门诊心电图数据的性能用于预测中风和心力衰竭事件
Hannah T Schwennesen1, Zhen Li2, Bradley G Hammill2,3
1Cardiac Electrophysiology Section, Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.
JACC. Advances
|November 5, 2024
概括
整合长期持续监测 (LTCM) 数据显著改善了心力衰竭和中风的心血管风险预测. LTCM揭示,心房动和心室外皮严重预测心力衰竭事件.
科学领域:
- 心脏病学 心脏病学
- 医疗技术 医疗技术 医学技术
- 数据科学数据科学数据科学
背景情况:
- 临床风险评分目前不包括从长期连续监测 (LTCM) 获得的个人心律失常数据.
- 心律不整的负担和心血管风险之间存在明显的关联.
- 来自LTCM的个体心律失常特征对于准确的风险预测至关重要.
研究的目的:
- 评估风险模型,将LTCM数据和患者声称用于预测心力衰竭 (HF) 和缺血性中风.
- 通过使用门诊心电图变量和CHA2DS2-VASc评分组件来评估预测模型的性能.
主要方法:
- 对320,974名接受ZioXT门诊监测的医疗保险受益人的回顾性分析.
- 从最多14天的LTCM心电图 (ECG) 数据中提取的特征与患者索赔相关.
- 使用LASSO Cox回归用于变量选择,以创建HF住院,中风住院和新发性HF的预测模型.
主要成果:
- 结合CHA2DS2-VASc和LTCM心电图变量的一种模型表明,与单独CHA2DS2-VASc (0.73) 相比,HF住院的歧视优越 (C统计值0.85).
- 增强模型的性能与CHA2DS2-VASc对于中风住院预测 (0.75对0.71) 的性能相似.
- 在LTCM治疗中,心房动和早发性心室配对显著与HF住院和中风风险增加有关.
结论:
- CHA2DS2-VASc得分对中风和HF事件具有适度的预测能力.
- 结合LTCM ECG共变量可以显著提高预测准确度.
- 通过14天的LTCM检测到的心房动和心室外皮是HF事件的强有力的预测因素.
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