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Performance in adults of the EULAR/PRINTO/PRES (Ankara 2008) classification criteria for IgA vasculitis
Objective: To examine the performance in adults of the European Alliance of Associations for Rheumatology (EULAR)/Pediatric Rheumatology European Society (PReS)-endorsed Ankara 2008 classification criteria for IgA vasculitis (IgAV). Methods: The EULAR/PReS/Ankara 2008 classification criteria for IgAV were applied to patients enrolled in an international observational cohort which included patients with IgAV and comparators with other forms of small-vessel and medium-vessel vasculitis. After the initial assessment of the performance of the criteria, possible revisions to increase the performance were tested. The revised criteria were then assessed in an independent validation cohort within a multicentre Turkish vasculitis registry. Results: The dataset consisted of 178 IgAV cases and 1705 comparators. The Ankara 2008 criteria require skin involvement plus one of the following four criteria: abdominal pain, a biopsy showing IgA deposition, arthritis or arthralgia, or renal involvement (any haematuria and/or proteinuria). The specificity of the criteria improved when a positive test for anti-neutrophil cytoplasmic autoantibody or blood cryoglobulins was considered an exclusion criterion. The revised criteria had a sensitivity of 76.4% (95% CI 69.8% to 82.2%) and a specificity of 94.5% (95.0% CI 93.4% to 95.1%). In the validation set, the sensitivity and specificity of the revised criteria were 97.8% (95% CI 94.0% to 99.0%) and 85.0% (95.0% CI 78.0% to 90.0%), respectively. Conclusion: The revised EULAR/PReS-endorsed Ankara 2008 IgAV classification criteria perform well in adults with IgAV and are appropriate for use in clinical research.
From theory to practice: insights into intervention development of the NON-STOP app for children with Perthes' disease.
The development of interventions in healthcare often lacks a robust theoretical basis, which may contribute to suboptimal engagement and effectiveness. This paper provides insights into and practical guidance on the development of complex interventions in healthcare, using the example of a digital self-management tool for children with Perthes' disease, called the Non-Surgical Treatment of Perthes' (NON-STOP) app. We applied the Medical Research Council framework, used psychological theory, and integrated stakeholder engagement to develop the intervention. The lessons learned and considerations for the developments of other complex interventions provide practical actions for clinicians and researchers in orthopaedics.
The risk of early revision after trainee led primary unicompartmental and total knee replacement.
BACKGROUND: Orthopaedic trainees must demonstrate competence in performing major joint replacement. This study aimed to determine the impact of lead surgeon grade and level of supervision for trainee led operations on the incidence of early revision procedures following elective primary knee replacement. METHODS: Data from the National Joint Registry was obtained for all primary elective total (TKR) and partial (UKR) knee replacements performed within a single NHS University Teaching Hospital from 2007 to 2021. Multivariate logistic regression was used to determine the risk of all cause revision within 1-year of the index procedure in relation to surgeon grade and level of supervision for trainee led (TL) operations. RESULTS: 9,931 primary knee replacements (KR) were undertaken, of which 4850 (48.8 %) were UKR. Revision procedures were performed in 109 (1.1 %) patients within 1-year of their index operation. The risk of revision was not significantly different for consultant-led (CL) or TL operations (OR 0.84, CI 0.55-1.26, p = 0.4). In comparison to CL operations, no difference was observed in the risk of revision for either consultant-supervised trainee (TS, OR 0.83, CI 0.49-1.35, p = 0.7) or non-consultant supervised trainee (TU, OR 0.84, CI 0.46-1.43, p = 0.7) operations. These trends remained on sub-analysis for both UKR and TKR operations. CONCLUSIONS: No differences were observed in the incidence of revision within 1-year of primary KR between consultant led and trainee led operations. These findings suggest that training surgeons in both TKR and UKR operations is not associated with an increased risk of early adverse patient outcomes requiring revision surgery.
The definitions and prevalence of nutritional disorders in hip and knee arthroplasty: A systematic review.
