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Cited by 167ECG Monitoring and AnalysisArtificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

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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

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Bachtiger, Patrik, Petri, Camille F, Scott, Francesca E, Ri Park, Se, Kelshiker, Mihir A, Sahemey, Harpreet K, Dumea, Bianca, Alquero, Regine, Padam, Pritpal S, Hatrick, Isobel R, Ali, Alfa, Ribeiro, Maria, Cheung, Wing-See, Bual, Nina, Rana, Bushra, Shun-Shin, Matthew, Kramer, Daniel B, Fragoyannis, Alex, Keene, Daniel, Plymen, Carla M, Peters, Nicholas S·2022·The Lancet Digital Health
DOI 10.1016/s2589-7500(21)00256-9PMC PMC8789562
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Abstract

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 AI-ECG 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 l2 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. FUNDING: NHS Accelerated Access Collaborative, NHSX, and the National Institute for Health Research.

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Figure 1 Schematic of ECG-enabled stethoscope and AI-ECG Illustration of anatomical positions for auscultation and position-specific angulation (vector) of ECG-enabled stethoscope; and flow diagram of raw ECG data to cloud-based CNN for interpretation of AI-ECG, with illustration of how raw outputs are classified according to adjustable (optimised) threshold. Anatomical images adapted from BioRender. AI=artificial intelligence. CNN=convolutional neural network. LVEF=left ventricular ejection fraction.
Figure 1 Schematic of ECG-enabled stethoscope and AI-ECG Illustration of anatomical positions for auscultation and position-specific angulation (vector) of ECG-enabled stethoscope; and flow diagram of raw ECG data to cloud-based CNN for interpretation of AI-ECG, with illustration of how raw outputs are classified according to adjustable (optimised) threshold. Anatomical images adapted from BioRender. AI=artificial intelligence. CNN=convolutional neural network. LVEF=left ventricular ejection fraction.CC BY · PubMed Central
Figure 2 Study profile TTE=transthoracic echocardiogram. LVEF=left ventricular ejection fraction. NHS=National Health Service. *Three hospitals and four community centres.
Figure 2 Study profile TTE=transthoracic echocardiogram. LVEF=left ventricular ejection fraction. NHS=National Health Service. *Three hospitals and four community centres.CC BY · PubMed Central
Figure 3 Receiver operating characteristic curves detection of reduced LVEF Data are shown for the single-best performing position (pulmonary), rule-based optimal combination of two positions (pulmonary and handheld), and exploratory logistic regression model with l2 regularisation using AI-enabled ECG outputs from optimal combination of two positions. AUROC=area under the receiver operating characteristic curve. LR=logistic regression. LVEF=left ventricular ejection fraction.
Figure 3 Receiver operating characteristic curves detection of reduced LVEF Data are shown for the single-best performing position (pulmonary), rule-based optimal combination of two positions (pulmonary and handheld), and exploratory logistic regression model with l2 regularisation using AI-enabled ECG outputs from optimal combination of two positions. AUROC=area under the receiver operating characteristic curve. LR=logistic regression. LVEF=left ventricular ejection fraction.CC BY · PubMed Central
Figure 4 Convolutional neural network's sensitivity and specificity to detect LVEF ≤40% Data are tabulated across a range stratified into age bands by sex, and by non-White ethnicity, using results from the pulmonary position at the threshold maximising the sum of sensitivity and specificity. The diagnostic OR and associated 95% CI is shown for each group and for the overall study sample. For sensitivity, data presented in brackets represents the number of patients in each group who had a positive result, with the denominator the number of patients with LVEF ≤40%. For specificity, this is the number of patients with a negative result, with the denominator patients with LVEF>40%. OR=odds ratio. LVEF=left ventricular ejection fraction.
Figure 4 Convolutional neural network's sensitivity and specificity to detect LVEF ≤40% Data are tabulated across a range stratified into age bands by sex, and by non-White ethnicity, using results from the pulmonary position at the threshold maximising the sum of sensitivity and specificity. The diagnostic OR and associated 95% CI is shown for each group and for the overall study sample. For sensitivity, data presented in brackets represents the number of patients in each group who had a positive result, with the denominator the number of patients with LVEF ≤40%. For specificity, this is the number of patients with a negative result, with the denominator patients with LVEF>40%. OR=odds ratio. LVEF=left ventricular ejection fraction.CC BY · PubMed Central

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Metadata

Authors
Bachtiger, P., Petri, C. F., Scott, F. E., Ri Park, S., Kelshiker, M. A., Sahemey, H. K., Dumea, B., Alquero, R., Padam, P. S., Hatrick, I. R., Ali, A., Ribeiro, M., Cheung, W.-S., Bual, N., Rana, B., Shun-Shin, M., Kramer, D. B., Fragoyannis, A., Keene, D., Plymen, C. M., & Peters, N. S.
Affiliations
  • Imperial College London (GB)
  • Imperial College Healthcare NHS Trust (GB)
  • Beth Israel Deaconess Medical Center (US)
Corresponding author
Peters, Nicholas S
ORCID
Venue
The Lancet Digital Health
Publisher
Elsevier BV
Published
2022-02
Volume
4
Issue
2
Pages
e117-e125
ISSN
2589-7500 (ISSN-L)
DOI
10.1016/s2589-7500(21)00256-9
PMC
PMC8789562
OpenAlex
W4206756110
Cited by
167
References
31
Citations by year
2026: 40 · 2025: 48 · 2024: 46 · 2023: 18 · 2022: 14
FWCI
21.94
Type
journal article
Language
en
Open access
Licence
Locations
  • The Lancet Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Spiral (Imperial College London) (submittedVersion, cc-by)
Funders
National Institute for Health Research
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
heart failure with reduced ejection fraction, point-of-care screening, artificial intelligence, single-lead ECG, convolutional neural networks, left ventricular ejection fraction, transthoracic echocardiography, cardiac auscultation, primary care, early diagnosis, model generalization, sex differences
MeSH terms
Aged, Artificial Intelligence, Electrocardiography, Female, United Kingdom, Heart Failure, Humans, Male, Middle Aged, Physical Examination, Prospective Studies, Neural Networks, Computer, Stethoscopes, Point-of-Care Systems

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