Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study

Background Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aimed to test an artificial intelligence (AI) algorithm applied to a single-lead ECG, recorded during ECG-enabled stethoscope examination, to validate a potential point-of-care screening tool for LVEF of 40% or lower. Methods We conducted an observational, prospective, multicentre study of a convolutional neural network (known as AI-ECG) that was previously validated for the detection of reduced LVEF using 12-lead ECG as input. We used AIECG retrained to interpret single-lead ECG input alone. Patients (aged >= 18 years) attending for transthoracic echocardiogram in London (UK) were recruited. All participants had 15 s of supine, single-lead ECG recorded at the four standard anatomical positions for cardiac auscultation, plus one handheld position, using an ECG-enabled stethoscope. Transthoracic echocardiogram-derived percentage LVEF was used as ground truth. The primary outcome was performance of AI-ECG at classifying reduced LVEF (LVEF <= 40%), measured using metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with two-sided 95% CIs. The primary outcome was reported for each position individually and with an optimal combination of AI-ECG outputs (interval range 0-1) from two positions using a rule-based approach and several classification models. This study is registered with ClinicalTrials.gov , NCT04601415. Findings Between Feb 6 and May 27, 2021, we recruited 1050 patients (mean age 62 years [SD 17.4], 535 [51%] male, 432 [41%] non-White). 945 (90%) had an ejection fraction of at least 40%, and 105 (10%) had an ejection fraction of 40% or lower. Across all positions, ECGs were most frequently of adequate quality for AI-ECG interpretation at the pulmonary position (979 [93.3%] of 1050). Quality was lowest for the aortic position (846 [80.6%]). AI-ECG performed best at the pulmonary valve position (p=0.02), with an AUROC of 0.85 (95% CI 0.81-0.89), sensitivity of 84.8% (76.2-91.3), and specificity of 69.5% (66.4-72.6). Diagnostic odds ratios did not differ by age, sex, or non-White ethnicity: Taking the optimal combination of two positions (pulmonary and handheld positions), the rule-based approach resulted in an AUROC of 0.85 (0.81-0.89), sensitivity of 82.7% (72.7-90-2), and specificity of 79.9% (77.0-82.6). Using AI-ECG outputs from these two positions, a weighted logistic regression with 12 regularisation resulted in an AUROC of 0.91(0.88-0.95), sensitivity of 91.9% (78.1-98.3), and specificity of 80.2% (75-5-84-3). Interpretation A deep learning system applied to single-lead ECGs acquired during a routine examination with an ECG-enabled stethoscope can detect LVEF of 40% or lower. These findings highlight the potential for inexpensive, non-invasive, workflow-adapted, point-of-care screening, for earlier diagnosis and prognostically beneficial treatment. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.