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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2021.755968/full |
| _version_ | 1818837703931723776 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T03:26:43Z |
| format | Article |
| id | doaj.art-d9c15d34e6d949d18bb347f30cd2f467 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-12-19T03:26:43Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-d9c15d34e6d949d18bb347f30cd2f4672022-12-21T20:37:36ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2021-11-01810.3389/fcvm.2021.755968755968Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical ProfilesMohanad Alkhodari0Herbert F. Jelinek1Herbert F. Jelinek2Angelos Karlas3Angelos Karlas4Angelos Karlas5Angelos Karlas6Stergios Soulaidopoulos7Petros Arsenos8Ioannis Doundoulakis9Konstantinos A. Gatzoulis10Konstantinos Tsioufis11Leontios J. Hadjileontiadis12Leontios J. Hadjileontiadis13Leontios J. Hadjileontiadis14Ahsan H. Khandoker15Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Biomedical Engineering, Biotechnology Center (BTC), Khalifa University, Abu Dhabi, United Arab EmiratesChair of Biological Imaging, Center for Translational Cancer Research (TranslaTUM), Technical University of Munich, Munich, GermanyInstitute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, GermanyDepartment for Vascular and Endovascular Surgery, Rechts der Isar University Hospital, Technical University of Munich, Munich, GermanyDZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, GermanyFirst Cardiology Department, School of Medicine, “Hippokration” General Hospital, National and Kapodistrian University of Athens, Athens, GreeceFirst Cardiology Department, School of Medicine, “Hippokration” General Hospital, National and Kapodistrian University of Athens, Athens, GreeceFirst Cardiology Department, School of Medicine, “Hippokration” General Hospital, National and Kapodistrian University of Athens, Athens, GreeceFirst Cardiology Department, School of Medicine, “Hippokration” General Hospital, National and Kapodistrian University of Athens, Athens, GreeceFirst Cardiology Department, School of Medicine, “Hippokration” General Hospital, National and Kapodistrian University of Athens, Athens, GreeceDepartment of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, GreeceDepartment of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab EmiratesBackground: 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.https://www.frontiersin.org/articles/10.3389/fcvm.2021.755968/fullheart failurecoronary artery diseaseleft ventricular ejection fractionclinical profilesdemographical and clinical informationradial visualization |
| spellingShingle | 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 Frontiers in Cardiovascular Medicine heart failure coronary artery disease left ventricular ejection fraction clinical profiles demographical and clinical information radial visualization |
| title | 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_short | 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 |
| topic | heart failure coronary artery disease left ventricular ejection fraction clinical profiles demographical and clinical information radial visualization |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2021.755968/full |
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