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Aims Outcomes in pulmonary hypertension (PH) are related to right ventricular (RV) function and remodelling. We hypothesized that changes in RV function and especially area strain (AS) could provide incremental prognostic information compared to the use of baseline data only. We therefore aimed to assess RV function changes between baseline and 6-month follow-up and evaluate their prognostic value for PH patients using 3D echocardiography. Methods and results Ninety-five PH patients underwent a prospective longitudinal study including ESC/ERS guidelines prognostic assessment and 3D RV echocardiographic imaging at baseline and 6-month follow-up. Semi-automatic software tracked the RV along the cycle, and its output was post-processed to extract 3D deformation patterns. Over a median follow-up of 24.8 (22.1–25.7) months, 21 patients died from PH or were transplanted. Improvements in RV global AS were associated with stable or improving clinical condition as well as survival free from transplant (P < 0.001). The 3D deformation patterns confirmed that the most significant regional changes occurred within the septum. RV global AS change over 6-month by +3.5% identifies patients with a 3.7-fold increased risk of death or transplant. On multivariate COX analysis, changes in WHO class, BNP, and RV global AS were independent predictors of outcomes. Besides, the combination of these three parameters was of special interest to identify high-risk patients [HR 11.5 (1.55–86.06)]. Conclusion Changes in RV function and especially changes in 3D RV AS are of prognostic importance. Our study underlines that assessing such changes from baseline to follow-up is of additional prognostic value for PH patients. Clinical Trial Registration http://clinicaltrials.gov/ct2/show/NCT02799979

583 Added value of a statistical atlas-based quantification of motion abnormalities for the prediction of CRT response {#article-title-2} Purpose: The presence of septal flash (SF), a specific inward/outward motion pattern of the septum during the isovolumic contraction period, recently shown up as good predictor of cardiac resynchronization therapy (CRT) outcome. The ability of accurately identifying a SF fully conditions the predictive value of this mechanism. Current SF detection from US images is based on visual inspection only and suffers low reproducibility. Thus, we created an atlas-based statistical method for the automatic quantification of motion abnormalities that we apply to SF detection, and evaluated its predicting capability. Method: We built an atlas of normal motion from 21 healthy volunteers (age 30±5y, 14 male), to which we compared 75 CRT candidates (age 68±9y, 56 male) with left ventricle dyssynchrony. Abnormal motion is quantified locally in apical 4-chamber 2DUS sequences, from a p-value based on a statistical distance to normality. SF is identified using automatic recognition of the inward/outward events on spatiotemporal abnormality maps, as shown in Fig1. CRT response was defined at 6 months follow-up, as increase ≥10% in the 6' walking test or NYHA functional class reduction ≥1pt. Results: Comparison of atlas-based and visual assessment of SF leads to a Cohen's Kappa of 0.54 and an observed agreement of 0.81 (48 SF and 13 without SF). We reached a positive predictive value of CRT outcome of 85%, the visual assessment one being of 83%. Conclusion: We have demonstrated the potential of an atlas-based automatic quantification of motion abnormalities for the understanding of CRT outcome. In particular, we have demonstrated its performance for the prediction of clinical response when the methodology is applied to the identification of SF[⇓][1]. # 584 How reliably can myocardial blood flow be tracked? – a validation of commercial tracking software using computer-generated datasets {#article-title-3} Background: Recent advances in myocardial tracking technology have stimulated attempts to also track contrast enhanced intracavitary blood flow. Little is known, however, how basic imaging parameters (line density, frame rate, contrast bubble density) affect the quality of such tracking results. Our study aimed at investigating this by using simulated echo data sets. Methods: A 3D blood flow field of the entire left ventricle (LV) was calculated for a cardiac cycle based on mitral inflow and LV volume data using Fluent 12.1 (ANSYS Inc., USA). Then, the 3D motion of contrast microbubbles was simulated and 2D B-mode image loops were obtained (f = 4.5MHz; 50° sector angle). After conversion to DICOM, image loops were analyzed using flow tracking software (Qflow, Siemens, Mountainview, USA). Vorticity and amplitude of the resulting in-plane velocity vector field was calculated at different frame rates (227, 113 and 76 fps) and bubble densities (BD) (60, 35 and 18 B/ml) and compared to the ground truth known from the CFD model. Results: Quality of tracking was clearly related to BD and frame rate. Velocities, estimated by tracking, correlated best with the ground truth at 60 B/ml and 227fps (r = 0.61, p < 0.01) and deteriorated with lower settings (e.g. r = 0.25 at 60 B/ml and 76 fps). Estimated averaged vorticity also correlated best with the ground truth at 60 B/ml and 227fps. Interestingly, at low frame rates (76fps), tracking results improved with decreasing BD (r = 0.57 at 60 B/ml vs. r = 0.67 at 18 B/ml). Conclusions: Flow tracking by contrast enhanced echocardiography is feasible. Good tracking requires high frame rates and an optimized bubble density. Currently available tracking software shows acceptable quality of velocity estimates, facilitating the recognition of basic flow patterns, such as vortices[⇓][2]. # 585 Early detection of functional abnormalities in asymptomatic arrhythmogenic right ventricular cardiomyopathy gene carriers using echocardiographic deformation imaging {#article-title-4} Purpose: The first presentation of arrhythmogenic right ventricular cardiomyopathy (ARVC) is often potentially lethal ventricular arrhythmias originating from the right ventricle (RV), typically at a young age. This emphasizes the importance of an early recognition of this disease, for instance in ARVC- family members. The aim of this study is to evaluate the value of tissue deformation imaging to detect subclinical RV functional abnormalities in asymptomatic genotyped carriers of ARVC. Methods: A total of 43 asymptomatic first degree family members of ARVC probands (not fulfilling the diagnosis of ARVC according to the task-force criteria (TF-c)) were prospectively enrolled for echocardiographic examination. In a total of 14 (38.0±13.2 years), a genetic mutation (PKP2) could be identified (others had no mutation or genetic screening). All individuals were age-matched with 4 controls (n = 56, 38.2±12.7 years) undergoing the same echocardiographic evaluation (dimensions, global systolic parameters, visual assessment, and deformation imaging of the RV free wall). Echocardiographic evaluation was performed blinded. Deformation analysis was analyzed blinded to group and findings from the conventional echocardiogram. A peak systolic strain >−18% and/or post-systolic shortening (post-systolic index >15%) in any segment was considered abnormal. Results: No significant differences in baseline characteristics were seen between the groups. RV dimensions in the family group were similar to the controls (RVOT 15.4±2.9 vs. 14.4±1.9 mm/m2, RVIT 18.6±2.6 vs. 19.1±2.6 mm/m2, p=NS). Global systolic parameters were moderately reduced in the family group (TVI-syst 9.1±1.6 vs. 11.1±1.7 cm/s, TAPSE 20.0±3.2 vs. 23.9±2.8 mm, p −18% was seen in 6 family members (43%) and post systolic strain in 10 (71%). Either abnormality was observed in 11 (79%), almost exclusively in the basal segment, and in non of the controls. 2D-strain showed abnormal segments in 8 (57%) of family members and 5 (9%) controls. Conclusion: Echocardiographic deformation imaging detects functional abnormalities in the basal RV segment in almost 80% of asymptomatic ARVC gene carriers. Furthermore, false positive findings in visual assessment (28%) could be prevented since all showed normal deformation values and patterns. [1]: #F1 [2]: #F2