Real-world performance, long-term efficacy, and absence of bias in the artificial intelligence enhanced electrocardiogram to detect left ventricular systolic dysfunction.
Real-world performance, long-term efficacy, and absence of bias in the artificial intelligence enhanced electrocardiogram to detect left ventricular systolic dysfunction.
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现实世界的性能，长期疗效以及人工智能中的偏差增强了心电图，以检测左心室收缩功能障碍。
DOI：
10.1093/ehjdh/ztac028
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发表时间：
2022-06
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--
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中科院分区：
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一些应用于医疗实践的人工智能模型需要持续的再培训，引入了无意的种族偏见，或者在不同的患者亚组中表现不一。我们评估了人工智能增强心电图在检测多个患者和心电图变量的左心室收缩功能障碍方面的真实性能，以确定该算法在没有再训练的情况下的长期有效性和潜在偏倚。分析了2019年在明尼苏达州、亚利桑那州和佛罗里达的马约诊所采集的心电图，并在14天内进行了超声心动图检查（n = 44986例独特患者）。计算曲线下面积（AUC），以评价算法在年龄组、人种和种族组、患者就诊位置、心电图特征和随时间推移的性能。用于检测左心室收缩功能障碍的人工智能增强心电图在总队列中的AUC为0.903。时间序列分析验证了模型的时间稳定性。所有人种和种族组的曲线下面积相似（0.90-0.92），性别间的性能差异极小。具有“正常窦性心律”心电图的患者（n = 37 047）显示AUC为0.91。所有其他心电图特征的曲线下面积在0.79和0.91之间，左束分支阻滞组的表现最低（0.79）。用于检测左心室收缩功能障碍的人工智能增强心电图在没有再训练的情况下随时间推移是稳定的，并且在包括时间、患者种族和心电图特征在内的多个变量方面是稳健的。AI性能监控。
Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features. AI Performance Monitoring.
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影响因子：
2.1
作者：
Cole, Graham D.;Dhutia, Niti M.;Francis, Darrel P.
通讯作者：
Francis, Darrel P.
影响因子：
158.5
作者：
PFEFFER, MA;BRAUNWALD, E;HAWKINS, CM
通讯作者：
HAWKINS, CM
影响因子：
8.9
作者：
Attia ZI;Kapa S;Noseworthy PA;Lopez-Jimenez F;Friedman PA
通讯作者：
Friedman PA
影响因子：
18.2
作者：
McDonagh, Theresa A.;Metra, Marco;Skibelund, Anne Kathrine
通讯作者：
Skibelund, Anne Kathrine
影响因子：
3.5
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
通讯作者：
Lopez-Jimenez F