Clinical Research
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Heart Failure

Nature Medicine (2019) - Screening for Cardiac Contractile Dysfunction Using an Artificial Intelligence–Enabled Electrocardiogram

In this work, researchers showed that application of artificial intelligence (AI) to the electrocardiogram (ECG) could identify ALVD. Using paired 12-lead ECG and echocardiogram data, from 44,959 patients at the Mayo Clinic, they trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone.

Authors: Zachi l. Attia, MS; J Dugan, CRC; P A Friedman, MD; F L Jimenez, MD; P A Noseworthy, MD; S J Asirvatham, MD; P A Pellikka, MD; D J Ladewig, BS; G A Satam, MBA; S Kapa MD

Abstract: Asymptomatic left ventricular dysfunction (ALVD) is present in 3–6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found. In this work, researchers showed that application of artificial intelligence (AI) to the electrocardiogram (ECG) could identify ALVD. Using paired 12-lead ECG and echocardiogram data, from 44,959 patients at the Mayo Clinic, they trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG—a ubiquitous, low-cost test—permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.

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