Imperial College London
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Dr Daniel Keene MBChB, MSc (Distinction), MRCP, PhD

Faculty of Medicine, National Heart & Lung Institute

Clinical Senior Lecturer in Cardiology (Clinical)
 
 
 
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Contact

 

d.keene

 
 
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Location

 

Block B Hammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bachtiger:2022:10.1016/S2589-7500(21)00256-9,
author = {Bachtiger, P and Petri, CF and Scott, FE and Ri, Park S and Kelshiker, MA and Sahemey, HK and Dumea, B and Alquero, R and Padam, PS and Hatrick, IR and Ali, A and Ribeiro, M and Cheung, W-S and Bual, N and Rana, B and Shun-Shin, M and Kramer, DB and Fragoyannis, A and Keene, D and Plymen, CM and Peters, NS},
doi = {10.1016/S2589-7500(21)00256-9},
journal = {The Lancet Digital Health},
title = {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},
url = {http://dx.doi.org/10.1016/S2589-7500(21)00256-9},
volume = {4},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 p
AU  - Bachtiger,P
AU  - Petri,CF
AU  - Scott,FE
AU  - Ri,Park S
AU  - Kelshiker,MA
AU  - Sahemey,HK
AU  - Dumea,B
AU  - Alquero,R
AU  - Padam,PS
AU  - Hatrick,IR
AU  - Ali,A
AU  - Ribeiro,M
AU  - Cheung,W-S
AU  - Bual,N
AU  - Rana,B
AU  - Shun-Shin,M
AU  - Kramer,DB
AU  - Fragoyannis,A
AU  - Keene,D
AU  - Plymen,CM
AU  - Peters,NS
DO  - 10.1016/S2589-7500(21)00256-9
PY  - 2022///
SN  - 2589-7500
TI  - 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
T2  - The Lancet Digital Health
UR  - http://dx.doi.org/10.1016/S2589-7500(21)00256-9
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34998740
UR  - https://www.thelancet.com/journals/landig/article/PIIS2589-7500(21)00256-9/fulltext
UR  - http://hdl.handle.net/10044/1/93563
VL  - 4
ER  -
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