Right ventricular (RV) function has proven to be a prognostic factor in heart failure with reduced and preserved ejection fraction and in pulmonary hypertension. RV function is also a cornerstone in the management of novel clinical issues, such as mechanical circulatory support devices or grown-up congenital heart disease patients. Despite the notable amount of circumferentially oriented myofibers in the subepicardial layer of the RV myocardium, the non-longitudinal motion directions are often neglected in the everyday assessment of RV function by echocardiography. However, the complex RV contraction pattern incorporates different motion components along three anatomically relevant axes: longitudinal shortening with traction of the tricuspid annulus towards the apex, radial motion of free wall often referred as the “bellows effect”, and anteroposterior shortening of the chamber by stretching the free wall over the septum. Advanced echocardiographic techniques, such as speckle-tracking and 3D echocardiography allow an in-depth characterization of RV mechanical pattern, providing better understanding of RV systolic and diastolic function. In our current review, we summarize the existing knowledge regarding RV mechanical adaptation to pressure- and/or volume-overloaded states and also other physiologic or pathologic conditions.

Aims Right atrial (RA) size predicts the outcome in some pathological conditions but reference values for RA volumes and myocardial function remain to be defined. Thus, we used two-dimensional speckle-tracking echocardiography (2D-STE) and three-dimensional echocardiography (3DE) to define normative reference values of RA volumes and function. Methods and results Two hundreds healthy volunteers (43 ± 15 years, range 18–75; 44% men) underwent two-dimensional echocardiography (2DE) to obtain RA volumes and longitudinal strain (LS) of RA wall using 2D-STE, and 3DE to measure maximal ( V max), minimal, and preA volumes to calculate total, passive, and active emptying volumes (TotEV, PassEV, and ActEV) and emptying fractions (TotEF, PassEF, and ActEF). Three-dimensional echocardiography volumes ( V max, 52 ± 15 mL vs. 41 ± 14 mL), EVs (TotEV, 33 ± 10 mL vs. 24 ± 9 mL), and EFs (TotEF, 63 ± 9 vs. 58 ± 9%) were larger than 2DE ones (all P < 0.0001). Indexed 3D volumes were significantly larger in men than in women. RA TotEF correlated with total LS ( r = 0.24, P = 0.025) and PassEF with positive LS (LSpos; r = 0.34, P < 0.0001). Ageing was associated with a decrease in passive (LSpos, r = −041; PassEV, r = −0.26; PassEF, r = −0.38; all P < 0.0001) and an increase in active RA function (negative LS, r = 0.34; ActEV, r = 0.25; all P < 0.0001; and ActEF, r = 0.15; P = 0.035) in order to maintain TotEV ( r = −0.14, P = 0.05). Conclusion Our study provides normative values for RA volumes and function measured by 3DE and 2D-STE in a relatively large cohort of healthy subjects with a wide age range. These data will help clinicians to identify RA remodelling and dysfunction.

Our aim was to develop a machine learning (ML)-based risk stratification system to predict 1-, 2-, 3-, 4-, and 5-year all-cause mortality from pre-implant parameters of patients undergoing cardiac resynchronization therapy (CRT). Multiple ML models were trained on a retrospective database of 1510 patients undergoing CRT implantation to predict 1- to 5-year all-cause mortality. Thirty-three pre-implant clinical features were selected to train the models. The best performing model [SEMMELWEIS-CRT score (perSonalizEd assessMent of estiMatEd risk of mortaLity With machinE learnIng in patientS undergoing CRT implantation)], along with pre-existing scores (Seattle Heart Failure Model, VALID-CRT, EAARN, ScREEN, and CRT-score), was tested on an independent cohort of 158 patients. There were 805 (53%) deaths in the training cohort and 80 (51%) deaths in the test cohort during the 5-year follow-up period. Among the trained classifiers, random forest demonstrated the best performance. For the prediction of 1-, 2-, 3-, 4-, and 5-year mortality, the areas under the receiver operating characteristic curves of the SEMMELWEIS-CRT score were 0.768 (95% CI: 0.674–0.861; P < 0.001), 0.793 (95% CI: 0.718–0.867; P < 0.001), 0.785 (95% CI: 0.711–0.859; P < 0.001), 0.776 (95% CI: 0.703–0.849; P < 0.001), and 0.803 (95% CI: 0.733–0.872; P < 0.001), respectively. The discriminative ability of our model was superior to other evaluated scores. The SEMMELWEIS-CRT score (available at semmelweiscrtscore.com) exhibited good discriminative capabilities for the prediction of all-cause death in CRT patients and outperformed the already existing risk scores. By capturing the non-linear association of predictors, the utilization of ML approaches may facilitate optimal candidate selection and prognostication of patients undergoing CRT implantation.

