死亡风险分层利用人工智能心电图用于心脏重症监护病房心脏病重症监护病人的高胆血症
David M Harmon1, Chris K Heinrich2, John J Dillon3
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
JACC. Advances
|October 7, 2024
概括
人工智能增强型心电图 (AI-ECG) 可以预测高血症,并对心脏重症监护室 (CICU) 患者的死亡风险进行分层,即使实验室水平正常.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 超血与心脏重症监护室 (CICU) 患者的死亡率增加有关.
- 应用于心电图 (ECG) 的人工智能 (AI) 算法在预测高血症和分层死亡风险方面表现有前途.
研究的目的:
- 测试AI-ECG算法用于高卡利米亚预测是否可以在超出标准实验室测量范围的CICU患者中分层死亡风险.
- 评估AI-ECG预测的高胆血症与患者结果之间的关联.
主要方法:
- 分析了一组11234名集中护理病房患者的队列,这些患者在入院时有可用的心电图和血清水平.
- 使用人工智能算法对心电图进行评估,以预测高血症 (定义为概率>0.5).
- 用逻辑回归,卡普兰-梅尔和考克斯分析分析了医院死亡率和1年生存率.
主要成果:
- AI-ECG算法预测了33.9%的患者患有高卡利米亚,而实验室确认的高卡利米亚 (K ≥5 mEq/L) 在12.9%的患者中存在.
- 与AI-ECG预测的高卡血病 (真阳性和假阳性) 和与真阴性相比假阴性预测的患者的住院死亡率明显高.
- 所有患有AI-ECG预测的高血症的组均呈现逐渐较低的1年生存率.
结论:
- 人工智能-心电图预测高卡血症与医院死亡率增加和急症监护室患者的1年生存率降低有关,不管实验室实际水平如何.
- 人工智能心电图分析为严重心脏病患者的快速个性化死亡风险分层提供了一个潜在的工具.
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