Utility of an Artificial Intelligence Enabled Electrocardiogram for Risk Assessment in Liver Transplant Candidates.
Artificial Intelligence enabled electrocardiogram
Artificial intelligence
Convolutional neural network
End stage liver disease
Liver transplantation
Receiver operator characteristic
Journal
Digestive diseases and sciences
ISSN: 1573-2568
Titre abrégé: Dig Dis Sci
Pays: United States
ID NLM: 7902782
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
received:
09
04
2022
accepted:
14
03
2023
medline:
18
5
2023
pubmed:
7
4
2023
entrez:
6
4
2023
Statut:
ppublish
Résumé
Post-operative cardiac complications occur infrequently but contribute to mortality after liver transplantation (LT). Artificial intelligence-based algorithms based on electrocardiogram (AI-ECG) are attractive for use during pre-operative evaluation to screen for risk of post-operative cardiac complications, but their use for this purpose is unknown. The aim of this study was to evaluate the performance of an AI-ECG algorithm in predicting cardiac factors such as asymptomatic left ventricular systolic dysfunction or potential for developing post-operative atrial fibrillation (AF) in cohorts of patients with end-stage liver disease either undergoing evaluation for transplant or receiving a liver transplant. A retrospective study was performed in two consecutive adult cohorts of patients who were either evaluated for LT or underwent LT at a single center between 2017 and 2019. ECG were analyzed using an AI-ECG trained to recognize patterns from a standard 12-lead ECG which could identify the presence of left ventricular systolic dysfunction (LVEF < 50%) or subsequent atrial fibrillation. The performance of AI-ECG in patients undergoing LT evaluation is similar to that in a general population but was lower in the presence of prolonged QTc. AI-ECG analysis on ECG in sinus rhythm had an AUROC of 0.69 for prediction of de novo post-transplant AF. Although post-transplant cardiac dysfunction occurred in only 2.3% of patients in the study cohorts, AI-ECG had an AUROC of 0.69 for prediction of subsequent low left ventricular ejection fraction. A positive screen for low EF or AF on AI-ECG can alert to risk of post-operative cardiac dysfunction or predict new onset atrial fibrillation after LT. The use of an AI-ECG can be a useful adjunct in persons undergoing transplant evaluation that can be readily implemented in clinical practice.
Sections du résumé
BACKGROUND
Post-operative cardiac complications occur infrequently but contribute to mortality after liver transplantation (LT). Artificial intelligence-based algorithms based on electrocardiogram (AI-ECG) are attractive for use during pre-operative evaluation to screen for risk of post-operative cardiac complications, but their use for this purpose is unknown.
AIMS
The aim of this study was to evaluate the performance of an AI-ECG algorithm in predicting cardiac factors such as asymptomatic left ventricular systolic dysfunction or potential for developing post-operative atrial fibrillation (AF) in cohorts of patients with end-stage liver disease either undergoing evaluation for transplant or receiving a liver transplant.
METHODS
A retrospective study was performed in two consecutive adult cohorts of patients who were either evaluated for LT or underwent LT at a single center between 2017 and 2019. ECG were analyzed using an AI-ECG trained to recognize patterns from a standard 12-lead ECG which could identify the presence of left ventricular systolic dysfunction (LVEF < 50%) or subsequent atrial fibrillation.
RESULTS
The performance of AI-ECG in patients undergoing LT evaluation is similar to that in a general population but was lower in the presence of prolonged QTc. AI-ECG analysis on ECG in sinus rhythm had an AUROC of 0.69 for prediction of de novo post-transplant AF. Although post-transplant cardiac dysfunction occurred in only 2.3% of patients in the study cohorts, AI-ECG had an AUROC of 0.69 for prediction of subsequent low left ventricular ejection fraction.
CONCLUSIONS
A positive screen for low EF or AF on AI-ECG can alert to risk of post-operative cardiac dysfunction or predict new onset atrial fibrillation after LT. The use of an AI-ECG can be a useful adjunct in persons undergoing transplant evaluation that can be readily implemented in clinical practice.
Identifiants
pubmed: 37022601
doi: 10.1007/s10620-023-07928-y
pii: 10.1007/s10620-023-07928-y
pmc: PMC10077316
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2379-2388Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Konerman MA, Fritze D, Weinberg RL, Sonnenday CJ, Sharma P. Incidence of and risk assessment for adverse cardiovascular outcomes after liver transplantation: A systematic review. Transplantation 2017;101:1645–1657. https://doi.org/10.1097/TP.0000000000001710 .
doi: 10.1097/TP.0000000000001710
pubmed: 28296809
pmcid: 5481461
Martin P, DiMartini A, Feng S, Brown R Jr, Fallon M. Evaluation for liver transplantation in adults: 2013 practice guideline by the American Association for the Study of Liver Diseases and the American Society of Transplantation. Hepatology 2014;59:1144–1165. https://doi.org/10.1002/hep.26972 .
doi: 10.1002/hep.26972
pubmed: 24716201
Raval R, Harinstein ME, Skaro AI et al. Cardiovascular risk assessment of the liver transplant candidate. J Am Coll Cardiol 2011;58:223–231. https://doi.org/10.1016/j.jacc.2011.03.026 .