De novo implanted cardiac resynchronization therapy with defibrillator (CRT-D) reduces the risk of morbidity and mortality in patients with left bundle branch block, heart failure and reduced ejection fraction (HFrEF). However, among HFrEF patients with right ventricular pacing (RVP), the efficacy of CRT-D upgrade is uncertain. In this multicentre, randomized, controlled trial, 360 symptomatic (New York Heart Association Classes II–IVa) HFrEF patients with a pacemaker or implantable cardioverter defibrillator (ICD), high RVP burden ≥ 20%, and a wide paced QRS complex duration ≥ 150 ms were randomly assigned to receive CRT-D upgrade (n = 215) or ICD (n = 145) in a 3:2 ratio. The primary outcome was the composite of all-cause mortality, heart failure hospitalization, or <15% reduction of left ventricular end-systolic volume assessed at 12 months. Secondary outcomes included all-cause mortality or heart failure hospitalization. Over a median follow-up of 12.4 months, the primary outcome occurred in 58/179 (32.4%) in the CRT-D arm vs. 101/128 (78.9%) in the ICD arm (odds ratio 0.11; 95% confidence interval 0.06–0.19; P < .001). All-cause mortality or heart failure hospitalization occurred in 22/215 (10%) in the CRT-D arm vs. 46/145 (32%) in the ICD arm (hazard ratio 0.27; 95% confidence interval 0.16–0.47; P < .001). The incidence of procedure- or device-related complications was similar between the two arms [CRT-D group 25/211 (12.3%) vs. ICD group 11/142 (7.8%)]. In pacemaker or ICD patients with significant RVP burden and reduced ejection fraction, upgrade to CRT-D compared with ICD therapy reduced the combined risk of all-cause mortality, heart failure hospitalization, or absence of reverse remodelling. Main inclusion criteria and randomization by arms showing the result of 360 patients (215 CRT-D vs. 145 ICD) analysed by intention to treat. Primary and secondary outcomes showing a substantial treatment effect of CRT-D compared with ICD alone. Among patients with HF and reduced LVEF with intermittent or permanent RV pacing, CRT-D upgrade resulted in a lower incidence of the composite of all-cause mortality, HF hospitalization, or <15% decrease in LVESV as the primary outcome. Secondary outcome of all-cause mortality and HF hospitalization also proved a substantially lower incidence in CRT-D patients compared with ICD. CI, confidence interval; CRT-D, cardiac resynchronization therapy with defibrillator; HF, heart failure; HR, hazard ratio; ICD, implantable cardioverter defibrillator; LVEF, left ventricular ejection fraction; LVESV, left ventricular end-systolic volume; OR, odds ratio; RV, right ventricular.

