Järvensivu-Koivunen Minna, Kallonen Antti, van Gils Mark, Lyytikäinen Leo-Pekka, Tynkkynen Juho, Hernesniemi Jussi
Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Tays Heart Hospital, Tampere University Hospital, Tampere, Finland.
Front Cardiovasc Med. 2024 Oct 23;11:1439069. doi: 10.3389/fcvm.2024.1439069. eCollection 2024.
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.
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.
Five-year SCD occurrence rate was 3.3% ( = 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 (-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).
This proof-of-concept study shows that automatic interpretation of ECG identifies previously validated risk features for SCD.
几乎所有用于采集和存储数字心电图的商业软件都能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8568例连续接受急性冠状动脉综合征治疗患者的回顾性研究。主要终点是SCD或等效事件（通过成功复苏或适当的植入式心律转复除颤器治疗终止的SCD）的五年发生率。CIE陈述从入院时心电图的GE Muse 12SL算法生成的总结陈述和测量值中提取。然后使用三种监督式机器学习算法（逻辑回归、极端梯度提升和随机森林）进行分析，通过随机70/30%划分发现和验证队列来寻找风险特征。
五年SCD发生率为3.3%（n = 287）。无论使用哪种机器学习算法，CIE检测到的最显著的心电图风险特征包括已知风险特征，如QRS时限以及与QRS时限相关的因素、心率校正QT时间（QTc）和室性早搏（PVC）的存在。尽管对任何临床风险因素（包括左心室射血分数）进行了调整，但使用与SCD风险相关的最显著CIE特征形成的风险评分仍然存在。CIE数据正确识别SCD高风险患者（5年SCD风险超过10%）的敏感性通常较低，但在仅选择逻辑回归建模确定的最显著特征时（模型中接受特征的p值阈值<0.01），特异性和阴性预测值分别高达96.9%和97.3%。总体而言，CIE数据在识别高风险个体方面表现一般，受试者工作特征曲线下面积值在0.652至0.693之间（极端梯度提升最高，逻辑回归最低）。
这项概念验证研究表明，心电图的自动解读可识别先前已验证的SCD风险特征。
