Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy.
Dilated cardiomyopathy
Echocardiography
Heart failure
Ischemic cardiomyopathy
Machine learning
Journal
BMC cardiovascular disorders
ISSN: 1471-2261
Titre abrégé: BMC Cardiovasc Disord
Pays: England
ID NLM: 100968539
Informations de publication
Date de publication:
26 09 2023
26 09 2023
Historique:
received:
27
07
2022
accepted:
19
09
2023
medline:
28
9
2023
pubmed:
27
9
2023
entrez:
26
9
2023
Statut:
epublish
Résumé
Machine learning (ML) can identify and integrate connections among data and has the potential to predict events. Heart failure is primarily caused by cardiomyopathy, and different etiologies require different treatments. The present study examined the diagnostic value of a ML algorithm that combines echocardiographic data to automatically differentiate ischemic cardiomyopathy (ICM) from dilated cardiomyopathy (DCM). We retrospectively collected the echocardiographic data of 200 DCM patients and 199 ICM patients treated in the First Affiliated Hospital of Guangxi Medical University between July 2016 and March 2022. All patients underwent invasive coronary angiography for diagnosis of ICM or DCM. The data were randomly divided into a training set and a test set via 10-fold cross-validation. Four ML algorithms (random forest, logistic regression, neural network, and XGBoost [ML algorithm under gradient boosting framework]) were used to generate a training model for the optimal subset, and the parameters were optimized. Finally, model performance was independently evaluated on the test set, and external validation was performed on 79 patients from another center. Compared with the logistic regression model (area under the curve [AUC] = 0.925), neural network model (AUC = 0.893), and random forest model (AUC = 0.900), the XGBoost model had the best identification rate, with an average sensitivity of 72% and average specificity of 78%. The average accuracy was 75%, and the AUC of the optimal subset was 0.934. External validation produced an AUC of 0.804, accuracy of 78%, sensitivity of 64% and specificity of 93%. We demonstrate that utilizing advanced ML algorithms can help to differentiate ICM from DCM and provide appreciable precision for etiological diagnosis and individualized treatment of heart failure patients.
Sections du résumé
BACKGROUND
Machine learning (ML) can identify and integrate connections among data and has the potential to predict events. Heart failure is primarily caused by cardiomyopathy, and different etiologies require different treatments. The present study examined the diagnostic value of a ML algorithm that combines echocardiographic data to automatically differentiate ischemic cardiomyopathy (ICM) from dilated cardiomyopathy (DCM).
METHODS
We retrospectively collected the echocardiographic data of 200 DCM patients and 199 ICM patients treated in the First Affiliated Hospital of Guangxi Medical University between July 2016 and March 2022. All patients underwent invasive coronary angiography for diagnosis of ICM or DCM. The data were randomly divided into a training set and a test set via 10-fold cross-validation. Four ML algorithms (random forest, logistic regression, neural network, and XGBoost [ML algorithm under gradient boosting framework]) were used to generate a training model for the optimal subset, and the parameters were optimized. Finally, model performance was independently evaluated on the test set, and external validation was performed on 79 patients from another center.
RESULTS
Compared with the logistic regression model (area under the curve [AUC] = 0.925), neural network model (AUC = 0.893), and random forest model (AUC = 0.900), the XGBoost model had the best identification rate, with an average sensitivity of 72% and average specificity of 78%. The average accuracy was 75%, and the AUC of the optimal subset was 0.934. External validation produced an AUC of 0.804, accuracy of 78%, sensitivity of 64% and specificity of 93%.
CONCLUSIONS
We demonstrate that utilizing advanced ML algorithms can help to differentiate ICM from DCM and provide appreciable precision for etiological diagnosis and individualized treatment of heart failure patients.
Identifiants
pubmed: 37752424
doi: 10.1186/s12872-023-03520-4
pii: 10.1186/s12872-023-03520-4
pmc: PMC10521456
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
476Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
Références
Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JG, Coats AJ, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the heart failure Association (HFA) of the ESC. Eur J Heart Fail. 2016;18:891–975.
pubmed: 27207191
Weintraub RG, Semsarian C, Macdonald P. Dilated cardiomyopathy. The Lancet. 2017;390:400–14.
