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

ABSTRACT 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/m 2 ), 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 (MSH original ; n=3,019) with prospective validation over 4 months (MSH validation ; 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/MSH original /MSH validation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model AUROC for UKBB/MSH original /MSH validation cohorts was 0.86/0.81/0.77, respectively. Prevalence of RV dilation for UKBB/MSH original /MSH validation cohorts was 1.6%/10.6%/4.3%. RV dilation model AUROC for UKBB/MSH original /MSH validation cohorts 0.91/0.81/0.92, respectively. MSH original MAE was RVEF=7.8% and RVEDV=17.6 ml/m 2 . 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.