Evidence has shown the independent prognostic value of right ventricular (RV) function, even in patients with left-sided heart disease. The most widely used imaging technique to measure RV function is echocardiography; however, conventional 2-dimensional (2D) echocardiographic assessment is unable to leverage the same clinical information that 3-dimensional (3D) echocardiography-derived right ventricular ejection fraction (RVEF) can provide. The authors aimed to implement a deep learning (DL)-based tool to estimate RVEF from 2D echocardiographic videos. In addition, they benchmarked the tool's performance against human expert reading and evaluated the prognostic power of the predicted RVEF values. The authors retrospectively identified 831 patients with RVEF measured by 3D echocardiography. All 2D apical 4-chamber view echocardiographic videos of these patients were retrieved (n = 3,583), and each subject was assigned to either the training or the internal validation set (80:20 ratio). Using the videos, several spatiotemporal convolutional neural networks were trained to predict RVEF. The 3 best-performing networks were combined into an ensemble model, which was further evaluated in an external data set containing 1,493 videos of 365 patients with a median follow-up time of 1.9 years. The ensemble model predicted RVEF with a mean absolute error of 4.57 percentage points in the internal and 5.54 percentage points in the external validation set. In the latter, the model identified RV dysfunction (defined as RVEF <45%) with an accuracy of 78.4%, which was comparable to an expert reader's visual assessment (77.0%; P = 0.678). The DL-predicted RVEF values were associated with major adverse cardiac events independent of age, sex, and left ventricular systolic function (HR: 0.924; 95% CI: 0.862-0.990; P = 0.025). Using 2D echocardiographic videos alone, the proposed DL-based tool can accurately assess RV function, with similar diagnostic and prognostic power as 3D imaging.

Background: Angiotensin-converting enzyme inhibitors attenuate left ventricular (LV) enlargement after acute myocardial infarction (AMI). Preclinical data suggest similar benefits with combined angiotensin receptor neprilysin inhibition, but human data are conflicting. The PARADISE-MI Echo Study (Prospective ARNI Versus ACE Inhibitor Trial to Determine Superiority in Reducing Heart Failure Events After Myocardial Infarction) tested the effect of sacubitril/valsartan compared with ramipril on LV function and adverse remodeling after high risk-AMI. Methods: In a prespecified substudy, 544 PARADISE-MI participants were enrolled in the Echo Study to undergo protocol echocardiography at randomization and after 8 months. Patients were randomized within 0.5 to 7 days of presentation with their index AMI to receive a target dose of sacubitril/valsartan 200 mg or ramipril 5 mg twice daily. Echocardiographic measures were performed at a core laboratory by investigators blinded to treatment assignment. The effect of treatment on change in echo measures was assessed with ANCOVA with adjustment for baseline value and enrollment region. The primary end points were change in LV ejection fraction (LVEF) and left atrial volume (LAV), and prespecified secondary end points included changes in LV end-diastolic and end-systolic volumes. Results: Mean age was 64±12 years; 26% were women; mean LVEF was 42±12%; and LAV was 49±17 mL. Of 544 enrolled patients, 457 (84%) had a follow-up echo at 8 months (228 taking sacubitril/valsartan, 229 taking ramipril). There was no significant difference in change in LVEF ( P =0.79) or LAV ( P =0.62) by treatment group. Patients randomized to sacubitril/valsartan demonstrated less increase in LV end-diastolic volume ( P =0.025) and greater decline in LV mass index ( P =0.037), increase in tissue Doppler e’ _lat ( P =0.005), decrease in E/e’ _lat ( P =0.045), and decrease in tricuspid regurgitation peak velocity ( P =0.024) than patients randomized to ramipril. These differences remained significant after adjustment for differences in baseline characteristics. Baseline LVEF, LV end-diastolic volume, LV end-systolic volume, LV mass index, LAV, and Doppler-based diastolic indices were associated with risk of cardiovascular death or incident heart failure. Conclusions: Treatment with sacubitril/valsartan compared with ramipril after AMI did not result in changes in LVEF or LAV at 8 months. Patients randomized to sacubitril/valsartan had less LV enlargement and greater improvement in filling pressure. Measures of LV size, systolic function, and diastolic properties were predictive of cardiovascular death and incident heart failure after AMI in this contemporary, well-treated cohort. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02924727.