VALTIVE1 is a multi-centre, single-arm, non-interventional biomarker study for patients with advanced ovarian cancer. Plasma samples (Tie2 concentration) are collected to detect vascular control in tumours during standard treatment with chemotherapy and bevacizumab. This qualitative study embedded in VALTIVE1 aimed to assess the acceptability and feasibility of a potential VALTIVE2 trial. It explored the participants’ perceptions of the study and treatments and how they might feel if bevacizumab were discontinued based on the results from the biomarker test.

This qualitative study used semi-structured telephone interviews, which were analysed using deductive and inductive thematic analysis.

Cancer treatment sites in the UK.

Participants recruited to VALTIVE1 were invited to take part in qualitative interviews. 11 female participants took part from four clinical sites.

Participants reported that they experienced side effects attributed to bevacizumab, including stiffness, pain, fatigue, nose bleeds and muscle aches. Participants felt that combining chemotherapy and bevacizumab may have increased the severity of the side effects they experienced. Most participants felt that it was acceptable, if not preferable, to be allocated to a group in a future VALTIVE2 study where bevacizumab may be discontinued according to the results from the biomarker test. A clear preference of participants was to be informed of the biomarker test results, health status and treatment side effects.

A future trial should consider ensuring all participants have access to test results, as participants indicated a preference to know whether bevacizumab was working and to discontinue bevacizumab if it had not prevented tumour growth based on the biomarker results. Comprehensive and ongoing information and support regarding treatment side effects should be provided to all participants throughout their cancer pathways and trials.

NCT04523116.

NHS 111 Wales offers 24-hour telephone assessment, care and referrals for urgent healthcare needs. Call handlers use the newly created and implemented Call Prioritisation Streaming System (CPSS) to assess patients. CPSS is a sophisticated Computer Decision Support Software designed to enhance decision-making processes. It achieves this by integrating individual patient data with a comprehensive computerised knowledge base, employing advanced software algorithms to produce recommendations and dispositions.

While CPSS offers many advantages, its introduction marked a major shift in clinical digital processes. Because of this significant change, it was essential to ensure that the system was functioning correctly and safely after it was implemented. This process of verification and validation is known as postimplementation clinical assurance.

An adapted Delphi–Rand/UCLA appropriateness method assessed patient outcomes. In round 1, 189 random anonymised cases were reviewed by international expert clinicians from diverse clinical backgrounds, with consensus measured at >75%. Round 2 involved reviewing non-consensus cases and providing up to 250 characters of context for content analysis.

In round 1, 49 participants reviewed all 189 cases (total 9913 reviews). In round 2, 41 participants continued to review (total 1746 reviews). Consensus on outcome appropriateness was achieved in 83% (7726 reviews of 144 cases), with a range of 100–76%. Non-consensus occurred in 16.6% (1535 reviews of 45 cases), with a range of 73–18%. For cases with consensus, participants agreed with the outcome 90.5% of the time; for non-consensus cases, outcome agreement was still 60.9%.

Content analysis highlighted the complex interplay of clinician-added value and the aims of prioritisation and streaming. Three themes to enhance CPSS were identified: clinical considerations, referral pathways and system-driven safeguarding identification. No significant clinical safety concerns were found.

The evaluation of CPSS in NHS 111 Wales shows high levels of outcome appropriateness, assuring patients, service providers and stakeholders. CPSS effectively prioritises and streams patients to appropriate outcomes based on expert clinician consensus.

The Critical Appraisal Skills Programme (CASP) describes bias in research as ‘systematic errors that can occur at any stage of the research process’ and can have a ‘significant impact on the reliability and validity of the findings’ that may lead to a distortion of the conclusions.1 Understanding research bias is important for several...

