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 inJournal of the American Heart Association Vol. 13; no. 1; p. e031671
Main Authors Duong, Son Q, Vaid, Akhil, My, Vy Thi Ha, Butler, Liam R, Lampert, Joshua, Pass, Robert H, Charney, Alexander W, Narula, Jagat, Khera, Rohan, Sakhuja, Ankit, Greenspan, Hayit, Gelb, Bruce D, Do, Ron, Nadkarni, Girish N
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LanguageEnglish
Published England John Wiley and Sons Inc 02.01.2024
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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
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DocumentTitleAlternate ECG Prediction of RV Size and Function
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Keywords ECG
deep learning
cardiac MRI
right ventricle
Language English
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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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StartPage e031671
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
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