Sridhar Arun R, Chen Amber Zih-Hua, Mayfield Jacob J, Fohner Alison E, Arvanitis Panagiotis, Atkinson Sarah, Braunschweig Frieder, Chatterjee Neal A, Zamponi Alessio Falasca, Johnson Gregory, Joshi Sanika A, Lassen Mats C H, Poole Jeanne E, Rumer Christopher, Skaarup Kristoffer G, Biering-Sørensen Tor, Blomstrom-Lundqvist Carina, Linde Cecilia M, Maleckar Mary M, Boyle Patrick M
Division of Cardiology, University of Washington, Seattle, Washington.
Department of Bioengineering, University of Washington, Seattle, Washington.
Cardiovasc Digit Health J. 2022 Apr;3(2):62-74. doi: 10.1016/j.cvdhj.2021.12.003. Epub 2021 Dec 31.
Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications.
Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE).
We studied intake ECGs from 1448 COVID-19 patients (60.5% male, aged 63.4 ± 16.9 years). Records were labeled by mortality (death vs discharge) or MACE (no events vs arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data.
A total of 245 (17.7%) patients died (67.3% male, aged 74.5 ± 14.4 years); 352 (24.4%) experienced at least 1 MACE (119 arrhythmic, 107 HF, 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60 ± 0.05 and 0.55 ± 0.07, respectively; these were comparable to AUC values for conventional models (0.73 ± 0.07 and 0.65 ± 0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance.
Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.
新型冠状病毒肺炎（COVID-19）的不良事件难以预测。由于需要保护医护人员，风险分层受到阻碍。我们假设人工智能（AI）可以帮助识别12导联心电图（ECG）中心肌受累的细微迹象，这有助于预测并发症。
使用COVID-19患者的入院心电图来训练AI模型，以预测死亡风险或主要不良心血管事件（MACE）。
我们研究了1448例COVID-19患者的入院心电图（男性占60.5%，年龄63.4±16.9岁）。记录根据死亡率（死亡与出院）或MACE（无事件与心律失常、心力衰竭[HF]或血栓栓塞[TE]事件）进行标记，然后用于训练AI模型；将这些模型与使用人口统计学和合并症数据开发的传统回归模型进行比较。
共有245例（17.7%）患者死亡（男性占67.3%，年龄74.5±14.4岁）；352例（24.4%）发生至少1次MACE（119例心律失常、107例HF、130例TE）。AI模型预测死亡率和MACE的曲线下面积（AUC）值分别为0.60±0.05和0.55±0.07；这些值与传统模型的AUC值（0.73±0.07和0.65±0.10）相当。我们队列中的死亡率或MACE发生率没有明显的时间趋势；使用截止日期（2020年6月9日）之后的数据进行保留测试并未降低模型性能。
仅使用入院心电图，我们的AI模型预测住院COVID-19患者死亡风险或MACE的能力有限。我们模型的准确性与使用更深入信息构建的传统模型相当，但要转化为临床应用需要更高的敏感性和阳性预测值。未来，我们希望开发利用心电图和临床数据的混合输入AI模型，以提高预测准确性。
