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

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