Rare diseases (RD) are collectively common and often genetic. Families value and can benefit from precise molecular diagnoses. Prolonged diagnostic odysseys exacerbate the burden of RD on patients, families and the healthcare system. Genome sequencing (GS) is a near-comprehensive test for genetic RD, but existing care models—where consultation with a medical geneticist is a prerequisite for testing—predate GS and may limit access or delay diagnosis. Evidence is needed to guide the optimal positioning of GS in care pathways. While initiating GS prior to geneticist consultation has been trialled in acute care settings, there are no data to inform the utility of this approach in outpatient care, where most patients with RD seek genetics services. We aim to evaluate the diagnostic yield, time to diagnosis, clinical and personal utility and incremental cost-effectiveness of GS initiated at the time of referral triage (pre-geneticist evaluation) compared with standard of care.

200 paediatric patients referred to one of two large genetics centres in Ontario, Canada, for suspected genetic RD will be randomised into a 1:1 ratio to the intervention (GS first) or standard of care (geneticist first) arm. An unblinded, permuted block randomisation design will be used, stratified within each recruitment site by phenotype and prior genetic testing. The primary outcome measure is time to genetic diagnosis or to cessation of active follow-up. Survival analysis will be used to analyse time-to-event data. Additional measures will include patient-reported and family-reported measures of satisfaction, understanding and perceived test utility, clinician-reported measures of perceived test utility and management impact, and healthcare system utilisation and costs.

This study was approved by Clinical Trials Ontario. Results will be disseminated, at minimum, via peer-reviewed journals, professional conferences and internal reports to funding bodies. Efforts will be made to share aggregated study results with participants and their families.

NCT06935019.

While survival rates following neonatal surgery for congenital heart disease (CHD) have improved over the years, neurodevelopmental delays are still highly prevalent in these patients. After correcting for the CHD subtype, the severity of developmental impairment is dependent on multiple factors, including intraoperative brain injury, which is more frequent and more severe in those undergoing aortic arch repair with deep hypothermic circulatory arrest (DHCA). It is proposed that brain injury may be reduced if cooling is stopped at the point of electrocerebral inactivity (ECI) on electroencephalogram (EEG), but there is limited evidence to support this as few centres perform perioperative EEG routinely. This study aims to assess the feasibility of EEG monitoring during neonatal aortic arch repair and investigate the relationship between temperature and EEG to inform the design of a future clinical trial.

Single-centre prospective observational cohort study in a UK specialist children’s hospital, aiming to recruit 74 neonates (≤4 weeks corrected age) undergoing aortic arch repair with DHCA. EEG will be acquired at least 1–3 hours before surgery, and brain activity will be monitored continuously until 24 hours following admission to intensive care. Demographic, clinical, surgical and outcome variables will be collected. Feasibility will be measured by the number of patients recruited, data collection procedures, technically successful EEG recordings and adverse events. The main outcomes are the temperature at which ECI is achieved and its duration, EEG patterns at key perioperative steps and neurodevelopmental outcomes at 24 months postsurgery.

The study was approved by the Yorkshire and The Humber Sheffield National Health Service Research Ethics Committee (20/YH/0192) on 18 June 2020. Written informed consent will be obtained from the participant’s parent/guardian prior to surgery. Findings will be disseminated to the academic community through peer-reviewed publications and presentations at conferences. Parents/guardians will be informed of the results through a newsletter in conjunction with local charities.

 Machine Learning (ML) has been transformative in healthcare, enabling more precise diagnostics, personalised treatment regimens and enhanced patient care. In cardiology, ML plays a crucial role in risk prediction and patient stratification, particularly for heart failure (HF), a condition affecting over 64 million people globally and imposing an economic burden of approximately $108 billion annually. ML applications in HF include predictive analytics for risk assessment, identifying patient subgroups with varying prognoses and optimising treatment pathways. By accurately predicting the likelihood of hospitalisation and rehospitalisation, ML tools help tailor interventions, reduce hospital visits, improve patient outcomes and lower healthcare costs.

To conduct a comprehensive review of existing ML models designed to predict hospitalisation risk in individuals with HF.

A database search including PubMed, SCOPUS and Web of Science was conducted on 31 March 2024. Studies were selected based on inclusion criteria focusing on ML models predicting hospitalisation risks in adults with HF. The data from 27 studies meeting the criteria were extracted and analysed, with a focus on the predictive performance of the ML models and the presence of economic analysis.

Most studies focused on predicting readmission rather than first-time hospitalisation. All included studies employed supervised ML algorithms, with ensemble-based methods generally yielding the highest predictive performance. For 30-day hospitalisation or readmission risk, Extreme Gradient Boosting (XGBoost) achieved the highest mean area under the curve (AUC) (0.69), followed by Naïve Bayes (0.68) and Deep Unified Networks (0.66). For 90-day risk, the best-performing models were Least Absolute Shrinkage and Selection Operator and Gradient Boosting, both with a mean AUC of 0.75, followed by Random Forest (0.67). When the prediction timeframe was unspecified, Categorical Boosting achieved the highest performance with a mean AUC of 0.88, followed by Generalised Linear Model Net and XGBoost (both 0.79).

Electronic health records were the primary data source across studies; however, few models included patient-reported outcomes or socioeconomic variables.

None of the studies conducted an economic evaluation to assess the cost-effectiveness of these models.

ML holds substantial potential for improving HF care. However, further efforts are needed to enhance the generalisation of models, integrate diverse data sources and evaluate the cost-effectiveness of these technologies.

Pre-eclampsia (PE) remains a major contributor to maternal morbidity and mortality globally. Early identification of risk factors and evaluation of prognostic models for severe adverse maternal outcomes are essential for improving management and reducing complications. While numerous studies have explored potential risk markers, there is still no consensus on the most reliable factors and models to use in clinical practice. This systematic review aims to consolidate research on both individual predictors and prognostic models of severe adverse maternal outcomes in PE, providing a comprehensive overview to support better clinical decision-making and patient care.

This review follows the Meta-analyses Of Observational Studies in Epidemiology (MOOSE) guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Protocol 2015 checklist. A systematic search will be performed using a detailed strategy across Medline, Embase, Cochrane, ProQuest dissertations, and grey literature from inception to 2 April 2024. Eligible studies will include those investigating clinical, laboratory-based, and sociodemographic predictors of severe adverse maternal outcomes in PE. Two reviewers will independently assess titles, abstracts, full texts, and extract data and assess study quality using the Quality In Prognostic Studies (QUIPS) tool for studies on risk predictors and the Prediction model Risk of Bias Assessment Tool (PROBAST) for prognostic models. The inclusion criteria will encompass cohort, case-control, and cross-sectional studies published in English and French involving women diagnosed with PE and reporting on the risk prediction for adverse maternal outcomes. The main outcomes of interest will include severe maternal morbidity and mortality during pregnancy, delivery, or within the postpartum period. Analyses will include both narrative synthesis and, where appropriate, meta-analysis using random-effects models. Pooled estimates will be calculated, with publication bias assessed through funnel plots and statistical tests (eg, Begg’s and Egger’s). Heterogeneity will be primarily assessed through visual inspection of forest plots, supported by statistical measures, such as the I² test, with further exploration through sensitivity, subgroup, and meta-regression analyses.

This systematic review will be based on published data and will not require ethics approval. Results will be disseminated through peer-reviewed publications and presentations at academic conferences.

CRD42024517097.