Epsom and St Helier University Hospitals NHS Trust

A roundtable discussion on the unmet needs in atopic dermatitis.

Pharmacotherapy combinations have been shown to improve survival and reduce hospitalisations in adults with chronic heart failure with reduced ejection fraction (HFrEF); however, their cost-effectiveness when used as first-line treatment remains uncertain. A lifetime cohort Markov model was developed from the perspective of the NHS in England to assess the cost-effectiveness of five first-line pharmacotherapy combinations: (i) angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) and beta-blocker (BB) (NICE-recommended treatment at the time of analysis); (ii) ACEI/ARB, BB and mineralocorticoid receptor antagonists (MRA); (iii) angiotensin receptor-neprilysin inhibitor (ARNI), BB and MRA; (iv) ACEI/ARB, BB, MRA and sodium-glucose cotransporter-2 inhibitor (SGLT2i); and (v) ARNI, BB, MRA and SGLT2i. Baseline hospitalisation and mortality rates were informed by real-world data, while treatment effects (HRs) were derived from a review of randomised controlled trials. Among individuals able to tolerate an ACEI, the combination of ACEI, BB, MRA and SGLT2i (cost, £12 124; quality-adjusted life years (QALYs), 5.72) was found to be the most cost-effective first-line treatment option with an incremental cost-effectiveness ratio (ICER) of £7699.Among individuals unable to tolerate an ACEI, the combination of ARNI, BB, MRA and SGLT2i (cost, £18 950; QALYs, 6.04) was found to be the most cost-effective first-line treatment option with an ICER of £15 821. The next most cost-effective first-line treatment option was the combination of ARB, BB, MRA and SGLT2i (cost, £11 842; QALYs, 5.59). These findings were primarily driven by the greater relative QALY gain of ARNI compared with ARB. This study demonstrates that a first-line quadruple pharmacotherapy combination is cost-effective compared with a stepwise approach for treating people with HFrEF, suggesting that wider adoption of early initiation of quadruple pharmacotherapy may improve health outcomes and optimise healthcare resource use.

We report a case of a male fetus born to an unrelated couple with a fetal phenotype of an omphalocele and inferior vermian hypoplasia. Prenatal trio exome sequencing identified a maternally inherited pathogenic CDKN1C variant consistent with Beckwith-Wiedemann syndrome (BWS). This finding prompted targeted testing of the proband's sibling, who was confirmed to carry the same variant. Posterior fossa abnormalities have been reported in cases with BWS, and specifically in children with the CDKN1C loss-of-function variant.

Informed consent is fundamental to ethical research, yet participant information leaflets (PILs) are often technical and difficult to understand. Although the importance of accessible study materials is widely recognised, practical guidance on how to develop them remains limited. For RaCeR 2, a randomised controlled trial evaluating different approaches to rehabilitation after shoulder rotator cuff repair, the baseline PIL was developed using Health Research Authority guidance. Early PPI consultation identified major concerns including dense formatting, overly complex language, and an unwelcoming tone, indicating the need for substantial redesign to support informed participation. A diverse PPI group (n = 5) supported the development of an accessible PIL. Contributors varied in age, gender identity, ethnicity, preferred language, employment status, disability, and experience of shoulder surgery. Engagement methods were tailored to participant needs and included online discussions, written feedback, and in-person “think-aloud” sessions. Given the depth of insight provided during initial PPI consultation, we adopted an iterative, user-centred approach drawing on co-design principles to enable contributors to directly influence the content, structure, and presentation of the PIL. Contributors identified challenges with the original materials, including confusing layout, inaccessible formatting, technical terminology, and a tone that did not feel supportive. Through iterative cycles of review and refinement guided by co-design principles, the leaflet was substantially redesigned to include clearer headings, formatting aligned with British Dyslexia Association guidance, bullet-pointed and tiered information, simplified explanations of data protection, and a more conversational tone. Accessibility testing confirmed compatibility with freely available online screen-reading software. During the subsequent regulatory review, inconsistencies between legal requirements and participant preferences highlighted tensions that may limit the accessibility of study materials. This commentary provides a pragmatic example of integrating co-design principles within PPI to create a more accessible PIL. Early engagement, flexible methods, iterative feedback, and testing with screen reading software were central to the process. Our experience also highlights the need for continued dialogue between researchers and regulators to ensure that participant-facing materials meet ethical and legal requirements while remaining understandable to all potential participants. When people take part in health research, it is important they understand what the study involves so they can make an informed choice. Researchers usually provide a written information leaflet, but many are difficult to read, particularly for people with lower literacy, disabilities, neurodivergent conditions, or for whom English is not their first language.RaCeR2 is a study comparing two approaches to helping people recover after shoulder surgery. When preparing to start the study, we drafted an initial leaflet using official guidance, but patients reported that it was hard to read, the images were confusing, it was long, and the tone felt “cold.” In response, we collaborated with a group of patients and public contributors to make the leaflet more accessible. Some joined online meetings, others provided written feedback, and one participated in in-person sessions, sharing thoughts aloud while reading the leaflet. This led to major changes: simplified language, clearer layout, bullet points, improved fonts and colours, and testing with screen readers.However, when submitting the leaflet for Health Research Authority approval, several changes were requested, including additional legal information, making the document longer, more complex, and less aligned with patient preferences.Our experience shows that involving patients and the public makes information leaflets clearer and easier to use. But we also learned that current rules can make it difficult to keep the leaflet simple and accessible. We need better ways for researchers and regulators to work together so that study information meets legal requirements and is easy for everyone to understand when deciding whether to take part.

