2021
DOI: 10.1016/j.mayocp.2021.06.024
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Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery

Abstract: Objective: To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality after cardiac surgery among patients without severe ventricular dysfunction (LVEF>35%). Methods: Patients who underwent valve or coronary bypass surgery at Mayo Clinic and had documented LVEF above 35% on baseline electrocardiography were included. We compared pa… Show more

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Cited by 13 publications
(7 citation statements)
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“…Mahayni AA et al described an ECG based AI algorithm that predicts ventricular dysfunction post-surgery, predicts long-term mortality in cardiac surgeries. Such algorithms can be developed and implemented for pediatric surgical procedures and drastically improve surgical outcomes [ 104 ].…”
Section: Resultsmentioning
confidence: 99%
“…Mahayni AA et al described an ECG based AI algorithm that predicts ventricular dysfunction post-surgery, predicts long-term mortality in cardiac surgeries. Such algorithms can be developed and implemented for pediatric surgical procedures and drastically improve surgical outcomes [ 104 ].…”
Section: Resultsmentioning
confidence: 99%
“…Application of AI/ML on large numbers of discrete variables or physiological inputs may be superior to clinical risk scores for assessing perioperative risk. 82 In patients undergoing valve or bypass surgery, application of CNNs to the ECG to screen for ventricular dysfunction predicted long-term mortality of inpatients (with EF>35%). 83 Intraoperatively, AI/ML applied to the electroencephalogram revealed spectral features that can assess the depth of anesthesia, guide anesthetic drug dosing, and potentially mitigate postoperative delirium.…”
Section: In-hospital Monitoringmentioning
confidence: 99%
“…Відомо лише кілька робіт на цю тему. У роботі А. А. Mahayni та співавтори [31] використовували ШІ для прогнозування тривалої смертності після кардіохірургії. Вони використовували передопераційну ЕКГ пацієнтів з низькою фракцією викиду лівого шлуночка для навчання згорткової нейронної мережі для бінарної класифікації та продемонстрували підвищену прогностичну цінність у передбаченні довготривалої смертності пацієнтів, яким проводили кардіохірургічні процедури.…”
Section: обговоренняunclassified