External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction.
External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction.
复制标题
用于检测心室功能障碍的深度学习心电图算法的外部验证。
DOI：
10.1016/j.ijcard.2020.12.065
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发表时间：
2021-04-15
影响因子：
3.5
通讯作者：
Lopez-Jimenez F
中科院分区：
文献类型：
--
作者：
Attia IZ;Tseng AS;Benavente ED;Medina-Inojosa JR;Clark TG;Malyutina S;Kapa S;Schirmer H;Kudryavtsev AV;Noseworthy PA;Carter RE;Ryabikov A;Perel P;Friedman PA;Leon DA;Lopez-Jimenez F
目的：验证一种新的人工智能心电图算法(AI-ECG)在外部人群中检测左心室收缩功能障碍(LVSD)的有效性。即使在无症状的情况下，LVSD也会增加发病率和死亡率。最近，我们根据在梅奥诊所接受治疗的患者的大样本，利用ECG获得了AI-ECG来检测LVSD。我们对了解你的心脏研究的受试者进行了一项外部验证研究，这是一项对居住在俄罗斯两个城市、年龄在35-69岁之间的成年人进行的横断面研究，他们都接受了心电图和经胸超声心动图检查。左心室射血分数为左心室射血分数(≤)<35%。我们评估了人工智能心电的表现，以在这一不同的患者群体中识别LVSD。在这项基于外部人群的验证研究的4277名受试者中，0.6%的人患有LVSD(而最初的临床衍生研究为7.8%)。AI-ECG检测LVSD的总体性能稳健，接收器操作曲线下的面积为0.82。当使用原始衍生研究的左心室短轴缩短率截断值0.256时，该人群的敏感性、特异性和准确性分别为26.9%、97.4%和97.0%。对不同敏感值的其他概率截止值进行了分析。AI-ECG在一个与开发算法时使用的人群非常不同的人群中以稳健的测试性能检测到了LVSD。临床应用可能需要特定人群的截断值。人群特征、心电图和超声心动图数据质量的差异可能会影响测试性能。
To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population. LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic. We performed an external validation study with subjects from the Know Your Heart Study, a cross-sectional study of adults aged 35–69 years residing in two cities in Russia, who had undergone both ECG and transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population. Among 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to 7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust with an area under the receiver operating curve of 0.82. When using the LVSD probability cut-off of 0.256 from the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%, 97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values. The AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance.
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影响因子：
37.8
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通讯作者：
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