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Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients.
Jentzer, Jacob C; Kashou, Anthony H; Attia, Zachi I; Lopez-Jimenez, Francisco; Kapa, Suraj; Friedman, Paul A; Noseworthy, Peter A.
  • Jentzer JC; 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 D
  • Kashou AH; Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address: Kashou.Anthony@mayo.edu.
  • Attia ZI; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address: attia.itzhak@mayo.edu.
  • Lopez-Jimenez F; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address: lopez@mayo.edu.
  • Kapa S; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address: Kapa.Suraj@mayo.edu.
  • Friedman PA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address: Friedman.Paul@mayo.edu.
  • Noseworthy PA; 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.
Int J Cardiol ; 326: 114-123, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-898899
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Ventricular Dysfunction, Left Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Limits: Aged / Female / Humans / Male / Middle aged Language: English Journal: Int J Cardiol Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Ventricular Dysfunction, Left Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Limits: Aged / Female / Humans / Male / Middle aged Language: English Journal: Int J Cardiol Year: 2021 Document Type: Article