长期主要不良心脏事件预测通过计算机断层扫描衍生的斑块测量和使用机器学习的临床参数
Shinichi Wada1,2, Makino Sakuraba3, Michikazu Nakai1,4
1Department of Medical and Health Information Management, National Cerebral and Cardiovascular Center, Japan.
Internal medicine (Tokyo, Japan)
|September 4, 2024
概括
机器学习 (ML) 随机森林模型在使用冠状动脉计算机断层扫描成像和临床数据预测主要不良心脏事件 (MACE) 方面表现有前途. 然而,需要外部验证,因为内部发现没有完全翻译.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 预测主要心脏不良事件 (MACE) 对于管理疑似冠状动脉疾病的患者至关重要.
- 机器学习 (ML) 提供了比传统方法更好的风险分层的潜力.
研究的目的:
- 评估ML模型,特别是随机森林 (RF) 在预测MACE的有效性.
- 使用冠状动脉计算机断层成像和临床数据,将ML-RF模型的预测性能与后勤回归进行比较.
主要方法:
- 使用全国性性别特定的动脉样硬化决定因素估计和缺血性心血管疾病前性队列 (NADESICO) 研究数据集 (1,187名患者).
- 开发并比较了一种ML-RF模型与MACE预测的后勤回归.
- 使用曲线下的面积 (AUC) 和95%置信区间 (CI) 评估模型性能.
- 进行了外部验证,以测试模型的通用性.
主要成果:
- 与物流回归 (0.750) 相比,ML-RF模型在MACE预测方面实现了更高的AUC (0.781).
- 由射频模型确定的关键预测因素包括冠状动脉狭窄 (CAS) 的程度和位置,HbA1c和性别.
- 外部验证显示模型精度降低 (AUC:0.635),表明缺乏可通用性.
结论:
- 与物流回归相比,ML-RF模型显示了长期MACE的优异内部预测性能.
- 模型的内部数据集中的预测变量没有有效地转化为外部验证队列.
- 需要进一步的研究来验证和完善ML模型,以便在不同患者群体中进行可靠的MACE预测.
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