Key result
Deep learning-enabled ECG prediction of right ventricular ejection fraction was significantly associated with transplant-free survival (HR 1.40 for each 10% decrease, p=0.031).
Why the study?
Right ventricular ejection fraction and end-diastolic volume are not readily assessed through traditional modalities, and deep learning-enabled ECG analysis for estimating right ventricular size or function remains unexplored.
Does deep learning-enabled ECG analysis accurately predict right ventricular size and function, and correlate with clinical outcomes, in adults?
Cohort (n=46,072)
Yes
Does deep learning-enabled ECG analysis accurately predict right ventricular size and function, and correlate with clinical outcomes, in adults?
Answers are grounded in this paper and cite their sources.
Deep learning analysis of 12-lead ECGs can effectively detect right ventricular dysfunction and dilation, offering a widely accessible tool that correlates with risk of adverse cardiac outcomes.
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.
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Duong et al. (2023) conducted a cohort in Patients with clinically indicated cMRI and paired ECG (n=46,072). Deep learning-enabled ECG analysis predicting RV function vs. Reference-standard cMRI volumetric measurements was evaluated on Transplant-free survival (composite of death or heart transplantation) (HR 1.40, 95% CI 1.03-1.91, p=0.031). Deep learning-enabled ECG prediction of right ventricular ejection fraction was significantly associated with transplant-free survival (HR 1.40 for each 10% decrease, p=0.031).
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