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基于社区的电子健康记录中房颤事件的预测:系统评价与荟萃分析。

Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis.

机构信息

Leeds Institute of Data Analytics, University of Leeds, Leeds, UK

Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.

出版信息

Heart. 2022 Jun 10;108(13):1020-1029. doi: 10.1136/heartjnl-2021-320036.

Abstract

OBJECTIVE

Atrial fibrillation (AF) is common and is associated with an increased risk of stroke. We aimed to systematically review and meta-analyse multivariable prediction models derived and/or validated in electronic health records (EHRs) and/or administrative claims databases for the prediction of incident AF in the community.

METHODS

Ovid Medline and Ovid Embase were searched for records from inception to 23 March 2021. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation.

RESULTS

Eleven studies met inclusion criteria, describing nine prediction models, with four eligible for meta-analysis including 9 289 959 patients. The CHADS (Congestive heart failure, Hypertension, Age>75, Diabetes mellitus, prior Stroke or transient ischemic attack) (summary c-statistic 0.674; 95% CI 0.610 to 0.732; 95% PI 0.526-0.815), CHADS-VASc (Congestive heart failure, Hypertension, Age>75 (2 points), Stroke/transient ischemic attack/thromboembolism (2 points), Vascular disease, Age 65-74, Sex category) (summary c-statistic 0.679; 95% CI 0.620 to 0.736; 95% PI 0.531-0.811) and HATCH (Hypertension, Age, stroke or Transient ischemic attack, Chronic obstructive pulmonary disease, Heart failure) (summary c-statistic 0.669; 95% CI 0.600 to 0.732; 95% PI 0.513-0.803) models resulted in a c-statistic with a statistically significant 95% PI and moderate discriminative performance. No model met eligibility for inclusion in meta-analysis if studies at high risk of bias were excluded and certainty of effect estimates was 'low'. Models derived by machine learning demonstrated strong discriminative performance, but lacked rigorous external validation.

CONCLUSIONS

Models externally validated for prediction of incident AF in community-based EHR demonstrate moderate predictive ability and high risk of bias. Novel methods may provide stronger discriminative performance.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021245093.

摘要

目的

心房颤动(AF)很常见,与中风风险增加有关。我们旨在系统地综述和荟萃分析源自电子健康记录(EHR)和/或行政索赔数据库并针对社区中发生 AF 进行预测的多变量预测模型。

方法

从 Ovid Medline 和 Ovid Embase 检索记录,检索时间从开始到 2021 年 3 月 23 日。通过贝叶斯荟萃分析提取并汇总判别措施,并通过 95%预测区间(PI)评估异质性。使用预测模型风险偏倚评估工具(Prediction model Risk Of Bias ASsessment Tool)和推荐评估、制定和评估(Grading of Recommendations, Assessment, Development and Evaluation)评估效果估计的确定性来评估偏倚风险。

结果

符合纳入标准的 11 项研究描述了 9 个预测模型,其中 4 个符合荟萃分析条件,包括 9289959 名患者。CHADS(充血性心力衰竭、高血压、年龄>75 岁、糖尿病、既往中风或短暂性脑缺血发作)(汇总 c 统计量 0.674;95%CI 0.610 至 0.732;95%PI 0.526 至 0.815)、CHADS-VASc(充血性心力衰竭、高血压、年龄>75 岁(2 分)、中风/短暂性脑缺血发作/血栓栓塞(2 分)、血管疾病、年龄 65-74 岁、性别)(汇总 c 统计量 0.679;95%CI 0.620 至 0.736;95%PI 0.531 至 0.811)和 HATCH(高血压、年龄、中风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 0.669;95%CI 0.600 至 0.732;95%PI 0.513 至 0.803)模型的 c 统计量具有统计学意义的 95%PI,具有中等判别性能。如果排除高偏倚风险的研究,没有模型符合纳入荟萃分析的条件,并且效果估计的确定性为“低”。基于机器学习的模型表现出很强的判别性能,但缺乏严格的外部验证。

结论

针对基于社区的 EHR 中发生 AF 进行预测的外部验证模型显示出中等的预测能力和高偏倚风险。新方法可能提供更强的判别性能。

系统综述注册

PROSPERO CRD42021245093。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7dff/9209680/3137efa7b72c/heartjnl-2021-320036f01.jpg

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