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

The Lancet Digital Health - Tập 4 - Trang e117-e125 - 2022
Patrik Bachtiger1,2,3, Camille F Petri1,3, Francesca E Scott1, Se Ri Park1, Mihir A Kelshiker1,2, Harpreet K Sahemey2, Bianca Dumea2, Regine Alquero2, Pritpal S Padam2, Isobel R Hatrick2, Alfa Ali2, Maria Ribeiro2, Wing-See Cheung2, Nina Bual2, Bushra Rana1, Matthew Shun-Shin1,2, Daniel B Kramer1,4, Alex Fragoyannis5, Daniel Keene1,2, Carla M Plymen2
1National Heart and Lung Institute and Centre for Cardiac Engineering, Imperial College London, London, UK
2Imperial College Healthcare NHS Trust, London, UK
3UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK
4Richard A and Susan F Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
5NHS Ealing Clinical Commissioning Group, London, UK

Tài liệu tham khảo

Bottle, 2018, Routes to diagnosis of heart failure: observational study using linked data in England, Heart, 104, 600, 10.1136/heartjnl-2017-312183 Heidenreich, 2013, Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association, Circ Heart Fail, 6, 606, 10.1161/HHF.0b013e318291329a Ambrosy, 2014, The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries, J Am Coll Cardiol, 63, 1123, 10.1016/j.jacc.2013.11.053 Bozkurt, 2021, Universal definition and classification of heart failure: a report of the Heart Failure Society of America, Heart Failure Association of the European Society of Cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition, Eur J Heart Fail, 23, 352, 10.1002/ejhf.2115 Solomon, 2019, Angiotensin-neprilysin inhibition in heart failure with preserved ejection fraction, N Engl J Med, 381, 1609, 10.1056/NEJMoa1908655 Burnett, 2017, Thirty years of evidence on the efficacy of drug treatments for chronic heart failure with reduced ejection fraction: a network meta-analysis, Circ Heart Fail, 10, 10.1161/CIRCHEARTFAILURE.116.003529 Bloom, 2017, Heart failure with reduced ejection fraction, Nat Rev Dis Primers, 3, 10.1038/nrdp.2017.58 Attia, 2019, Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram, Nat Med, 25, 70, 10.1038/s41591-018-0240-2 Adedinsewo, 2020, Artificial intelligence-enabled ECG algorithm to identify patients with left ventricular systolic dysfunction presenting to the emergency department with dyspnea, Circ Arrhythm Electrophysiol, 13, 10.1161/CIRCEP.120.008437 Attia, 2020, Artificial intelligence ECG to detect left ventricular dysfunction in COVID-19: a case series, Mayo Clin Proc, 95, 2464, 10.1016/j.mayocp.2020.09.020 Attia, 2019, Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction, J Cardiovasc Electrophysiol, 30, 668, 10.1111/jce.13889 Attia, 2021, External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction, Int J Cardiol, 329, 130, 10.1016/j.ijcard.2020.12.065 Yao, 2021, Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial, Nat Med, 27, 815, 10.1038/s41591-021-01335-4 DeLong, 1988, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, 837, 10.2307/2531595 Savarese, 2017, Global public health burden of heart failure, Card Fail Rev, 3, 7, 10.15420/cfr.2016:25:2 de Couto, 2010, Early detection of myocardial dysfunction and heart failure, Nat Rev Cardiol, 7, 334, 10.1038/nrcardio.2010.51 Cowie, 2017, The heart failure epidemic: a UK perspective, Echo Res Pract, 4, R15, 10.1530/ERP-16-0043 Schwalbe, 2020, Artificial intelligence and the future of global health, Lancet, 395, 1579, 10.1016/S0140-6736(20)30226-9 Celi, 2019, An awakening in medicine: the partnership of humanity and intelligent machines, Lancet Digit Health, 1, e255, 10.1016/S2589-7500(19)30127-X O'Neal, 2017, Electrocardiographic predictors of heart failure with reduced versus preserved ejection fraction: the multi-ethnic study of atherosclerosis, J Am Heart Assoc, 6, 10.1161/JAHA.117.006023 Hendry, 2016, Scoring system based on electrocardiogram features to predict the type of heart failure in patients with chronic heart failure, Cardiol Res, 7, 110, 10.14740/cr473w Alhamaydeh, 2020, Identifying the most important ECG predictors of reduced ejection fraction in patients with suspected acute coronary syndrome, J Electrocardiol, 61, 81, 10.1016/j.jelectrocard.2020.06.003 Rim, 2021, Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs, Lancet Digit Health, 3, e306, 10.1016/S2589-7500(21)00043-1 Topol, 2019, High-performance medicine: the convergence of human and artificial intelligence, Nat Med, 25, 44, 10.1038/s41591-018-0300-7 Parikh, 2019, Regulation of predictive analytics in medicine, Science, 363, 810, 10.1126/science.aaw0029 Nagendran, 2020, Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies, BMJ, 368, m689, 10.1136/bmj.m689 Orchard, 2019, Uptake of a primary care atrial fibrillation screening program (AF-SMART): a realist evaluation of implementation in metropolitan and rural general practice, BMC Fam Pract, 20, 170, 10.1186/s12875-019-1058-9 Wahl, 2018, Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?, BMJ Glob Health, 3, 10.1136/bmjgh-2018-000798 Chorba, 2021, Deep learning algorithm for automated cardiac murmur detection via a digital stethoscope platform, J Am Heart Assoc, 10, 10.1161/JAHA.120.019905 Attia, 2019, An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction, Lancet, 394, 861, 10.1016/S0140-6736(19)31721-0 Hagiwara, 2018, Computer-aided diagnosis of atrial fibrillation based on ECG signals: a review, Inf Sci, 467, 99, 10.1016/j.ins.2018.07.063