Pathological right ventricular changes in synthesized electrocardiogram in end-stage renal disease patients and their association with mortality and cardiac hospitalization: a cohort study

Assessment of right ventricular dysfunction and its association with excess risk of cardiovascular events in patients undergoing maintenance hemodialysis

AIMS

Recent accumulating evidence has recently documented a significant prevalence of right ventricular dysfunction (RVD) in end-stage renal disease (ESRD) patients. Tricuspid annular plane systolic excursion (TAPSE)/pulmonary-artery systolic pressure (PASP) ratio assessed with echocardiography might be a useful clinical index of right ventricular (RV) -pulmonary arterial (PA) coupling. The current study aimed to investigate the value of the TAPSE/PASP ratios in patients on maintenance hemodialysis (MHD).

METHODS

We studied 83 times echocardiographic tests from 68 patients with MHD. The associations of TAPSE/PASP ratios with echocardiography variables, clinical characteristics, and biochemical parameters were analyzed, as well as the associations of TAPSE/PASP ratios with odds of all-cause mortality, cardiovascular disease (CVD) events and frequent intermittent dialysis hypotension (IDH).

RESULTS

Correlation analysis showed TAPSE/PASP ratios positively correlated with LVEF and negatively correlated with E/A and E/e' values. For clinical and biochemical parameters, TAPSE/PASP ratios negatively correlated with BNP, NT-proBNP, age, CRP, and average interdialysis weight gain (ΔBW) and positively correlated with albumin. Logistic regression analysis, which induced the TAPSE/PASP ratio as a continuous variable (per 0.1 mm/mmHg increase), identified that the TAPSE/PASP ratio was associated with decreased CVD events (OR 0.386 [95% CI 0.231-0.645], p < 0.001) and frequent IDH odds (OR 0.571 [95% CI 0.397-0.820], p = 0.002). Moreover, the TAPSE/PASP ratio independently predicted CVD events (adjusted HR 0.539 [95% CI 0.391-0.743], p < 0.001) during a follow-up period of 12 months.

CONCLUSIONS

RVD, assessed by echocardiography TAPSE/PASP ratio, was found to be associated with increased risks of CVD events and frequent IDH in patients with MHD.

Quantitative Prediction of Right Ventricular Size and Function From the ECG

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.

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/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.