Quantitative Prediction of Right Ventricular Size and Function From the ECG
Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored. We trained a deep learning-ECG model to predict RV dilation...
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Published in | Journal of the American Heart Association Vol. 13; no. 1; p. e031671 |
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Main Authors | , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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England
John Wiley and Sons Inc
02.01.2024
Wiley |
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Abstract | Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored.
We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m
), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSH
[Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSH
; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSH
/MSH
cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSH
/MSH
cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSH
/MSH
cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSH
/MSH
cohorts was 0.91/0.81/0.92, respectively. MSH
mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m
. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease;
=0.031).
Deep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome. |
---|---|
AbstractList | BACKGROUNDRight ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored.METHODS AND RESULTSWe trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m2), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSHoriginal [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.91/0.81/0.92, respectively. MSHoriginal mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m2. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease; P=0.031).CONCLUSIONSDeep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome. Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored. We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m ), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSH [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSH ; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSH /MSH cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSH /MSH cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSH /MSH cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSH /MSH cohorts was 0.91/0.81/0.92, respectively. MSH mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m . The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease; =0.031). Deep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome. Background Right ventricular ejection fraction (RVEF) and end‐diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning–enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored. Methods and Results We trained a deep learning–ECG model to predict RV dilation (RVEDV >120 mL/m 2 ), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12‐lead ECG paired with reference‐standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine‐tuned in a multicenter health system (MSH original [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSH validation ; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant‐free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSH original /MSH validation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSH original /MSH validation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSH original /MSH validation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSH original /MSH validation cohorts was 0.91/0.81/0.92, respectively. MSH original mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m 2 . The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow‐up of 2.3 years, predicted RVEF was associated with adjusted transplant‐free survival (hazard ratio, 1.40 for each 10% decrease; P =0.031). Conclusions Deep learning–ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome. Background Right ventricular ejection fraction (RVEF) and end‐diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning–enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored. Methods and Results We trained a deep learning–ECG model to predict RV dilation (RVEDV >120 mL/m2), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12‐lead ECG paired with reference‐standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine‐tuned in a multicenter health system (MSHoriginal [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant‐free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.91/0.81/0.92, respectively. MSHoriginal mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m2. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow‐up of 2.3 years, predicted RVEF was associated with adjusted transplant‐free survival (hazard ratio, 1.40 for each 10% decrease; P=0.031). Conclusions Deep learning–ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome. |
Author | Vaid, Akhil Greenspan, Hayit Narula, Jagat Nadkarni, Girish N Pass, Robert H Khera, Rohan Sakhuja, Ankit My, Vy Thi Ha Gelb, Bruce D Duong, Son Q Butler, Liam R Charney, Alexander W Lampert, Joshua Do, Ron |
AuthorAffiliation | 10 Center for Outcomes Research and Evaluation, Yale‐New Haven Hospital New Haven CT 2 The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai New York NY 6 Mount Sinai Heart, Icahn School of Medicine at Mount Sinai New York NY 11 Division of Cardiovascular Critical Care, Department of Cardiac and Thoracic Surgery West Virginia University Morgantown WV 13 The Division of Data Driven and Digital Medicine (D3M), Department of Medicine Icahn School of Medicine at Mount Sinai New York NY 9 Biomedical Informatics and Data Science, Yale School of Medicine New Haven CT 5 Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY 3 Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai New York NY 7 Section of Cardiovascular Medicine, Department of Internal Medicine Yale School of Medicine New Haven CT 1 Division of Pediatric Cardiology, Department of Pediatrics Icahn School of Medicine at Mou |
AuthorAffiliation_xml | – name: 3 Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai New York NY – name: 11 Division of Cardiovascular Critical Care, Department of Cardiac and Thoracic Surgery West Virginia University Morgantown WV – name: 7 Section of Cardiovascular Medicine, Department of Internal Medicine Yale School of Medicine New Haven CT – name: 13 The Division of Data Driven and Digital Medicine (D3M), Department of Medicine Icahn School of Medicine at Mount Sinai New York NY – name: 9 Biomedical Informatics and Data Science, Yale School of Medicine New Haven CT – name: 12 Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai New York NY – name: 4 Helmsley Center for Electrophysiology at The Mount Sinai Hospital New York NY – name: 8 Section of Health Informatics, Department of Biostatistics Yale School of Public Health New Haven CT – name: 1 Division of Pediatric Cardiology, Department of Pediatrics Icahn School of Medicine at Mount Sinai New York NY – name: 5 Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY – name: 10 Center for Outcomes Research and Evaluation, Yale‐New Haven Hospital New Haven CT – name: 2 The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai New York NY – name: 6 Mount Sinai Heart, Icahn School of Medicine at Mount Sinai New York NY |
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Keywords | ECG deep learning cardiac MRI right ventricle |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 For Sources of Funding and Disclosures, see page 14. Preprint posted on MedRxiv April 26, 2023. doi: https://doi.org/10.1101/2023.04.25.23289130. S. Q. Duong and A. Vaid are co‐first authors. This manuscript was sent to Barry London, MD, PhD, Senior Guest Editor, for review by expert referees, editorial decision, and final disposition. Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.123.031671 |
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Snippet | Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG... Background Right ventricular ejection fraction (RVEF) and end‐diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep... BACKGROUNDRight ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep... Background Right ventricular ejection fraction (RVEF) and end‐diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep... |
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SubjectTerms | cardiac MRI deep learning ECG Electrocardiography Heart Humans Magnetic Resonance Imaging - methods Original Research right ventricle Stroke Volume Ventricular Dysfunction, Right Ventricular Function, Right |
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Title | Quantitative Prediction of Right Ventricular Size and Function From the ECG |
URI | https://www.ncbi.nlm.nih.gov/pubmed/38156471 https://www.proquest.com/docview/2908124334/abstract/ https://pubmed.ncbi.nlm.nih.gov/PMC10863807 https://doaj.org/article/5493fdcd7dd54c148a3db367814dc8e4 |
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