Abstract Cryoneurolysis is a minimally invasive image-guided technique that uses controlled freezing to provide temporary pain relief by interrupting nerve conduction. This review summarizes the current understanding of its mechanisms, indications, procedural steps, and clinical outcomes. The technique relies on the Joule–Thomson effect to create localized ice formation around the target nerve, producing reversible axonal injury while preserving connective tissue structures. When applied under ultrasound or computed tomography guidance, cryoneurolysis offers safe and effective pain management for musculoskeletal, neuropathic, and cancer-related pain. Evidence from recent studies demonstrates significant pain reduction, functional improvement, and low complication rates. This article presents a practical overview of how cryoneurolysis is performed and its growing role in interventional radiology practice.

Computer vision and image-based artificial intelligence (AI) are increasingly being used in orthopaedic imaging. Applications in lower limb orthopaedic surgery present unique diagnostic, biomechanical and surgical planning challenges. This review systematically maps how computer vision has been applied to lower limb orthopaedic imaging, identifying key tasks, modalities and algorithmic trends in published studies.A scoping review was conducted following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Ovid MEDLINE and Embase were searched from January 1995 to October 2025. Studies were included if they applied an automated or semi-automated computer vision method to imaging of the hip, knee, ankle or foot. Exclusions included conference abstracts, ongoing clinical trials and studies without full text.Twenty studies met the inclusion criteria. The knee was the most frequently studied region (40%), followed by the hip (30%), ankle (15%) and foot (10%). Radiographs (40%) and CT (45%) were the dominant imaging modalities, while MRI (10%) and ultrasound (5%) were less common. Deep learning was employed in 85% of studies, primarily using convolutional architectures such as U-Net, YOLO, and ResNet. Across tasks, reported diagnostic accuracies were typically above 85%, and segmentation Dice coefficients frequently exceeded 0.85. Despite strong technical results, only 20% of studies included external validation, with no prospective clinical trials identified.Computer vision research in lower limb orthopaedics is diversifying and progressing from diagnostic classification toward quantitative measurement and intraoperative integration. Despite promising accuracy and automation potential, the evidence base is constrained by predominantly single-centre retrospective designs and scarce external validation. Future studies should prioritise reproducibility, large-scale validation and clinical practice deployment.

Computer vision has advanced in spinal imaging, enabling automated interpretation of radiographs, CT, and MRI for diagnosis, surgical planning, and postoperative assessment. The spine’s complex anatomy and high imaging volume make it a key area for algorithmic assistance. This scoping review maps current applications of computer vision in spinal orthopaedics and describes the clinical tasks, imaging modalities, and computational methods used in published studies.A systematic search of Ovid MEDLINE and Embase was performed from January 1995 to October 2025. Studies were included if they applied automated or semi-automated computer vision techniques to spinal imaging for diagnostic, morphometric, or surgical planning. Two reviewers screened and recorded data in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines.Twenty studies met the inclusion criteria. CT (45%) and MRI (35%) were the dominant imaging modalities, followed by radiographs (15%) and ultrasound (5%). Deep learning methods were employed in 90% of studies, mainly convolutional architectures such as U-Net, ResNet, and YOLOv5. Segmentation and vertebral labeling were the most common tasks (60%), achieving Dice coefficients of 0.86-0.97 and accuracies of 90-98%. Fracture detection networks (25%) reached AUCs of 0.91-0.95, while morphometric measurement algorithms (15%) produced intraclass correlations of 0.93-0.98 compared with human analysis. Despite strong technical performance, only 20% of studies included external validation, and none conducted prospective testing.Computer vision has demonstrated strong performance in spinal image segmentation and fracture detection, particularly on CT and MRI. Nevertheless, clinical translation remains limited. Future research should prioritize multi-center datasets, real-world validation, and integration into surgical clinical practice to support preoperative and intraoperative care.

