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Background Valvar abnormalities in children and adults with autosomal dominant polycystic kidney disease (ADPKD) have previously been reported as a frequent occurrence. Mitral valve prolapse (MVP), in particular, has been reported in almost one-third of adult patients and nearly 12% of children with ADPKD. Our objective in this study was to establish the prevalence of valvar abnormalities in a large, contemporary series of children and young people (CYP) with ADPKD. Methods A retrospective, single centre, cross-sectional analysis of the echocardiograms performed on all consecutive children seen in a dedicated paediatric ADPKD clinic. Full anatomical and functional echocardiograms were performed and analysed for valvar abnormalities. Results The echocardiograms of 102 CYP with ADPKD (range 0.25-18 years, mean age 10.3 years, SD ± 5.3 years) were analysed. One (0.98%), 3-year-old boy, had MVP. There was no associated mitral regurgitation. Evaluating variations in normal valvar anatomy, 9 (8.8%) patients, aged 7.1 to 18 years, had minor bowing ± visual elongation of either the anterior or posterior leaflet of the mitral valve, none of which fell within the criteria of true MVP. Three (1.9%) patients, 2 boys and 1 girl aged between 7 and 14 years, had trivial or mild aortic regurgitation. No patients had echocardiographic evidence of tricuspid valve prolapse (TVP). Conclusion In this contemporary cohort of CYP with ADPKD, the incidence of MVP and other valvar lesions is significantly lower than previously reported. Graphical abstract A higher resolution version of the Graphical abstract is available as Supplementary information., Author(s): Alexandra Savis [sup.1] , John M. Simpson [sup.1] , Saleha Kabir [sup.1] , Kelly Peacock [sup.1] , Hayley Beardsley [sup.1] , Manish D. Sinha [sup.2] [sup.3] Author Affiliations: (1) [...]

Fetal two‐dimensional speckle tracking echocardiography (2D‐STE) is a novel technique that provides information on fetal heart function by measuring global longitudinal strain (GLS) and global longitudinal strain rate (GLSR). These features assess the longitudinal deformity of the fetal cardiac wall. 2D‐STE is shown to be of prognostic value in children and adults with congenital heart disease (CHD). Therefore, its importance in fetal life should also be considered. This systematic review and meta‐analysis provides an overview of the literature on 2D‐STE (GLS/GLSR) in fetuses with CHD, focusing on the left and right ventricles (LV/RV). Findings indicated that LV‐GLS was significantly lower in fetuses with coarctation of the aorta (CoA) and Tetralogy of Fallot (ToF) compared to controls. Conversely, fetuses with a single left ventricle exhibited higher LV‐GLS. RV‐GLS was significantly lower in fetuses with hypoplastic left heart syndrome (HLHS) and ToF compared to controls. LV‐GLSR was significantly lower in fetuses with CoA. Overall, considerable heterogeneity was observed, possibly due to differences in study design. More prospective longitudinal studies on 2D‐STE in fetuses with CHD, considering heterogeneity parameters, could offer better insights into this promising technique. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) has the potential to improve prenatal detection of congenital heart disease. We analysed the performance of the current national screening programme in detecting hypoplastic left heart syndrome (HLHS) to compare with our own AI model. Methods: Current screening programme performance was calculated from local and national sources. AI models were trained using four‐chamber ultrasound views of the fetal heart, using a ResNet classifier. Results: Estimated current fetal screening programme sensitivity and specificity for HLHS were 94.3% and 99.985%, respectively. Depending on calibration, AI models to detect HLHS were either highly sensitive (sensitivity 100%, specificity 94.0%) or highly specific (sensitivity 93.3%, specificity 100%). Our analysis suggests that our highly sensitive model would generate 45,134 screen positive results for a gain of 14 additional HLHS cases. Our highly specific model would be associated with two fewer detected HLHS cases, and 118 fewer false positives. Conclusion: If used independently, our AI model performance is slightly worse than the performance level of the current screening programme in detecting HLHS, and this performance is likely to deteriorate further when used prospectively. This demonstrates that collaboration between humans and AI will be key for effective future clinical use. Key points: What is already known on this topic? Artificial intelligence (AI) can be used to interpret medical images and make diagnoses, including detecting fetal congenital heart disease (CHD) by ultrasound.The sensitivity of the current English screening programme for fetal cardiac malformations is publicly available, but specificity is not reported. What this study adds? The current screening programme in our region is operating at a very high specificity for fetal hypoplastic left heart syndrome (HLHS).Using a curated retrospective dataset, it is possible to train AI models to detect HLHS with a performance approaching that of the current screening programme.Current AI models do not have high enough specificity to be used independently for screening for fetal CHD, meaning that human‐AI interaction when performing or interpreting ultrasound will be important to select cases for specialist referral. [ABSTRACT FROM AUTHOR]