出血转变的早期预测准确性:系统审查和元分析
Benqiao Wang1, Bohao Jiang2, Dan Liu1
1Department of Neurology, First Hospital of China Medical University, Shenyang, China.
Journal of medical Internet research
|May 23, 2025
概括
机器学习模型显示出预测急性缺血性中风 (AIS) 中的出血转换 (HT) 的前景. 结合临床特征和放射学可以提高预测准确性,以获得更好的患者结果.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 出血转化 (HT) 是急性缺血性中风 (AIS) 的常见并发症,与预后不佳有关.
- 目前用于早期HT风险预测的工具不足.
- 机器学习 (ML) 正在成为在AIS中预测HT的潜在工具.
研究的目的:
- 系统地评估ML模型在AIS中对HT风险的预测性能.
- 确定影响基于ML的HT预测准确性的因素.
- 为开发改进的HT预测工具提供基于证据的指导.
主要方法:
- 在主要医疗数据库 (PubMed,Embase,Web of Science,Cochrane) 进行了全面的文献搜索,截至2025年3月.
- 预测模型偏差风险评估工具 (PROBAST) 用于评估研究质量.
- 根据治疗,诊断标准和HT类型进行了子组分析.
主要成果:
- 包括83项研究,包括106个ML模型和88,197名AIS患者 (9,323名HT患者) 的数据.
- 整体聚合性能:c指数为0.832,灵敏度为0.82,特异性为0.78.
- 结合了利用临床特征和放射学模型的组合模型,证明了卓越的预测准确性.
结论:
- 现有的HT预测方法在预测价值方面存在局限性.
- ML模型,特别是那些整合临床数据和放射学模型,为提高AIS中的HT风险预测提供了显著的潜力.
- 这一元分析为开发更有效的临床HT预测工具提供了基础.
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