社区心力衰竭的预测模型:系统性审查和元分析
Ramesh Nadarajah1,2,3, Tanina Younsi3, Elizabeth Romer3
1Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
European journal of heart failure
|July 5, 2023
概括
多变量预测模型在估计心力衰竭风险方面表现强. 然而,由于高偏差和有效性证据有限,它们的临床实用性是不确定的.
科学领域:
- 心血管医学 心血管医学
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 多变量预测模型对于估计一般人群中发生心力衰竭 (HF) 风险至关重要.
- 进行了系统性审查和元分析,以评估这些高频预测模型的性能.
研究的目的:
- 系统地审查和元分析多变量预测模型在社区基队伍中发生心力衰竭 (HF) 的表现.
- 评估区分能力,并确定高频风险预测的高性能模型.
主要方法:
- 搜索了MEDLINE和EMBASE数据库从创立到2022年11月的相关研究.
- 使用贝叶斯元分析对来自≥3个队列的模型进行聚合歧视措施 (c-统计),以95%预测间隔 (PI) 评估异质性.
- 使用PROBAST评估偏差风险,包括36项研究和59个预测模型.
主要成果:
- 五种模型显示出优异的歧视:社区动脉样硬化风险 (ARIC) 风险评分 (0.802),基于GRaph的注意力模型 (GRAM; 0.791),聚合队列方程以预防心力衰竭 (PCP-HF) 白人 (0.820),PCP-HF 白人女性 (0.852),和反向时间注意力IoN模型 (RETAIN; 0.839).
- ARIC和PCP-HF模型在具有统一预测窗口的队列中显示出显著的歧视.
- 发现偏差风险高 (77%) 和证据确定性低;没有模型包括临床影响研究.
结论:
- 基于社区的高频预测模型表现出极好的歧视性.
- 这些模型的临床有用性是不确定的,原因是存在重大偏差风险,证据确定性低,缺乏临床有效性研究.
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