Jacob C Jentzer1, Anthony H Kashou2, Zachi I Attia3, Francisco Lopez-Jimenez4, Suraj Kapa5, Paul A Friedman6, Peter A Noseworthy7. 1. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America. Electronic address: jentzer.jacob@mayo.edu. 2. Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address: Kashou.Anthony@mayo.edu. 3. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address: attia.itzhak@mayo.edu. 4. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address: lopez@mayo.edu. 5. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address: Kapa.Suraj@mayo.edu. 6. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address: Friedman.Paul@mayo.edu. 7. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America. Electronic address: Noseworthy.Peter@mayo.edu.
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.
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.
Authors: Akhil Vaid; Kipp W Johnson; Marcus A Badgeley; Sulaiman S Somani; Mesude Bicak; Isotta Landi; Adam Russak; Shan Zhao; Matthew A Levin; Robert S Freeman; Alexander W Charney; Atul Kukar; Bette Kim; Tatyana Danilov; Stamatios Lerakis; Edgar Argulian; Jagat Narula; Girish N Nadkarni; Benjamin S Glicksberg Journal: JACC Cardiovasc Imaging Date: 2021-10-13
Authors: Daniel Sierra-Lara Martinez; Peter A Noseworthy; Oguz Akbilgic; Joerg Herrmann; Kathryn J Ruddy; Abdulaziz Hamid; Ragasnehith Maddula; Ashima Singh; Robert Davis; Fatma Gunturkun; John L Jefferies; Sherry-Ann Brown Journal: Am Heart J Plus Date: 2022-04-01
Authors: Christopher J Klein; Ilke Ozcan; Zachi I Attia; Michal Cohen-Shelly; Amir Lerman; Jose R Medina-Inojosa; Francisco Lopez-Jimenez; Paul A Friedman; Margherita Milone; Shahar Shelly Journal: Mayo Clin Proc Innov Qual Outcomes Date: 2022-09-16