The Women's and Children's Hospital is located on King William Road in North Adelaide, Australia.It is one of the major hospitals in Adelaide and is a teaching hospital of the University of Adelaide, the University of South Australia and Flinders University.It was created through the amalgamation of the Queen Victoria Hospital and Adelaide Children's Hospital in March 1989. The new (in name) hospital occupies the site of the former Adelaide Children's Hospital.The hospital is part of the Children, Youth and Women's Health Service along with the Child and Youth Health.The Children's and adolescents' wards cater for all paediatric specialities. The women's wards cater for antenatal, gynaecology, neonatal, and postnatal disciplines.The Women's & Children's Hospital Foundation is the primary charity for the hospital and exists to raise money and invest initiatives that support the care and future health of South Australia's women, babies and children.The hospital is part of the wider Women's and Children's Health Network, which includes the Child and Family Health Service, Child and Adolescent Mental Health Service, Metropolitan Youth Health, Women's Health, Yarrow Place, Torrens House and Helen Mayo House.The official Facebook Page of the Women's and Children's Hospital and Health Network is @WCHNetwork.A new hospital will be built next to the New Royal Adelaide Hospital, near the river Torrens.
The use of standardised structured radiology reports improves the consistency, reproducibility and overall quality of radiological reporting while enhancing communication with referring physicians and ultimately contributing to improved patient care. To develop a standardised structured report template for foetal and neonatal postmortem magnetic resonance imaging through expert consensus. A Delphi survey was conducted between September and December 2025 among members of the ESPR Postmortem Task Force and other recommended international PM imaging experts. The surveyed items were derived from clinically used MRI reporting templates across the expert group. Consensus was defined using a ≥75
Syndromic cardiac malformations can result in morbidity, yet their genetic etiology is only understood for a subset of individuals. Genome sequencing efforts in congenital anomaly cohorts may identify disease-associated variants in previously unrecognized genes. Through international matchmaking efforts, we identified eighteen individuals in total with de novo or loss-of-function variants in EIF3A (n = 4) or EIF3B (n = 14). The clinical phenotype varied but predominantly included cardiac defects, craniofacial dysmorphisms, mild developmental delays, and behavioral abnormalities. These genes encode core subunits of the eukaryotic initiation factor 3 (eIF3) complex, which plays a critical role in binding mRNA transcripts to the 40S ribosomal subunit during translation initiation. Both genes are highly constrained against loss of function, and animal models have demonstrated that disruptions in the eIF3 complex result in a range of developmental defects, including cardiovascular malformations. Additionally, EIF3B is located within the minimally overlapping region implicated in cardiac anomalies associated with 7p22.3 microdeletions. We sought to further study the role of these genes in syndromic congenital heart disease. To explore their functional impact, we generated zebrafish models with mutations in the orthologous eif3s10 and eif3ba genes, which resulted in developmental abnormalities, including thin heart tubes, lack of craniofacial cartilage, and embryonic lethality. We propose that pathogenic variants in EIF3A, as well as pathogenic variants or microdeletions involving EIF3B, cause a distinct autosomal-dominant neurodevelopmental syndrome characterized by cardiovascular and craniofacial manifestations.
OBJECTIVE:The Fontan operation is the final step in staged palliation for patients with single-ventricle physiology. It has extended their life expectancy and improved their quality of life. However, long-term complications and Fontan failure remain lifelong concerns. We aimed to use machine learning to develop a patient-specific preoperative Fontan failure risk calculator. METHODS:Patient data were obtained from the Australia and New Zealand Fontan Registry (ANZFR). The primary composite end point was Fontan failure, defined as any of death, transplant, Fontan takedown or conversion, protein-losing enteropathy, plastic bronchitis, or New York Heart Association class III/IV. To construct the risk calculator, we first used Cox regression with regularization to predict Fontan failure from 54 preoperative predictors in the ANZFR database. A regularization machine learning tool was used to automate variable selection among many predictors. We then manually added clinically relevant predictors. Six predictors (age, ventricular morphology, primary diagnosis, total anomalous pulmonary venous drainage, Fontan type, and moderate or greater atrioventricular valve regurgitation) were ultimately used in a subsequent multivariable Cox regression (without regularization) to ensure the final risk prediction model was simple and easy to interpret. RESULTS:Data from 1888 patients over 48 years (1975-2023) were available. The ANZFR collects perioperative and follow-up variables about each patient. After excluding patients with Fontan procedures with an atriopulmonary connection (n = 290) and missing predictors or outcome data (n = 125), data from 1473 patients were used to construct the calculator. Median age at Fontan was 4.5 years (interquartile range, 3.7, 5.6 years). Median follow-up was 11.0 years (interquartile range, 5.3, 17.8 years). Freedom from Fontan failure for the overall cohort at 10, 20, and 30 years was 92% (confidence interval [CI], 90%-93%), 83% (CI, 80%-86%), and 72% (CI, 65%-78%), respectively. External validation in an independent cohort demonstrated acceptable model performance. The risk prediction model was then implemented in a Desktop application using the Shiny library in R and used to develop the preoperative Fontan failure calculator on the basis of the 6 predictors. CONCLUSIONS:Machine learning can be applied to "big data" from a binational Fontan Registry to develop a preoperative, patient-specific Fontan failure risk calculator. The model will continue to learn and improve as more data is added. This is a step toward personalized medicine enabling patient-specific pre-operative counselling and realistic expectations.
Household overcrowding is a major driver of acute rheumatic fever and rheumatic heart disease, along with other adverse social, cultural and health outcomes in remote Aboriginal communities. Overcrowding is compounded by poor thermal performance of current housing, energy insecurity and climate change. Despite strong evidence of the causes of rheumatic heart disease, upstream prevention through housing design remains underexplored. Wilya Janta, an Aboriginal-led organisation in Tennant Creek, has developed the Explain Home design: a culturally responsive, climate-adapted prototype designed to reduce overcrowding-related harms. With an unprecedented $4 billion investment in remote housing, health professionals have a critical role in advocating for evidence-informed, culturally safe housing as a form of preventive health intervention to improve equity and outcomes.
The increasing application of molecular diagnostics in pathology has enabled the identification of novel tumor entities, including clear cell tumor with MITF::CREM translocation, as recognized in the 5th edition of the WHO Classification of Tumours. We describe a case involving a 2-year-old boy with a left scalp lesion, initially suspected clinically to represent a pyogenic granuloma. Histopathological assessment, corroborated by molecular analysis, established the diagnosis of clear cell tumor with MITF::CREM translocation. This case emphasizes the need to consider rare entities in the differential diagnosis of dermal lesions and highlights the critical role of molecular testing in diagnostic precision. Notably, this molecularly defined neoplasm demonstrated no immunohistochemical evidence of melanocytic differentiation, contrasting with the features described in the WHO classification.