PURPOSE: This systematic review aims to use European Society of Parenteral and Enteral Nutrition (ESPEN) terminology and diagnostic criteria to determine the prevalence of nutritional disorders in hip and knee arthroplasty. METHODS: A systematic review of Level 1-4 evidence was conducted as per the PRISMA statement and Cochrane handbook for Systematic Review (PROSPERO ID: CRD 42023360496). In March 2024, AMED, CENTRAL, EMBASE, MEDLINE, Scopus and Web of Science were searched. Articles were included if they defined and reported the prevalence of nutritional disorders in hip and knee arthroplasty populations. Exclusion criteria were subtrochanteric fracture, pathological fracture and <50 cases. The risk of bias in non-randomised studies of interventions and risk of bias 2 tools were used to assess bias. No pooled analyses were performed due to study heterogeneity. RESULTS: Fifty-five studies and 2,107,283 patients were included. Thirty-nine different definitions of nutritional disorder were identified. The prevalence of nutritional disorder varied depending on the chosen definition: 0.9%-71.7% in primary, 1.33%-47.5% in revision and 4.5%-60% in hip fracture arthroplasty. Thirty-four studies used albumin to diagnose malnutrition, with hypoalbuminaemia seen most frequently in hip fracture (20.3%-71.13%) and revision cohorts (2.5%-42.8%). No study reported the prevalence of sarcopenia in revision or hip fracture cohorts. CONCLUSION: All forms of nutritional disorder exist within hip and knee arthroplasty populations, particularly among revision and hip fracture patients. Included studies showed poor compliance with ESPEN recommendations and heterogeneity in the chosen definition of disorder. A prospective study using ESPEN-recommended diagnostic criteria is required to better determine the prevalence of nutritional disorders, contributing towards the understanding of the financial and patient-related costs following hip and knee arthroplasty. LEVEL OF EVIDENCE: Systematic review of articles with Level I-IV evidence.
Comparison of outcomes with elranatamab and real world treatments in the UK for triple class exposed relapsed and refractory multiple myeloma.
BACKGROUND: Patients with triple class exposed (TCE), relapsed and refractory (RR) multiple myeloma (MM) have limited treatment options and poor prognosis. Elranatamab, a bispecific BCMA-targeted antibody, is an investigational treatment for RRMM with demonstrated efficacy and safety in MagnetisMM-3, a single-arm, multi-centre, phase-2 study. This study aimed to characterise outcomes for real world TCE RRMM patients and to estimate the treatment effect of elranatamab compared to treatments available in routine clinical care for TCE RRMM in the NHS. METHODS: A retrospective, observational, external control arm (ECA) study combining participants from a single arm, multi-centre phase 2 study, MagnetisMM-3, receiving elranatamab to compare patient characteristics and median survival using a comparator cohort of TCE RRMM patients treated with real world regimens in five UK centres between 2015 and 2023. Both naive and adjusted treatment effect estimates for progression free survival (PFS) and overall survival (OS) were obtained using inverse probability of treatment weighted (IPTW) Cox proportional hazards models and differences in restricted mean survival time (dRMST). Quantitative bias analysis was used to assess the robustness of effect estimates to unmeasured confounding. RESULTS: From a total of 5,535 patients identified with a diagnosis of MM, 81 were identified as eligible for inclusion in the ECA. A total of 13 different regimens were recorded as being initiated from the real world RRMM at index date, the most common regimen was pomalidomide + dexamethasone (48.15%). Clinical outcomes in the ECA were poor (median PFS 3.71 months [95% confidence interval (CI) 2.73-4.73], median OS 11.00 months [8.02-18.10]). In unadjusted analyses the elranatamab cohort had significant improvements in PFS (dRMST 6.95 months [4.08-9.61]) and OS (Hazard Ratio (HR) 0.66 [0.45-0.96]). Adjusted analyses showed similar effects for PFS (dRMST 6.45 [3.05-9.45]) but were equivocal for OS (HR 0.75 [0.46-1.26]). CONCLUSION: This study provides recent real world evidence of poor outcomes in TCE RRMM in the UK. PFS was longer among patients who received elranatamab compared with treatments for TCE RRMM in routine UK clinical practice.
Multiple myeloma
Multiple myeloma (MM) is characterized by the proliferation of a clone of plasma cells that produce a monoclonal protein. The plasma cell proliferation results in extensive skeletal destruction, with osteolytic lesions, hypercalcaemia, anaemia and/or soft tissue plasmacytomas. In addition, the excessive production of nephrotoxic monoclonal proteins can result in renal failure and an increased risk of developing potentially life-threatening infections due to the lack of functional immunoglobulins. Pathobiology, clinical presentation, complications of the disease, and their management, are discussed in this chapter.
