Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, California, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, California, USA.
Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, California, USA.
JACC Clin Electrophysiol. 2022 Apr;8(4):411-423. doi: 10.1016/j.jacep.2022.02.004. Epub 2022 Mar 30.
This study aimed to develop a novel clinical prediction algorithm for avertable sudden cardiac death.
Sudden cardiac death manifests as ventricular fibrillation (VF)/ ventricular tachycardia (VT) potentially treatable with defibrillation, or nonshockable rhythms (pulseless electrical activity/asystole) with low likelihood of survival. There are no available clinical risk scores for targeted prediction of VF/VT.
Subjects with out-of-hospital sudden cardiac arrest presenting with documented VF or pulseless VT (33% of total cases) were ascertained prospectively from the Portland, Oregon, metro area with population ≈1 million residents (n = 1,374, 2002-2019). Comparisons of lifetime clinical records were conducted with a control group (n = 1,600) with ≈70% coronary disease prevalence. Prediction models were constructed from a training dataset using backwards stepwise logistic regression and applied to an internal validation dataset. Receiver operating characteristic curves (C statistic) were used to evaluate model discrimination. External validation was performed in a separate, geographically distinct population (Ventura County, California, population ≈850,000, 2015-2020).
A clinical algorithm (VFRisk) constructed with 13 clinical, electrocardiogram, and echocardiographic variables had very good discrimination in the training dataset (C statistic = 0.808; [95% CI: 0.774-0.842]) and was successfully validated in internal (C statistic = 0.776 [95% CI: 0.725-0.827]) and external (C statistic = 0.782 [95% CI: 0.718-0.846]) datasets. The algorithm substantially outperformed the left ventricular ejection fraction (LVEF) ≤35% (C statistic = 0.638) and performed well across the LVEF spectrum.
An algorithm for prediction of sudden cardiac arrest manifesting with VF/VT was successfully constructed using widely available clinical and noninvasive markers. These findings have potential to enhance primary prevention, especially in patients with mid-range or preserved LVEF.
本研究旨在开发一种新的可预防心源性猝死的临床预测算法。
心源性猝死表现为室颤（VF）/室性心动过速（VT），可能需要除颤治疗，或无脉搏电活动/心搏停止等非冲击性节律，生存可能性低。目前尚无针对 VF/VT 进行靶向预测的可用临床风险评分。
前瞻性地从俄勒冈州波特兰都会区（人口约 100 万居民）确定患有院外心搏骤停且伴有记录的 VF 或无脉搏 VT 的患者（占总病例的 33%）（n=1374，2002-2019 年）。使用向后逐步逻辑回归构建来自训练数据集的预测模型，并将其应用于内部验证数据集。使用接收者操作特征曲线（C 统计量）评估模型区分度。在另一个地理位置不同的人群（加利福尼亚州文图拉县，人口约 85 万，2015-2020 年）中进行外部验证。
使用 13 项临床、心电图和超声心动图变量构建的临床算法（VFRisk）在训练数据集中具有非常好的区分度（C 统计量为 0.808；[95%CI：0.774-0.842]），并在内部（C 统计量为 0.776 [95%CI：0.725-0.827]）和外部（C 统计量为 0.782 [95%CI：0.718-0.846]）数据集成功验证。该算法显著优于左心室射血分数（LVEF）≤35%（C 统计量为 0.638），并且在整个 LVEF 范围内表现良好。
使用广泛可用的临床和非侵入性标志物成功构建了用于预测表现为 VF/VT 的心搏骤停的算法。这些发现有可能增强一级预防，特别是在 LVEF 处于中值或保留的患者中。
