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Rare variants in the T-box transcription factor 4 gene (TBX4) have recently been recognised as an emerging cause of paediatric pulmonary hypertension (PH). Their pathophysiology and contribution to persistent pulmonary hypertension in neonates (PPHN) are unknown. We sought to define the spectrum of clinical manifestations and histopathology associated with TBX4 variants in neonates and children with PH.We assessed clinical data and lung tissue in 19 children with PH, including PPHN, carrying TBX4 rare variants identified by next-generation sequencing and copy number variation arrays.Variants included six 17q23 deletions encompassing the entire TBX4 locus and neighbouring genes, and 12 likely damaging mutations. 10 infants presented with neonatal hypoxic respiratory failure and PPHN, and were subsequently discharged home. PH was diagnosed later in infancy or childhood. Three children died and two required lung transplantation. Associated anomalies included patent ductus arteriosus, septal defects, foot anomalies and developmental disability, the latter with a higher prevalence in deletion carriers. Histology in seven infants showed abnormal distal lung development and pulmonary hypertensive remodelling.TBX4 mutations and 17q23 deletions underlie a new form of developmental lung disease manifesting with severe, often biphasic PH at birth and/or later in infancy and childhood, often associated with skeletal anomalies, cardiac defects, neurodevelopmental disability and other anomalies.

Background: The World Alliance Societies of Echocardiography (WASE) Normal Values Study evaluates individuals from multiple countries and races with the aim of describing normative values that could be applied to the global community worldwide and to determine differences and similarities among people from different countries and races. The present report focuses specifically on two-dimensional (2D) left ventricular (LV) dimensions, volumes, and systolic function. Methods: The WASE Normal Values Study is a multicenter international, observational, prospective, crosssectional study of healthy adult individuals. Participants recruited in each country were evenly distributed among six predetermined subgroups according to age and gender. Comprehensive 2D transthoracic echocardiograms were acquired and analyzed following strict protocols based on recent American Society of Echocardiography and European Association of Cardiovascular Imaging guidelines. Analysis was performed at the WASE 2D core laboratory and included 2D LV dimensions, LV volumes, and LV ejection fraction (LVEF) by the biplane Simpson method and global longitudinal strain (GLS). Results: Two thousand eight subjects were enrolled in 15 countries. The median age was 45 years (interquartile range, 32-65 years), 42.8% were white, 41.8% were Asian, and 9.7% were black. LV dimensions and volumes were larger in male subjects, while LVEF and GLS were higher in female subjects. Global

Background Associating a patient’s profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography (STE) data sets derived from patients with known constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Methods and Results Clinical and echocardiographic data of 50 patients with CP and 44 with RCM were used for developing an associative memory classifier (AMC) based machine learning algorithm. The STE data was normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve (AUC) of the AMC was evaluated for differentiating CP from RCM. Using only STE variables, AMC achieved a diagnostic AUC of 89·2%, which improved to 96·2% with addition of 4 echocardiographic variables. In comparison, the AUC of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63·7%, respectively. Furthermore, AMC demonstrated greater accuracy and shorter learning curves than other machine learning approaches with accuracy asymptotically approaching 90% after a training fraction of 0·3 and remaining flat at higher training fractions. Conclusions This study demonstrates feasibility of a cognitive machine learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.

The SUCCOUR trial will be the first randomized controlled trial of GLS and will provide evidence to inform guidelines regarding the place of GLS for surveillance for CTRCD. (Strain sUrveillance of Chemotherapy for improving Cardiovascular Outcomes [SUCCOUR]; ANZ Clinical Trials ACTRN12614000341628).

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