This article was originally published here<\/p>\n
Cardiovasc Digit Health J. 2021 Dec 31. doi: 10.1016\/j.cvdhj.2021.12.003. Online ahead of print.<\/p>\n
ABSTRACT<\/p>\n
BACKGROUND: 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.<\/p>\n
OBJECTIVE: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE).<\/p>\n
METHODS: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, 63.416.9 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.<\/p>\n
RESULTS: 245 (17.7%) patients died (67.3% male, 74.514.4 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 0.600.05 and 0.550.07, respectively; these were comparable to AUC values for conventional models (0.730.07 and 0.650.10). 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.<\/p>\n
CONCLUSION: 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.<\/p>\n
PMID:35005676 | PMC:PMC8719367 | DOI:10.1016\/j.cvdhj.2021.12.003<\/p>\n
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Identifying Risk of Adverse Outcomes in COVID-19 Patients via Artificial Intelligence-Powered Analysis of 12-Lead Intake Electrocardiogram - DocWire...<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" This article was originally published here Cardiovasc Digit Health J. 2021 Dec 31. doi: 10.1016\/j.cvdhj.2021.12.003. Continue reading