Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction

Abstract

Objectives We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12‐lead electrocardiogram (ECG) in a large prospective cohort. Background Patients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging. Methods We applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new “positive screens.” Results Among 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 ± 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 “false‐positives screens,” 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT‐pro‐BNP after the initial “positive screen.” Conclusions A deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes.

A Prototype Photoplethysmography Electronic Device that Distinguishes Congestive Heart Failure from Healthy Individuals by Applying Natural Time Analysis

“…Previous ECG-based methods for automated computer aided detection (CAD) of HF using various methodologies have achieved encouraging results. The duration of ECG signal recording was variable and could be as short as 2 s [10,[54][55][56][57][58]. Simple clinical prediction rules (CPR) for HF detection performed less well compared to ECG based methods [59].…”

Artificial intelligence‐augmented ECG assessment: The promise and the challenge

“…Since Einthoven developed the electrocardiogram (ECG) investigators and clinicians have sought to expand the diagnostic potential of this convenient, widely available and relatively inexpensive noninvasive examination . In this issue of the Journal of Cardiovascular Electrophysiology, Attia and colleagues demonstrate the ability of a novel algorithm based on the standard 12‐lead ECG developed with deep learning artificial intelligence (AI) to detect patients with reduced left ventricular ejection fraction (rLVEF) . This form of AI, also called machine learning allows computer programs to identify relationships directly from data.…”

“…Attia et al report impressive statistics on the ability of the AI‐ECG model (AI‐ECG) to detect patients with LVEF ≤35%. Three population samples are reported.…”

“…The effect of filtering patients using N‐terminal pro‐brain‐type natriuretic peptide (NT‐proBNP) was explored in 522 patients in whom this biomarker was assessed . An NT‐proBNP threshold of ≥125 ng/mL reduced the number of false positive cases by five (from 96 to 91) with no increase in the number of false negatives.…”

“…The third sample assessed by Attia et al was comprised of 5999 patients who had an ECG obtained but no echocardiogram. Application of the AI‐ECG identified 3.5% as having an LVEF ≤35%.…”