Briceno N, Schuster A, Lumley M, Perera D. Ischaemic cardiomyopathy: pathophysiology, assessment and the role of revascularisation. Heart. 2016;102:397–406.
pubmed: 26740480
Ziaeian B, Fonarow GC. Epidemiology and aetiology of heart failure. Nat Rev Cardiol. 2016;13:368–78.
pubmed: 26935038
pmcid: 4868779
Sisakian H, Cardiomyopathies. Evolution of pathogenesis concepts and potential for new therapies. World J Cardiol. 2014;6:478–94.
pubmed: 24976920
pmcid: 4072838
Hare JM, Walford GD, Hruban RH, Hutchins GM, Deckers JW, Baughman KL. Ischemic cardiomyopathy: endomyocardial biopsy and ventriculographic evaluation of patients with congestive failure, dilated cardiomyopathy and coronary artery disease. J Am Coll Cardiol. 1992;20:1318–25.
pubmed: 1430681
Allman KC, Shaw LJ, Hachamovitch R, Udelson JE. Myocardial viability testing and impact of revascularization on prognosis in patients with coronary artery disease and left ventricular dysfunction: a meta-analysis. J Am Coll Cardiol. 2002;39:1151–8.
pubmed: 11923039
Bonow RO. The hibernating myocardium: implications for management of congestive heart failure. Am J Cardiol. 1995;75:17A–25A.
pubmed: 7840050
Westphal JG, Rigopoulos AG, Bakogiannis C, Ludwig SE, Mavrogeni S, Bigalke B, et al. The MOGE(S) classification for cardiomyopathies: current status and future outlook. Heart Fail Rev. 2017;22:743–52.
pubmed: 28721466
Ommen SR, Mital S, Burke MA, Day SM, Deswal A, Elliott P, et al. 2020 AHA/ACC Guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: executive summary: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2020;142:e533–e57.
pubmed: 33215938
Chrysohoou C, Greenberg M, Stefanadis C. Non-invasive methods in differentiating ischaemic from non-ischaemic cardiomyopathy. A review paper. Acta Cardiol. 2006;61:454–62.
pubmed: 16970057
Carey MG, Al-Zaiti SS, Canty JM Jr., Fallavollita JA. High-risk electrocardiographic parameters are ubiquitous in patients with ischemic cardiomyopathy. Ann Noninvasive Electrocardiol. 2012;17:241–51.
pubmed: 22816543
pmcid: 3404736
Walsh JL, AlJaroudi WA, Lamaa N, Abou Hassan OK, Jalkh K, Elhajj IH, et al. A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type. Scand Cardiovasc J. 2020;54:92–9.
pubmed: 31623474
Laszlo R, Kunz K, Dallmeier D, Klenk J, Denkinger M, Koenig W, et al. Accuracy of ECG indices for diagnosis of left ventricular hypertrophy in people > 65 years: results from the ActiFE study. Aging Clin Exp Res. 2016;29:875–84.
pubmed: 27830522
Cheitlin MD, Armstrong WF, Aurigemma GP, Beller GA, Bierman FZ, Davis JL, et al. ACC/AHA/ASE 2003 Guideline Update for the clinical application of Echocardiography: Summary Article. J Am Soc Echocardiogr. 2003;16:1091–110.
pubmed: 14566308
Nihoyannopoulos P, Vanoverschelde JL. Myocardial ischaemia and viability: the pivotal role of echocardiography. Eur Heart J. 2011;32:810–9.
pubmed: 21297129
Miyazawa AA. Artificial intelligence: the future for cardiology. Heart. 2019;105:1214.
pubmed: 30636218
Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017;69:2657–64.
pubmed: 28545640
Pinto YM, Elliott PM, Arbustini E, Adler Y, Anastasakis A, Bohm M, et al. Proposal for a revised definition of dilated cardiomyopathy, hypokinetic non-dilated cardiomyopathy, and its implications for clinical practice: a position statement of the ESC working group on myocardial and pericardial diseases. Eur Heart J. 2016;37:421–34.
Zhao J, Yang S, Jing R, Jin H, Hu Y, Wang J, et al. Plasma metabolomic profiles differentiate patients with dilated cardiomyopathy and ischemic cardiomyopathy. Front Cardiovasc Med. 2020;7:597546.
pubmed: 33240942
pmcid: 7683512
Chinese Society of Cardiology CMCCG. Chinese guidelines for diagnosis and treatment of dilated. J J Clin Cardiol. 2018;34:421–34.
Japp AG, Gulati A, Cook SA, Cowie MR, Prasad SK. The diagnosis and evaluation of dilated cardiomyopathy. J Am Coll Cardiol. 2016;67:2996–3010.
pubmed: 27339497
elker GM, Shaw LK. CM. OC. A standardized definition of ischemic cardiomyopathy for use in clinical research. J Am Coll Cardiol. 2002;39:210–8.
Nishimura RA, Otto CM, Bonow RO, Carabello BA, Erwin JP 3rd, Guyton RA, et al. 2014 AHA/ACC guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63:e57–185.
