Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease

Elias, Pierre; Poterucha, Timothy J.; Rajaram, V.; Moller, Luca Matos; Rodriguez, Victor A.; Bhave, Shreyas; Hahn, Rebecca T.; Tison, Geoffrey H.; Abreau, Sean; Barrios, Joshua; Torres, Jessica Nicole; Hughes, J. Weston; Pérez, Marco; Finer, Joshua; Kodali, Susheel; Khalique, Omar; Hamid, Nadira; Schwartz, Allan; Homma, Shunichi; Kumaraiah, Deepa; Cohen, David J.; Maurer, Mathew S.; Nazif, Tamim; Leon, Martin B.

doi:10.1016/j.jacc.2022.05.029

Cited by 58 publications

(36 citation statements)

References 25 publications

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“…Additionally, ECG-predicted RVEF was associated with combined outcome of death/heart transplant independent of demographics and LV function in short term follow-up, which demonstrates the potential clinical utility of this algorithm for prediction of patient outcome. DL-ECG models have been shown to predict left ventricular systolic dysfunction, elevated left ventricular mass, and primarily left-sided structural heart disease 14,[18][19][20] .…”

Section: Discussionmentioning

confidence: 99%

Quantitative prediction of right ventricular and size and function from the electrocardiogram

Duong

Vaid

et al. 2023

Preprint

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Background. Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep-learning enabled 12-lead electrocardiogram analysis (DL-ECG) for estimation of RV size or function is unexplored. Methods. We trained a DL-ECG model to predict RV dilation (RVEDV>120 mL/m2), RV dysfunction (RVEF<40%), and numerical RVEDV/RVEF from 12-lead ECG paired with reference-standard cardiac MRI (cMRI) volumetric measurements in UK biobank (UKBB; n=42,938). We fine-tuned in a multi-center health system (MSHoriginal; n=3,019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance using area under the receiver operating curve (AUROC) for categorical and mean absolute error (MAE) for continuous measures overall and in key subgroups. We assessed association of RVEF prediction with transplant-free survival with Cox proportional hazards models. Results. Prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model AUROC for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. Prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model AUROC for UKBB/MSHoriginal/MSHvalidation cohorts 0.91/0.81/0.92, respectively. MSHoriginal MAE was RVEF=7.8% and RVEDV=17.6 ml/m2. Performance was similar in key subgroups including with and without left ventricular dysfunction. Over median follow-up of 2.3 years, predicted RVEF was independently associated with composite outcome (HR 1.37 for each 10% decrease, p=0.046). Conclusions. DL-ECG analysis can accurately identify significant RV dysfunction and dilation both overall and in key subgroups. Predicted RVEF is independently associated with clinical outcome.

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Section: Discussionmentioning

confidence: 99%

Quantitative prediction of right ventricular and size and function from the electrocardiogram

Duong

Vaid

et al. 2023

Preprint

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“…Input features were restricted to those available in our EHR data warehouse, and our study only included patients from one health system, which may result in selection bias. Other biologically measured features, such as electrocardiographic tracings that have been shown to have predictive value for disease state classification and prognosis [32][33][34] , were intentionally not incorporated into this analysis to test the scalability of our approach using only EHR-derived traits.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised Clustering Applied to Electronic Health Record-derived Phenotypes in Patients with Heart Failure

Reza¹,

Yang

Bone

et al. 2022

Preprint

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BackgroundHigh-dimensional electronic health records (EHR) data can be used to phenotype complex diseases. The aim of this study is to apply unsupervised clustering to EHR-based traits derived in a cohort of patients with heart failure (HF) from a large integrated health system.MethodsUsing the institutional EHR, we identified 8569 patients with HF and extracted 1263 EHR-based input features, including clinical, echocardiographic, and comorbidity data, prior to the time of HF diagnosis. Principal component analysis, Uniform Manifold Approximation and Projection, and spectral clustering were applied to the input features after sex stratification of the cohort. The optimal number of clusters for each sex-stratified group was selected by highest Silhouette score and by within-cluster and between-cluster sums of squares. Determinants of cluster assignment were evaluated.ResultsWe identified four clusters in each of the female-only (44%) and male-only (56%) cohorts. Sex-specific cohorts differed significantly by age of HF diagnosis, left ventricular chamber size, markers of renal and hepatic function, and comorbidity burden (all p<0.001). Left ventricular ejection fraction was not a strong driver of cluster assignment.ConclusionReadily available EHR data collected in the course of routine care can be leveraged to accurately classify patients into major phenotypic HF subtypes using data driven approaches.

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“…Others have similarly used DNN-based approaches to accomplish a variety of novel tasks using ECGs. Examples include detection of hypertrophic cardiomyopathy (HCM), 9 , 10 pulmonary hyptertension, 9 , 11 amyloid, 9 , 12 mitral valve prolapse, 9 mitral and aortic regurgitation, 13 aortic valve stenosis, 13 , 14 hyperkalemia, 15 and mortality risk estimation. 16 , 17 …”

Section: Recent Advances and Applications Of ML In Cardiovascular Med...mentioning

confidence: 99%

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease

Cited by 58 publications

References 25 publications

Quantitative prediction of right ventricular and size and function from the electrocardiogram

Quantitative prediction of right ventricular and size and function from the electrocardiogram

Unsupervised Clustering Applied to Electronic Health Record-derived Phenotypes in Patients with Heart Failure

Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective

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