TY - JOUR
T1 - Diagnostic accuracy of the PMcardio smartphone application for artificial intelligence–based interpretation of electrocardiograms in primary care (AMSTELHEART-1)
AU - Himmelreich, Jelle C. L.
AU - Harskamp, Ralf E.
N1 - Funding Information: This study was funded by internal funds of the Department of General Practice of the Amsterdam University Medical Centers, Location Academic Medical Center (AUMC-AMC). The authors received no funding from the owners/distributors of the investigated medical application. Funding Information: We thank all participating primary care practices for their cooperation in performing this study, and we thank Powerful Medical for providing the investigated application for validation purposes. This study was funded by internal funds of the Department of General Practice of the Amsterdam University Medical Centers, Location Academic Medical Center (AUMC-AMC). The authors received no funding from the owners/distributors of the investigated medical application. The authors were allowed free access to the current version of the PMcardio application by the owners/distributors, Powerful Medical (Bratislava, Slovakia), for purposes of validation. The authors performed an independent investigation; the manufacturer of the investigated medical application was not involved in the design, conduct, or reporting of this work. All authors attest they meet the current ICMJE criteria for authorship. The VESTA study was approved by the AUMC-AMC Medical Ethical Review Committee (MERC; 2017_023). All participants in VESTA provided written informed consent. Inclusion of de-identified patient data in the extension cohort was granted a waiver for informed consent by the AUMC-AMC MERC under the Dutch WMO for use of de-identified retrospective routine care data (W23_074). The authors designed the study and gathered and analyzed the data according to the Helsinki Declaration guidelines on human research. Publisher Copyright: © 2023 Heart Rhythm Society
PY - 2023/6
Y1 - 2023/6
N2 - Background: The use of 12-lead electrocardiogram (ECG) is common in routine primary care, however it can be difficult for less experienced ECG readers to adequately interpret the ECG. Objective: To validate a smartphone application (PMcardio) as a stand-alone interpretation tool for 12-lead ECG in primary care. Methods: We recruited consecutive patients who underwent 12-lead ECG as part of routinely indicated primary care in the Netherlands. All ECGs were assessed by the PMcardio app, which analyzes a photographed image of 12-lead ECG for automated interpretation, installed on an Android platform (Samsung Galaxy M31) and an iOS platform (iPhone SE2020). We validated the PMcardio app for detecting any major ECG abnormality (MEA, primary outcome), defined as atrial fibrillation/flutter (AF), markers of (past) myocardial ischemia, or clinically relevant impulse and/or conduction abnormalities; or AF (key secondary outcome) with a blinded expert panel as reference standard. Results: We included 290 patients from 11 Dutch general practices with median age 67 (interquartile range 55–74) years; 48% were female. On reference ECG, 71 patients (25%) had MEA and 35 (12%) had AF. Sensitivity and specificity of PMcardio for MEA were 86% (95% CI: 76%–93%) and 92% (95% CI: 87%–95%), respectively. For AF, sensitivity and specificity were 97% (95% CI: 85%–100%) and 99% (95% CI: 97%–100%), respectively. Performance was comparable between Android and iOS platform (kappa = 0.95, 95% CI: 0.91–0.99 and kappa = 1.00, 95% CI: 1.00–1.00 for MEA and AF, respectively). Conclusion: A smartphone app developed to interpret 12-lead ECGs was found to have good diagnostic accuracy in a primary care setting for major ECG abnormalities, and near-perfect properties for diagnosing AF.
AB - Background: The use of 12-lead electrocardiogram (ECG) is common in routine primary care, however it can be difficult for less experienced ECG readers to adequately interpret the ECG. Objective: To validate a smartphone application (PMcardio) as a stand-alone interpretation tool for 12-lead ECG in primary care. Methods: We recruited consecutive patients who underwent 12-lead ECG as part of routinely indicated primary care in the Netherlands. All ECGs were assessed by the PMcardio app, which analyzes a photographed image of 12-lead ECG for automated interpretation, installed on an Android platform (Samsung Galaxy M31) and an iOS platform (iPhone SE2020). We validated the PMcardio app for detecting any major ECG abnormality (MEA, primary outcome), defined as atrial fibrillation/flutter (AF), markers of (past) myocardial ischemia, or clinically relevant impulse and/or conduction abnormalities; or AF (key secondary outcome) with a blinded expert panel as reference standard. Results: We included 290 patients from 11 Dutch general practices with median age 67 (interquartile range 55–74) years; 48% were female. On reference ECG, 71 patients (25%) had MEA and 35 (12%) had AF. Sensitivity and specificity of PMcardio for MEA were 86% (95% CI: 76%–93%) and 92% (95% CI: 87%–95%), respectively. For AF, sensitivity and specificity were 97% (95% CI: 85%–100%) and 99% (95% CI: 97%–100%), respectively. Performance was comparable between Android and iOS platform (kappa = 0.95, 95% CI: 0.91–0.99 and kappa = 1.00, 95% CI: 1.00–1.00 for MEA and AF, respectively). Conclusion: A smartphone app developed to interpret 12-lead ECGs was found to have good diagnostic accuracy in a primary care setting for major ECG abnormalities, and near-perfect properties for diagnosing AF.
KW - Atrial fibrillation
KW - Cardiac arrhythmia
KW - Digital health
KW - Electrocardiogram
KW - Primary care
KW - Smartphone
UR - http://www.scopus.com/inward/record.url?scp=85154596264&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.cvdhj.2023.03.002
DO - https://doi.org/10.1016/j.cvdhj.2023.03.002
M3 - Article
C2 - 37351331
SN - 2666-6936
VL - 4
SP - 80
EP - 90
JO - Cardiovascular Digital Health Journal
JF - Cardiovascular Digital Health Journal
IS - 3
ER -