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心肌梗死后心源性猝死:来自汇总队列的个体参与者数据。

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts.

机构信息

Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

The Healthcare Improvement Studies Institute (THIS Institute), Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

出版信息

Eur Heart J. 2024 Nov 14;45(43):4616-4626. doi: 10.1093/eurheartj/ehae326.

Abstract

BACKGROUND AND AIMS

Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation.

METHODS

The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal-external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis.

RESULTS

There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor.

CONCLUSIONS

More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors.

摘要

背景和目的

心肌梗死后心源性猝死的风险分层和通过除颤器进行预防依赖于左心室射血分数(LVEF)。需要在整个 LVEF 范围内进行改进的风险分层,以便做出是否植入除颤器的决策。

方法

该分析汇总了 20 个数据集,包含 140204 例心肌梗死后患者的人口统计学、病史、临床特征、生物标志物、心电图、超声心动图和心脏磁共振成像信息。分别在以下患者中进行了单独分析:(i)携带 LVEF≤35%的一级预防心脏转复除颤器(植入式心脏转复除颤器[ICD]患者);(ii)无 ICD 的 LVEF≤35%(非 ICD 患者≤35%);(iii)无 ICD 的 LVEF>35%(非 ICD 患者>35%)。主要结局为心源性猝死或在除颤器携带者中,恰当的除颤器治疗。使用竞争风险框架和系统内-外部交叉验证,开发并外部验证了仅使用 LVEF、多变量灵活参数生存模型和多变量随机森林生存模型的模型。通过随机效应荟萃分析评估预测性能。

结果

在平均随访 30.0、46.5 和 57.6 个月时,7543 例 ICD 患者中有 1326 例出现主要结局,25058 例非 ICD 患者≤35%中有 1193 例,107603 例非 ICD 患者>35%中有 1567 例。在这三个亚组中,LVEF 对心源性猝死的预测能力较差(C 统计量在 0.50 到 0.56 之间)。考虑到其他参数并不能提高校准和区分度,而且模型的通用性也很差。

结论

对于 LVEF 严重降低或 LVEF 保留的高危个体,无法使用 LVEF 或其他预测指标更准确地对心源性猝死进行风险分层并识别低危个体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/810a/11560274/85df8443a70c/ehae326_ga.jpg

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