一种新型人工智能技术的成本效益,用于量化冠状动脉炎症和心血管风险,在接受常规冠状动脉计算机断层扫描血管造影的患者中
Apostolos Tsiachristas1, Kenneth Chan2, Elizabeth Wahome2
1Nuffield Department of Primary Care Health Sciences & Department of Psychiatry, University of Oxford, Oxford, United Kingdom.
European heart journal. Quality of care & clinical outcomes
|September 28, 2024
概括
将人工智能增强风险 (AI-Risk) 评估集成到冠状动脉计算机断层扫描血管学 (CCTA) 中,可以改善心脏事件的预测,并且具有成本效益. 这种人工智能工具完善了风险分层,以便在怀疑冠状动脉疾病 (CAD) 时更好地进行医疗管理.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 卫生经济学 卫生经济学
背景情况:
- 冠状动脉计算机断层扫描血管图 (CCTA) 是诊断胸痛患者冠状动脉阻塞性疾病 (CAD) 的主要工具.
- 即使没有阻塞性CAD,急性心脏事件也经常发生,这突显了当前风险评估的局限性.
- 需要新的方法来改善风险分层,超越传统的阻塞性CAD检测.
研究的目的:
- 评估人工智能增强图像分析算法 (AI-Risk) 的终身成本效益,以分层心脏事件风险.
- 评估AI-Risk使用常规CCTA数据量化冠状动脉炎症,斑块程度和临床风险因素的能力.
- 为了确定AI-风险能否改善医疗管理并减少心脏不良事件.
主要方法:
- 一个混合决策树和一个人口队列马尔科夫模型是使用3,393名患者的数据开发的,随访时间中位数为7.7年.
- 前性评估涉及744名患者 (胸痛调查) 和1214名患者 (心血管风险概况),以评估AI-Risk对治疗开始的影响.
- 该模型根据AI-风险指南预测了终身成本和结果,包括主要的不良心脏事件.
主要成果:
- 人工智能风险评估导致45%接受CCTA治疗胸痛的患者开始或加强治疗.
- 在一个更广泛的风险分析队列中,AI-Risk导致39%的治疗变化超出了当前指南.
- 终身建模预测,人工智能风险导向治疗的心肌梗塞 (11%),缺血性中风 (4%),心力衰竭 (4%) 和心脏病死亡 (12%) 的相对显著减少.
- 增量成本效益比 (ICER) 是有利的 (1371-3244英),表明了成本效益.
结论:
- 将AI风险评估添加到常规的CCTA解释中是具有成本效益的.
- 人工智能风险改进了以风险为导向的医疗管理,与标准护理相比改善了结果.
- 实施AI-Risk既有利于当前的指导方针,也有利于没有阻塞性CAD的患者.
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