A computational medicine framework integrating multi-omics, systems biology, and artificial neural networks for Alzheimer’s disease therapeutic discovery
The translation of genetic findings from genome-wide association studies into actionable therapeutics persists as a critical challenge in Alzheimer’s disease (AD) research. Here, we present PI4AD, a computational medicine framework that integrates multi-omics data, systems biology, and artificial neural networks for therapeutic discovery. This framework leverages multi-omic and network evidence to deliver three core functionalities: clinical target prioritisation; self-organising prioritisation map construction, distinguishing AD-specific targets from those linked to neuropsychiatric disorders; and pathway crosstalk-informed therapeutic discovery. PI4AD successfully recovers clinically validated targets like APP and ESR1, confirming its prioritisation efficacy. Its artificial neural network component identifies disease-specific molecular signatures, while pathway crosstalk analysis reveals critical nodal genes (e.g., HRAS and MAPK1), drug repurposing candidates, and clinically relevant network modules. By validating targets, elucidating disease-specific therapeutic potentials, and exploring crosstalk mechanisms, PI4AD bridges genetic insights with pathway-level biology, establishing a systems genetics foundation for rational therapeutic development. Importantly, its emphasis on Ras-centred pathways—implicated in synaptic dysfunction and neuroinflammation—provides a strategy to disrupt AD progression, complementing conventional amyloid/tau-focused paradigms, with the future potential to redefine treatment strategies in conjunction with mRNA therapeutics and thereby advance translational medicine in neurodegeneration. The PI4AD portal is accessible at http://www.genetictargets.com/PI4AD.
Progressive resistance and flexibility exercises versus usual care advice for improving pain and function after distal radius fracture in adults aged 50 years or over : protocol for the WISE randomized superiority trial.
AIMS: Distal radius fractures are very common injuries; the majority affect females aged 50 years or over. Most patients experience pain and stiffness in their wrist and upper limb weakness, making activities of daily living difficult. The aim of the WISE (Wrist Injury Strengthening Exercise) trial is to assess the effectiveness of a flexibility and resistance exercise programme for the upper limb compared with usual care advice after distal radius fracture. METHODS: This is a multicentre, parallel-group, superiority, individually randomized controlled trial. We aim to recruit 588 participants aged 50 years and older with a distal radius fracture treated surgically or non-surgically from at least 15 UK NHS hospitals. Participants will be randomized 1:1 using a web-based service to usual care advice plus a therapist-supervised exercise programme (three one-to-one therapy sessions of tailored advice and prescribed home exercise over 12 weeks) or usual care advice only. The primary outcome is participant-reported wrist-related pain and function six months after randomization, measured by the Patient-Rated Wrist Evaluation. Secondary outcomes at three and six months measure health-related quality of life, pain, physical function, self-efficacy, exercise adherence, grip strength, complications, and resource use. CONCLUSION: This study will assess whether a therapist-supervised exercise programme is more clinically effective than usual care advice for people aged 50 years and older after distal radius fracture. At the time of submission, the trial is currently completing recruitment; follow-up will be completed in 2025 (ISRCTN registry identifier: ISRCTN78953418).
Transforming label-efficient decoding of healthcare wearables with self-supervised learning and "embedded" medical domain expertise.
Healthcare wearables are transforming health monitoring, generating vast and complex data in everyday free-living environments. While supervised deep learning has enabled tremendous advances in interpreting such data, it remains heavily dependent on large labeled datasets, which are often difficult and expensive to obtain in clinical practice. Self-supervised contrastive learning (SSCL) provides a promising alternative by learning from unlabeled data, but conventional SSCL frequently overlooks important physiological similarities by treating all non-identical instances as unrelated, which can result in suboptimal representations. In this study, we revisit the enduring value of domain knowledge "embedded" in traditional domain feature engineering pipelines and demonstrate how it can be used to guide SSCL. We introduce a framework that integrates clinically meaningful features-such as heart rate variability from electrocardiograms (ECGs)-into the contrastive learning process. These features guide the formation of more relevant positive pairs through nearest-neighbor matching and promote global structure through clustering-based prototype representations. Evaluated across diverse wearable technologies, our method achieves comparable performance with only 10% labeled data, compared to conventional SSCL approaches with full annotations for fine-tuning. This work highlights the indispensable and sustainable role of domain expertise in advancing machine learning for real-world healthcare, especially for healthcare wearables.