Identifying Risk of Adverse Outcomes in COVID-19 Patients via Artificial Intelligence-Powered Analysis of 12-Lead Intake Electrocardiogram.
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Sridhar AR, Chen ZH, Mayfield JJ, Fohner AE, Arvanitis P, Atkinson S, Braunschweig F, Chatterjee NA, Zamponi AF, Johnson G, Joshi SA, Lassen MCH, Poole JE, Rumer C, Skaarup KG, Biering-Sørensen T, Blomstrom-Lundqvist C, Linde CM, Maleckar MM, Boyle PM
Cardiovasc Digit Health J - (-) - [2021-12-31; online 2021-12-31]
Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that 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, 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. 245 (17.7%) patients died (67.3% male, 74.5±14.4 years); 352 (24.4%) experienced at least one 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.
PubMed 35005676
DOI 10.1016/j.cvdhj.2021.12.003
Crossref 10.1016/j.cvdhj.2021.12.003
pii: S2666-6936(21)00146-8
pmc: PMC8719367