Heart:基于社区电子健康记录对房颤事件的预测效能

2021-10-06 MedSci原创 MedSci原创

在基于社区的EHR中,用于预测AF事件的外部验证模型具有中等预测能力和较高的偏倚风险。新方法可以提供更强的判别性能。

心房颤动(AF)很常见,并且与卒中风险增加相关。

近日,心脏病领域权威杂志Heart上发表了一篇研究文章,研究人员旨在系统地评估和荟萃分析在电子健康记录(EHR)和/或行政索赔数据库中导出和/或验证的多变量预测模型,以预测社区中发生的AF。

研究人员从Ovid Medline和Ovid Embase中检索了2021年3月23日之前发表的研究,并通过贝叶斯荟萃分析提取和汇总指标的差异情况,并通过95%的预测区间(PI)评估异质性。研究人员使用偏倚风险评估工具的预测模型评估偏倚风险,并通过建议、评估、开发和评价分级对效果估计的确定性进行评估。

该分析共有11项研究符合纳入标准,描述了9种预测模型,其中4项符合纳入标准,包含9289959名患者。CHADS(充血性心力衰竭、高血压、年龄>75、糖尿病、既往卒中或短暂性脑缺血发作)(汇总c统计量为0.674;95%CI为0.610至0.732;95%PI为0.526-0.815)、CHA2DS2(心力衰竭、高血压、年龄>75岁(2分)、卒中/短暂性脑缺血发作/血栓栓塞(2分)、血管疾病、65-74岁、性别)(汇总c统计量为0.679;95%CI为0.620至0.736;95%PI为0.531-0.811)和HATCH(高血压、年龄、卒中或短暂性脑缺血发作、慢性阻塞性肺疾病、心力衰竭)(汇总c统计量为0.669;95%CI为0.600至0.732;95%PI为0.513-0.803)模型得到了具有统计显著性95%PI为和中等区分性能的c统计量。如果排除高偏倚风险的研究并且效应估计的确定性较“低”,则没有模型符合纳入荟萃分析的资格。机器学习衍生的模型具有很强的判别性能,但缺乏严格的外部验证。

由此可见,在基于社区的EHR中,用于预测AF事件的外部验证模型具有中等预测能力和较高的偏倚风险。新方法可以提供更强的判别性能。

原始出处:

Ramesh Nadarajah.et al.Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis.heart.2021.https://heart.bmj.com/content/early/2021/10/03/heartjnl-2021-320036

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    2021-10-08 zhaojie88
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    2021-10-08 slcumt
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    2021-10-07 老龙一点

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