doi: 10.1016/j.jacc.2011.03.026
pubmed: 21737011
Lentine KL, Costa SP, Weir MR et al. Cardiac disease evaluation and management among kidney and liver transplantation candidates: a scientific statement from the American Heart Association and the American College of Cardiology Foundation. J Am Coll Cardiol 2012;60:434–480. https://doi.org/10.1016/j.jacc.2012.05.008 .
doi: 10.1016/j.jacc.2012.05.008
pubmed: 22763103
VanWagner LB, Lapin B, Levitsky J et al. High early cardiovascular mortality after liver transplantation. Liver Transpl 2014;20:1306–1316. https://doi.org/10.1002/lt.23950 .
doi: 10.1002/lt.23950
pubmed: 25044256
pmcid: 4213202
Khurmi NS, Chang YH, Eric Steidley D et al. Hospitalizations for cardiovascular disease after liver transplantation in the United States. Liver Transpl 2018;24:1398–1410. https://doi.org/10.1002/lt.25055 .
doi: 10.1002/lt.25055
pubmed: 29544033
Chokesuwattanaskul R, Thongprayoon C, Bathini T et al. Liver transplantation and atrial fibrillation: A meta-analysis. World J Hepatol 2018;10:761–771. https://doi.org/10.4254/wjh.v10.i10.761 .
doi: 10.4254/wjh.v10.i10.761
pubmed: 30386469
pmcid: 6206153
Nicolau-Raducu R, Gitman M, Ganier D et al. Adverse cardiac events after orthotopic liver transplantation: a cross-sectional study in 389 consecutive patients. Liver Transpl 2015;21:13–21. https://doi.org/10.1002/lt.23997 .
doi: 10.1002/lt.23997
pubmed: 25213120
Attia ZI, Kapa S, Lopez-Jimenez F et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med 2019;25:70–74. https://doi.org/10.1038/s41591-018-0240-2 .
doi: 10.1038/s41591-018-0240-2
pubmed: 30617318
Moller S, Bernardi M. Interactions of the heart and the liver. Eur Heart J 2013;34:2804–2811. https://doi.org/10.1093/eurheartj/eht246 .
doi: 10.1093/eurheartj/eht246
pubmed: 23853073
Attia ZI, Noseworthy PA, Lopez-Jimenez F et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. Lancet 2019;394:861–867. https://doi.org/10.1016/S0140-6736(19)31721-0 .
doi: 10.1016/S0140-6736(19)31721-0
pubmed: 31378392
Attia ZI, Kapa S, Noseworthy PA, Lopez-Jimenez F, Friedman PA. Artificial intelligence ECG to detect left ventricular dysfunction in COVID-19: A case series. Mayo Clin Proc 2020;95:2464–2466. https://doi.org/10.1016/j.mayocp.2020.09.020 .
doi: 10.1016/j.mayocp.2020.09.020
pubmed: 33153634
Attia ZI, Kapa S, Yao X et al. Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction. J Cardiovasc Electrophysiol 2019;30:668–674. https://doi.org/10.1111/jce.13889 .
doi: 10.1111/jce.13889
pubmed: 30821035
Ko WY, Siontis KC, Attia ZI et al. Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram. J Am Coll Cardiol 2020;75:722–733. https://doi.org/10.1016/j.jacc.2019.12.030 .
doi: 10.1016/j.jacc.2019.12.030
pubmed: 32081280
Noseworthy PA, Attia ZI, Brewer LC et al. Assessing and mitigating bias in medical artificial intelligence: The effects of race and ethnicity on a deep learning model for ECG analysis. Circ Arrhythm Electrophysiol 2020;13:e007988. https://doi.org/10.1161/CIRCEP.119.007988 (2020).
Rautaharju PM, Surawicz B, Gettes LS et al. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part IV: the ST segment, T and U waves, and the QT interval: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society. Endorsed by the International Society for Computerized Electrocardiology. J Am Coll Cardiol 2009;53:982–991. https://doi.org/10.1016/j.jacc.2008.12.014 .
Lang RM, Badano LP, Mor-Avi V et al. Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr 2015;28:1–39e14. https://doi.org/10.1016/j.echo.2014.10.003 .
Christopoulos G, Graff-Radford J, Lopez CL et al. Artificial intelligence-electrocardiography to predict incident atrial fibrillation: A population-based study. Circ Arrhythm Electrophysiol 2020. https://doi.org/10.1161/CIRCEP.120.009355 .
Raghunath S, Pfeifer JM, Ulloa-Cerna AE et al. Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation-related stroke. Circulation 2021;143:1287–1298. https://doi.org/10.1161/CIRCULATIONAHA.120.047829 .
doi: 10.1161/CIRCULATIONAHA.120.047829
pubmed: 33588584
pmcid: 7996054
Sonny A, Govindarajan SR, Jaber WA et al. Systolic heart failure after liver transplantation: Incidence, predictors, and outcome. Clin Transplant 2018;32:e13199. https://doi.org/10.1111/ctr.13199 .
Fan R, Zhang N, Yang L et al. AI-based prediction for the risk of coronary heart disease among patients with type 2 diabetes mellitus. Sci Rep 2020;10:14457. https://doi.org/10.1038/s41598-020-71321-2 .
doi: 10.1038/s41598-020-71321-2
pubmed: 32879331
pmcid: 7467935