The Hillingdon Hospitals NHS Foundation Trust

Background Targeted testing for cytomegalovirus (CMV) in preterm infants <21 days has two benefits: timely congenital CMV (cCMV) diagnosis, and differentiation between cCMV and postnatal CMV (pCMV). We present an audit of targeted testing for cCMV alongside rates of clinically suspected pCMV in preterm infants.Methods We collected data on CMV testing from 2016 to 2023 in infants born <30 weeks gestation during admission to a tertiary London neonatal centre.Results Of all infants, 77% (899/1162) were tested for CMV during their admission with 74.6% tested <21 days of age. CMV infection was confirmed in 58 infants, 4 cases of cCMV (0.4%) and 54 cases of pCMV (6.0%). One infant with cCMV died, and all were symptomatic: microcephaly, thrombocytopenia, leucopenia, white matter hyperintensity on brain imaging, but none had hearing loss. Most pCMV cases (92.6%) were symptomatic, with bone-marrow suppression (92.6%), sepsis-like syndrome (50%), respiratory (31.5%) and gastrointestinal symptoms (29.6%). All infants with cCMV and 38.9% of infants with pCMV received treatment. Overall, 6% (54/899) had symptomatic CMV disease, 16.7% of infants with CMV died during their neonatal intensive care unit admission, and 42.6% were discharged on home oxygen.Conclusions CMV causes a substantial disease burden in infants born <30 weeks gestation, whereby 6% have symptomatic CMV disease. We show the feasibility and value of targeted cCMV and opportunistic pCMV testing. In the absence of universal screening, targeted birth testing for cCMV in infants <30 weeks gestational age should be considered.

OBJECTIVE:To explore the medication-related knowledge, behaviours and support needs of children and young people (YP) aged 11-16 years with long-term conditions as they prepare to transition from paediatric to adult healthcare services. METHODS:This exploratory cross-sectional study was co-designed with YP living with chronic conditions, conducted at a district general hospital in Northwest London between April and September 2023. Participants were those aged 11-16 years admitted to the paediatric ward with long-term conditions requiring regular medication. YP self-completed a questionnaire on medication-related aspects of transition, including knowledge of medication names, dosages, reordering, side effects, adherence and their ability to manage medications independently. RESULTS:Of 41 eligible YP, 30 completed the questionnaire (73% response rate). Most knew the names and dosages of their medications (24/30) and why they were prescribed (25/30). However, only half (15) knew how their medicines worked, and fewer (10) were aware of potential side effects. Just 12 participants knew how to reorder their medication. Fifteen reported missing doses, mostly due to forgetfulness. Most relied on parents or carers to manage medicines (23), with only four managing independently. YP reported healthcare professionals (17) and family (19) as their main sources of medication information. CONCLUSIONS:This study suggests that while YP often have good foundational medication knowledge, many lack the deeper understanding and practical skills required for independent self-management. Knowledge of side effects, reordering processes and shared decision-making was limited, areas which could undermine transition readiness if unaddressed. Pharmacists, working as part of the multidisciplinary team, are well placed to support YP through targeted, age-appropriate education and regular review.

INTRODUCTION:The incidence of anal carcinoma is increasing, with the current gold standard treatment being chemoradiotherapy. There is currently a wide range in the radiotherapy dose used internationally which may lead to overtreatment of early-stage disease and potential undertreatment of locally advanced disease.PLATO is an integrated umbrella trial protocol which consists of three trials focused on assessing risk-adapted use of adjuvant low-dose chemoradiotherapy in anal margin tumours (ACT3), reduced-dose chemoradiotherapy in early anal carcinoma (ACT4) and dose-escalated chemoradiotherapy in locally advanced anal carcinoma (ACT5), given with standard concurrent chemotherapy. METHODS AND ANALYSIS:The primary endpoints of PLATO are locoregional failure (LRF)-free rate for ACT3 and ACT4 and LRF-free survival for ACT5. Secondary objectives include acute and late toxicities, colostomy-free survival and patient-reported outcome measures. ACT3 will recruit 90 participants: participants with removed anal tumours with margins ≤1 mm will receive lower dose chemoradiotherapy, while participants with anal tumours with margins >1 mm will be observed. ACT4 will recruit 162 participants, randomised on a 1:2 basis to receive either standard-dose intensity modulated radiotherapy (IMRT) in combination with chemotherapy or reduced-dose IMRT in combination with chemotherapy. ACT5 will recruit 459 participants, randomised on a 1:1:1 basis to receive either standard-dose IMRT in combination with chemotherapy, or one of two increased-dose experimental arms of IMRT with synchronous integrated boost in combination with chemotherapy. ETHICS AND DISSEMINATION:This study has been approved by Yorkshire & The Humber - Bradford Leeds Research Ethics Committee (ref: 16/YH/0157, IRAS: 204585), July 2016. Results will be disseminated via national and international conferences, peer-reviewed journal articles and social media. A plain English report will be shared with the study participants, patients' organisations and media. TRIAL REGISTRATION NUMBER:ISRCTN88455282.

Background:Artificial Intelligence (AI) and Machine Learning (ML) are revolutionising orthopaedic surgery by transforming clinical problem-solving into data-driven input-output frameworks. AI allows surgeons and clinicians to analyse problems and offer innovative solutions objectively. It also enables clinicians to view these problems as an input-output continuum rather than an obstacle that needs to be solved from the basic principles upwards. These technologies would allow clinicians to bypass traditional reliance on foundational principles, instead leveraging computational models to optimise decision-making and patient outcomes. Methods:A scoping review was conducted using PubMed, Scopus, and IEEE Xplore databases (2010-2023), targeting peer-reviewed articles with keywords including Artificial Intelligence, Machine Learning, Generative AI, and Clinical Algorithms. Inclusion criteria prioritised studies demonstrating AI/ML applications in Orthopaedic diagnostics, predictive analytics, or surgical planning. Results:Advances in computational power, deep learning architectures, and interoperable data infrastructure have accelerated the development of AI/ML tools for Orthopaedic practice. Key innovations include predictive algorithms for postoperative risk stratification, generative models for patient-specific implant design, and computer vision systems for intraoperative guidance. Ubiquitous adoption of portable data-capture devices (e.g., tablets, voice-recognition systems) and clinician-facing software platforms has further streamlined data aggregation, enhancing model accuracy and clinical relevance. Conclusion:The integration of AI/ML into Orthopaedic surgery is driven by synergistic advancements in hardware and software, offering transformative potential for personalised care, surgical precision, and outcome prediction. Future adoption hinges on addressing ethical, regulatory, and interoperability challenges while fostering interdisciplinary collaboration between engineers, clinicians, and data scientists.