在一系列的预测地平线的发生性心房的风险计算器
Jianhua Wu1, Ramesh Nadarajah2, Yoko M Nakao3
1Wolfson Institute of Population Health, Queen Mary, University of London, UK.
American heart journal
|March 8, 2024
概括
新的模型准确地预测了10年内的心房动 (AF) 风险,有助于临床试验招生和对这种日益增长的心血管疾病的初级预防策略.
科学领域:
- 心脏病学 心脏病学
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 心房 (AF) 的负担越来越大,需要改善临床试验和初级预防的风险识别.
- 目前用于识别高风险个体的方法需要提高准确的长期预测.
研究的目的:
- 开发和验证预测模型,以识别患有心房动 (AF) 高风险的个体.
- 建立从6个月到10年的预测时间.
主要方法:
- 利用英国初级保健电子健康记录数据 (1998-2018) 来自超过200万没有已知的AF的个人.
- 开发了包含人口因素和并发症的后勤回归模型.
- 使用引导重新抽样验证模型,并将性能与CHA2DS2-VASc和C2HEST分数进行比较.
主要成果:
- 最佳风险模型包括年龄,性别,种族和8种并发症,在所有预测视野中显示出良好的到优秀的歧视和校准.
- 与现有的CHA2DS2-VASc和C2HEST分数相比,模型显示出更高的性能.
- 有一个基于网络的FIND-AF计算器可用.
结论:
- 开发的FIND-AF模型在短期和长期预测心房动风险方面表现出很高的准确性.
- 需要进一步的研究来证实这些模型在指导AF查和初级预防试验招生临床决策中的实用性.
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