Congenital heart defect (CHD) is a significant, rapidly emerging global problem in child health and a leading cause of neonatal and childhood death. Prenatal detection of CHDs with the help of ultrasound allows better perinatal management of such pregnancies, leading to reduced neonatal mortality, morbidity and developmental complications. However, there is a wide variation in reported fetal heart problem detection rates from 34% to 85%, with some low- and middle-income countries detecting as low as 9.3% of cases before birth. Research has shown that deep learning-based or more general artificial intelligence (AI) models can support the detection of fetal CHDs more rapidly than humans performing ultrasound scan. Progress in this AI-based research depends on the availability of large, well-curated and diverse data of ultrasound images and videos of normal and abnormal fetal hearts. Currently, CHD detection based on AI models is not accurate enough for practical clinical use, in part due to the lack of ultrasound data available for machine learning as CHDs are rare and heterogeneous, the retrospective nature of published studies, the lack of multicentre and multidisciplinary collaboration, and utilisation of mostly standard planes still images of the fetal heart for AI models. Our aim is to develop AI models that could support clinicians in detecting fetal CHDs in real time, particularly in nonspecialist or low-resource settings where fetal echocardiography expertise is not readily available.

We have designed the Clinical Artificial Intelligence Fetal Echocardiography (CAIFE) study as an international multicentre multidisciplinary collaboration led by a clinical and an engineering team at the University of Oxford. This study involves five multicountry hospital sites for data collection (Oxford, UK (n=1), London, UK (n=3) and Southport, Australia (n=1)). We plan to curate 14 000 retrospective ultrasound scans of fetuses with normal hearts (n=13 000) and fetuses with CHDs (n=1000), as well as 2400 prospective ultrasound cardiac scans, including the proposed research-specific CAIFE 10 s video sweeps, from fetuses with normal hearts (n=2000) and fetuses diagnosed with major CHDs (n=400). This gives a total of 16 400 retrospective and prospective ultrasound scans from the participating hospital sites. We will build, train and validate computational models capable of differentiating between normal fetal hearts and those diagnosed with CHDs and recognise specific types of CHDs. Data will be analysed using statistical metrics, namely, sensitivity, specificity and accuracy, which include calculating positive and negative predictive values for each outcome, compared with manual assessment.

We will disseminate the findings through regional, national and international conferences and through peer-reviewed journals. The study was approved by the Health Research Authority, Care Research Wales and the Research Ethics Committee (Ref: 23/EM/0023; IRAS Project ID: 317510) on 8 March 2023. All collaborating hospitals have obtained the local trust research and development approvals.

The PREgnancy Care Integrating translational Science, Everywhere Network was established to investigate specific placental disorders (pregnancy hypertension, preterm birth, fetal growth restriction and stillbirth) in sub-Saharan Africa. We created a repository of clinical and social data with associated biological samples from pregnant and non-pregnant women. Alongside this, local infrastructure and expertise in the field of maternal and child health research were enhanced.

Pregnant women were recruited in participating health facilities in The Gambia, Kenya and Mozambique at their first antenatal visit or at the time a placental disorder was diagnosed (Kenya and The Gambia only). Follow-up study visits were conducted in the third trimester, delivery and 6 weeks to 6 months postpartum. To elucidate the difference between pregnancy and non-pregnancy biology in these settings, non-pregnant nulliparous and parous women, aged 16–49 years, were recruited opportunistically primarily from family planning clinics in Kenya and Mozambique, and randomly through the Health and Demographic Surveillance System in The Gambia. Non-pregnant participants only had one study visit. Biological samples were processed rapidly and locally, stored initially in liquid nitrogen and then at –80°C, and details entered into an OpenSpecimen database linked to their social determinants and clinical research data.

A total of 6932 pregnant and 1825 non-pregnant women were recruited to the study, providing a repository of clinical and social data and a biorepository of 482 448 samples. To date, baseline descriptive analysis of the cohort has been undertaken, as well as a substudy on the prevalence of COVID-19 in the cohort.

Analysis of data and samples will include an analysis of biomarker and social and physical determinants of health and how these interact in a systemic approach to understanding the origins of common placental disorders. The data from non-pregnant women will provide control data for comparison with the data from normal and complicated pregnancies. Findings will be disseminated to local stakeholders and communities through meetings and ongoing community engagement and globally by publication and presentations at scientific meetings.