Deo RC. Machine learning in Medicine. Circulation. 2015;132:1920–30.
pubmed: 26572668
pmcid: 5831252
Doshi D, Ben-Yehuda O, Bonafede M, Josephy N, Karmpaliotis D, Parikh MA, et al. Underutilization of coronary artery Disease Testing among Patients hospitalized with New-Onset Heart failure. J Am Coll Cardiol. 2016;68:450–8.
pubmed: 27470451
pmcid: 9135048
Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform Biomedicine. Cell. 2020;181:92–101.
pubmed: 32243801
pmcid: 7141410
Awan SE, Bennamoun M, Sohel F, Sanfilippo FM, Dwivedi G. Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics. ESC Heart Fail. 2019;6:428–35.
pubmed: 30810291
pmcid: 6437443
Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, et al. Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart failure identification. Korean Circ J. 2019;49:629–39.
pubmed: 31074221
pmcid: 6597456
Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, et al. Prediction of 30-Day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA Cardiol. 2017;2:204–9.
pubmed: 27784047
Przewlocka-Kosmala M, Marwick TH, Dabrowski A, Kosmala W. Contribution of Cardiovascular Reserve to Prognostic categories of heart failure with preserved ejection fraction: a classification based on machine learning. J Am Soc Echocardiogr. 2019;32:604 – 15 e6.
Alimadadi A, Manandhar I, Aryal S, Munroe PB, Joe B, Cheng X. Machine learning-based classification and diagnosis of clinical cardiomyopathies. Physiol Genomics. 2020;52:391–400.
pubmed: 32744882
pmcid: 7509247
Rodriguez J, Schulz S, Voss A, Giraldo BF. Cardiovascular Coupling-Based classification of ischemic and dilated cardiomyopathy patients. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:2007–10.
pubmed: 31946294
Dinh A, Miertschin S, Young A, Mohanty SD. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med Inform Decis Mak. 2019;19:211.
pubmed: 31694707
pmcid: 6836338
Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-learning algorithms to automate morphological and functional assessments in 2D Echocardiography. J Am Coll Cardiol. 2016;68:2287–95.
pubmed: 27884247
Adler ED, Voors AA, Klein L, Macheret F, Braun OO, Urey MA, et al. Improving risk prediction in heart failure using machine learning. Eur J Heart Fail. 2020;22:139–47.
pubmed: 31721391
Kwon JM, Kim KH, Jeon KH, Park J. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography. 2019;36:213–8.
pubmed: 30515886
Al’Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2019;40:1975–86.
pubmed: 30060039
Kim EK, Chang SA, Choi JO, Glockner J, Shapiro B, Choe YH, et al. Concordant and discordant Cardiac magnetic resonance imaging delayed hyperenhancement patterns in patients with ischemic and non-ischemic cardiomyopathy. Korean Circ J. 2016;46:41–7.
pubmed: 26798384
pmcid: 4720848
Ananthasubramaniam K, Dhar R, Cavalcante JL. Role of multimodality imaging in ischemic and non-ischemic cardiomyopathy. Heart Fail Rev. 2011;16:351–67.
pubmed: 21165696
Peteiro Vázquez J, Monserrat Iglesias L, Vázquez Rey E, Calviño Santos R, Vázquez Rodríguez JM, Fabregas Casal R, et al. [Exercise echocardiography to differentiate dilated cardiomyopathy from ischemic left ventricular dysfunction]. Rev Esp Cardiol. 2003;56:57–64.
pubmed: 12550001
Blankstein R, Shturman LD, Rogers IS, Rocha-Filho JA, Okada DR, Sarwar A, et al. Adenosine-induced stress myocardial perfusion imaging using dual-source cardiac computed tomography. J Am Coll Cardiol. 2009;54:1072–84.
pubmed: 19744616
Raman SV, Shah M, McCarthy B, Garcia A, Ferketich AK. Multi–detector row cardiac computed tomography accurately quantifies right and left ventricular size and function compared with cardiac magnetic resonance. Am Heart J. 2006;151:736–44.
pubmed: 16504643
Gerber BL, Belge B, Legros GJ, Lim P, Poncelet A, Pasquet A, et al. Characterization of acute and chronic myocardial infarcts by multidetector computed tomography: comparison with contrast-enhanced magnetic resonance. Circulation. 2006;113:823–33.
pubmed: 16461822
Fazel R, Krumholz HM, Wang Y, Ross JS, Chen J, Ting HH, et al. Exposure to low-dose ionizing radiation from medical imaging procedures. N Engl J Med. 2009;361:849–57.
pubmed: 19710483
pmcid: 3707303
Schuster A, Morton G, Chiribiri A, Perera D, Vanoverschelde JL, Nagel E. Imaging in the management of ischemic cardiomyopathy: special focus on magnetic resonance. J Am Coll Cardiol. 2012;59:359–70.
pubmed: 22261158