Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients. (1st March 2021)

• Record Type: Journal Article
• Authors: Jentzer, Jacob C.; Kashou, Anthony H.; Attia, Zachi I.; Lopez-Jimenez, Francisco; Kapa, Suraj; Friedman, Paul A.; Noseworthy, Peter A.
• Is Part Of: International journal of cardiology. Volume 326(2021)
• Page Start: 114
• Page End: 123
• Publication Date: 2021-03-01
• Subjects: Electrocardiogram -- Artificial intelligence -- Left ventricular dysfunction -- Echocardiography -- Cardiac intensive care unit; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2020.10.074
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Background: An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram [TTE]) in cardiac intensive care unit (CICU) patients. Method: We included unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG and TTE within 7 days, at least one of which was during hospitalization. Discrimination of the AI-ECG for LVSD was determined using receiver-operator characteristic curve (AUC) values. Results: We included 5680 patients with a mean age of 68 ± 15 years (37% females). Acute coronary syndrome (ACS) was present in 55%. LVSD was present in 34% of patients (mean LVEF 48 ± 16%). The AI-ECG had an AUC of 0.83 (95% confidence interval 0.82–0.84) for discrimination of LVSD. Using the optimal cut-off, the AI-ECG had 73%, specificity 78%, negative predictive value 85% and overall accuracy 76% for LVSD. AUC values were higher for patients aged <70 years (0.85 versus 0.80), males (0.84 versus 0.79), patients without ACS (0.86 versus 0.80), and patients who did not undergo revascularization (0.84 versus 0.80). Conclusions: The AI-ECG algorithm had very good discrimination for LVSD in this critically-ill CICU cohort with a high prevalence of LVSD. Performance was better in younger male patients and those without ACS, highlighting those CICU patients in whom screening for LVSD using AI ECG may be more effective. The AI-ECG might potentially be useful for identification of LVSD in resource-limited settings when TTE is unavailable. Highlights: Artificial intelligence (AI) can identify left ventricular systolic dysfunction (LVSD) on the ECG. AI ECG had good discrimination for LVSD in 5680 unique cardiac intensive care unit (CICU) patients. AI ECG performed better for CICU patients without acute coronary syndrome, especially males <70 years old.