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.

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.

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.

Global right ventricular (RV) function is determined by the interplay of different motion components related to the myofiber architecture, and the relative importance of these components is still not thoroughly characterized. The aims of this study were to quantify the relative contributions of longitudinal, radial, and anteroposterior motion components to global RV function and to examine their determining factors in a large cohort of healthy volunteers using three-dimensional echocardiography. Three hundred healthy adults with a balanced age range and an equal sex distribution were investigated at two centers. A three-dimensional mesh model of the right ventricle was generated, and its motion was decomposed along the three anatomically relevant axes. Multiplicative relative contributions were measured by dividing the ejection fraction (EF) values generated by shortening in the longitudinal, radial, and anteroposterior directions by global RV EF (longitudinal EF index [LEFi], radial EF index [REFi], and anteroposterior EF index, respectively). The circumferential contribution was defined as shortening in the radial and anteroposterior directions, omitting only longitudinal shortening. Circumferential EF index was markedly higher compared with LEFi (79 ± 7% vs 47 ± 9%, P < .001). LEFi (47 ± 9%) and anteroposterior EF index (49 ± 7%) were found to be similar in the pooled population, whereas REFi (44 ± 10%) was lower (P < .001). In younger individuals (20-39 years of age), the relative contribution of longitudinal shortening was significantly higher compared with the radial component; however, in the older age groups, LEFi and REFi were comparable. Age, body surface area, heart rate, and RV end-diastolic volume were independent predictors of LEFi and REFi, but all with opposite effects on the two motion directions. In contrast to the traditional viewpoint, the contributions of the radial and anteroposterior motion directions may be of comparable significance with that of longitudinal shortening in determining global RV function. Standard parameters referring only to longitudinal shortening of the right ventricle may be inadequate to characterize RV function thoroughly.