Predicting long-term risk of sudden cardiac death with automatic computer-interpretations of electrocardiogram.

IF 2.8 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Frontiers in Cardiovascular Medicine Pub Date : 2024-10-23 eCollection Date: 2024-01-01 DOI:10.3389/fcvm.2024.1439069
Minna Järvensivu-Koivunen, Antti Kallonen, Mark van Gils, Leo-Pekka Lyytikäinen, Juho Tynkkynen, Jussi Hernesniemi
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Abstract

Background: Computer-interpreted electrocardiogram (CIE) data is provided by almost all commercial software used to capture and store digital electrocardiograms. CIE is widely available, inexpensive, and accurate. We tested the potential of CIE in long-term sudden cardiac death (SCD) risk prediction.

Methods: This is a retrospective of 8,568 consecutive patients treated for acute coronary syndrome. The primary endpoint was five-year occurrence of SCDs or equivalent events (SCDs aborted by successful resuscitation or adequate ICD therapy). CIE statements were extracted from summary statements and measurements made by the GE Muse 12SL algorithm from ECGs taken during admission. Three supervised machine learning algorithms (logistic regression, extreme gradient boosting, and random forest) were then used for analysis to find risk features using a random 70/30% split for discovery and validation cohorts.

Results: Five-year SCD occurrence rate was 3.3% (n = 287). Regardless of the used ML algorithm, the most significant risk ECG risk features detected by the CIE included known risk features such as QRS duration and factors associated with QRS duration, heart rate-corrected QT time (QTc), and the presence of premature ventricular contractions (PVCs). Risk score formed by using most significant CIE features associated with the risk of SCD despite adjusting for any clinical risk factor (including left ventricular ejection fraction). Sensitivity of CIE data to correctly identify patients with high risk of SCD (over 10% 5-year risk of SCD) was usually low, but specificity and negative prediction value reached up to 96.9% and 97.3% when selecting only the most significant features identified by logistic regression modeling (p-value threshold <0.01 for accepting features in the model). Overall, CIE data showed a modest overall performance for identifying high risk individuals with area under the receiver operating characteristic curve values ranging between 0.652 and 0.693 (highest for extreme gradient boosting and lowest for logistic regression).

Conclusion: This proof-of-concept study shows that automatic interpretation of ECG identifies previously validated risk features for SCD.

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利用计算机自动解读心电图预测心脏性猝死的长期风险。
背景:几乎所有用于采集和存储数字心电图的商业软件都提供计算机解读心电图(CIE)数据。CIE 广泛可用、价格低廉且准确。我们测试了 CIE 在长期心脏性猝死(SCD)风险预测中的潜力:这是一项对连续接受急性冠状动脉综合征治疗的 8568 名患者的回顾性研究。主要终点是五年内发生的 SCD 或同等事件(复苏成功或 ICD 治疗充分后中止的 SCD)。通用电气 Muse 12SL 算法从入院时的心电图中提取了 CIE 语句的摘要语句和测量值。然后使用三种有监督的机器学习算法(逻辑回归、极梯度提升和随机森林)进行分析,以发现风险特征,发现队列和验证队列的随机比例为 70/30%:五年 SCD 发生率为 3.3%(n = 287)。无论使用哪种ML算法,CIE检测到的最重要的心电图风险特征包括已知的风险特征,如QRS持续时间和与QRS持续时间相关的因素、心率校正QT时间(QTc)和室性早搏(PVC)的存在。在调整任何临床风险因素(包括左心室射血分数)的情况下,利用与 SCD 风险相关的最重要 CIE 特征进行风险评分。CIE数据正确识别SCD高风险(5年SCD风险超过10%)患者的灵敏度通常较低,但当仅选择逻辑回归模型确定的最重要特征时,特异性和阴性预测值分别高达96.9%和97.3%(P值阈值结论):这项概念验证研究表明,自动解读心电图可识别出先前已验证的 SCD 风险特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Cardiovascular Medicine
Frontiers in Cardiovascular Medicine Medicine-Cardiology and Cardiovascular Medicine
CiteScore
3.80
自引率
11.10%
发文量
3529
审稿时长
14 weeks
期刊介绍: Frontiers? Which frontiers? Where exactly are the frontiers of cardiovascular medicine? And who should be defining these frontiers? At Frontiers in Cardiovascular Medicine we believe it is worth being curious to foresee and explore beyond the current frontiers. In other words, we would like, through the articles published by our community journal Frontiers in Cardiovascular Medicine, to anticipate the future of cardiovascular medicine, and thus better prevent cardiovascular disorders and improve therapeutic options and outcomes of our patients.
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