Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, de...

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Autores principales:	Mohanad Alkhodari, Herbert F. Jelinek, Angelos Karlas, Stergios Soulaidopoulos, Petros Arsenos, Ioannis Doundoulakis, Konstantinos A. Gatzoulis, Konstantinos Tsioufis, Leontios J. Hadjileontiadis, Ahsan H. Khandoker
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	heart failure coronary artery disease left ventricular ejection fraction clinical profiles demographical and clinical information radial visualization Diseases of the circulatory (Cardiovascular) system RC666-701
Acceso en línea:	https://doaj.org/article/d9c15d34e6d949d18bb347f30cd2f467
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spelling	oai:doaj.org-article:d9c15d34e6d949d18bb347f30cd2f4672021-11-22T05:02:16ZDeep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles2297-055X10.3389/fcvm.2021.755968https://doaj.org/article/d9c15d34e6d949d18bb347f30cd2f4672021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcvm.2021.755968/fullhttps://doaj.org/toc/2297-055XBackground: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF.Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges.Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories.Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98.Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.Mohanad AlkhodariHerbert F. JelinekHerbert F. JelinekAngelos KarlasAngelos KarlasAngelos KarlasAngelos KarlasStergios SoulaidopoulosPetros ArsenosIoannis DoundoulakisKonstantinos A. GatzoulisKonstantinos TsioufisLeontios J. HadjileontiadisLeontios J. HadjileontiadisLeontios J. HadjileontiadisAhsan H. KhandokerFrontiers Media S.A.articleheart failurecoronary artery diseaseleft ventricular ejection fractionclinical profilesdemographical and clinical informationradial visualizationDiseases of the circulatory (Cardiovascular) systemRC666-701ENFrontiers in Cardiovascular Medicine, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	heart failure coronary artery disease left ventricular ejection fraction clinical profiles demographical and clinical information radial visualization Diseases of the circulatory (Cardiovascular) system RC666-701
spellingShingle	heart failure coronary artery disease left ventricular ejection fraction clinical profiles demographical and clinical information radial visualization Diseases of the circulatory (Cardiovascular) system RC666-701 Mohanad Alkhodari Herbert F. Jelinek Herbert F. Jelinek Angelos Karlas Angelos Karlas Angelos Karlas Angelos Karlas Stergios Soulaidopoulos Petros Arsenos Ioannis Doundoulakis Konstantinos A. Gatzoulis Konstantinos Tsioufis Leontios J. Hadjileontiadis Leontios J. Hadjileontiadis Leontios J. Hadjileontiadis Ahsan H. Khandoker Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles
description	Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF.Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges.Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories.Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98.Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.
format	article
author	Mohanad Alkhodari Herbert F. Jelinek Herbert F. Jelinek Angelos Karlas Angelos Karlas Angelos Karlas Angelos Karlas Stergios Soulaidopoulos Petros Arsenos Ioannis Doundoulakis Konstantinos A. Gatzoulis Konstantinos Tsioufis Leontios J. Hadjileontiadis Leontios J. Hadjileontiadis Leontios J. Hadjileontiadis Ahsan H. Khandoker
author_facet	Mohanad Alkhodari Herbert F. Jelinek Herbert F. Jelinek Angelos Karlas Angelos Karlas Angelos Karlas Angelos Karlas Stergios Soulaidopoulos Petros Arsenos Ioannis Doundoulakis Konstantinos A. Gatzoulis Konstantinos Tsioufis Leontios J. Hadjileontiadis Leontios J. Hadjileontiadis Leontios J. Hadjileontiadis Ahsan H. Khandoker
author_sort	Mohanad Alkhodari
title	Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles
title_short	Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles
title_full	Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles
title_fullStr	Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles
title_full_unstemmed	Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles
title_sort	deep learning predicts heart failure with preserved, mid-range, and reduced left ventricular ejection fraction from patient clinical profiles
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/d9c15d34e6d949d18bb347f30cd2f467
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Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

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