Inflammation plays an essential role in the pathogenesis of cardiovascular diseases (CVDs), and despite advances in treatments, challenges still exist. Recent studies have explored the use of colchicine in reducing the risk of CVDs. Therefore, the present systematic review aimed to evaluate the impact of colchicine in terms of efficacy, safety, and therapeutic role in managing CVD patients. A comprehensive literature search was performed from different electronic databases, such as PubMed, Scopus, and the Cochrane Library, using keywords associated with the aim of the study, using Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Methodological quality assessment was performed using the Cochrane risk of bias-2.0 (RoB-2.0) and the Risk of Bias in Non-Randomized Studies-Intervention (ROBINS-I) tools for randomized controlled trials (RCTs) and non-RCTs, respectively. For the meta-analysis, RevMan 5.4 was used to construct forest plots. Finally, 19 studies were included for qualitative and quantitative analyses. Both male and female patients were included in the studies; however, studies were more skewed toward male patients reported with different types of CVDs, including acute pericarditis, coronary artery disease (CAD), heart failure, and myocardial infarction, and also reported comorbidities, like hypertension (HTN), diabetes, and dyslipidemia. Overall, a low dose of 0.5 mg/daily of colchicine was used. A high adherence rate (>85%) was observed in the studies, with few cases of discontinuation of medications. Numerous studies have reported that colchicine successfully benefits in reducing the inflammatory and other biomarkers, such as C-reactive protein (CRP), Interleukin (IL)-6, and IL-1β. The pooled estimate size for comorbidities, like diabetes, was 0.94, odds ratio (OR) (95% CI, 0.86-1.02, p = 0.13, I2 = 0%). For hypertension and dyslipidemia, it was 1, OR (95% CI, 0.93-1.07, p = 0.95, I2 = 9%) and 0.89, OR (95% CI, 0.66-1.2, p = 0.46, I2 = 0%), respectively. Meanwhile, the overall effect size was 0.97, OR (95% CI, 0.92-1.02, p = 0.29, I2 = 0%). The pooled estimate size for major adverse cardiovascular events (MACEs) and all complications was 0.68, OR (95% CI, 0.36-1.26, p = 0.22, I2 = 47%) and 1.60, OR (95% CI, 1.11-2.31, p = 0.01, I2 = 65%). The overall effect size was 1.26, OR (95% CI, 0.92-1.72, p = 0.16, I2 = 63%). Furthermore, the pooled estimate size for mortality associated with CVDs and all-cause mortality was 0.77, OR (95% CI, 0.56-1.06, p = 0.11, I2 = 17%) and 1.11, OR (95% CI, 0.72-1.71, p = 0.65, I2 = 48%), with the overall effect size of 0.89, OR (95% CI, 0.70-1.12, p = 0.32, I2 = 40%). These outcomes suggest that colchicine offers potential anti-inflammatory benefits in CVD patients, but its clinical impact on outcomes, like MACE and mortality, remains uncertain due to the non-significant difference, warranting further large-scale, high-quality multicenter studies.

The treatment of urolithiasis is changing quickly by moving away from conventional diagnostic techniques and toward more complex, data-driven strategies. A major part in this change is being played by artificial intelligence (AI) through providing the clinicians with invaluable assistance. This study examines the state of AI applications in urolithiasis today and how they affect everything from treatment planning to initial imaging. AI models are improving the accuracy of computed tomography (CT) and ultrasonography (US) in diagnoses. These techniques provide automatic stone detection throughout the diagnostic process and a precise stone burden calculation, and even assistance in differentiating difficult mimics, such as ureteral stones, from phleboliths. Additionally, sophisticated algorithms and radiomics are demonstrating great promise in determining the composition of stones preoperatively from imaging data or even digital photos. AI has also changed and improved the intervention for kidney stones, which is highlighted by models now capable of predicting the success of procedures like extracorporeal shock wave lithotripsy (ESWL) and percutaneous nephrolithotomy (PCNL), in some cases outperforming traditional scoring systems. Despite this progress, significant hurdles remain, particularly the need for large datasets and ensuring models are reliable and generalizable across different clinical settings. Successfully integrating these powerful tools into daily urological practice will require a concerted effort toward developing best-practice guidelines, robust training programs, and strong interdisciplinary collaboration. This review aims to summarize current AI applications in imaging and intervention for urolithiasis, identify limitations, and outline future research directions.