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Developing an adaptive platform trial for evaluation of medical treatments for Crohn's disease
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Impact of NICE Guideline NG241 'Ovarian Cancer: identifying and managing familial and genetic risk' on a regional NHS family history and clinical genetics service
No full textBACKGROUND: NICE Guideline NG241: identifying and managing familial and genetic risk of ovarian cancer (OC) was published by the National Institute for Health and Care Excellence (NICE) in March 2024. NG241 advises germline genetic testing of genes predisposing to OC in unaffected individuals with an OC family history at different mutation likelihood thresholds depending on age and sex, ranging from 2% to 10% likelihood of finding a germline pathogenic variant (GPV). Prior to implementation of NG241, updates to the NHS England National Genomic Test Directory would be required. Clinical genetics services have to consider equity of access to assessment and testing across all familial cancer types, best use of their limited resources and other factors such as complexity of delivery of clinical pathways. METHODS: We analysed data from 8011 patients who provided digital family histories to the South West Thames Centre for Genomics between October 2019 and June 2024. RESULTS: We estimate 527/782 (68%) females and 28/77 (36%) males would meet test criteria for NICE NG241. We estimate we would reject 2919/5485 (53%) females and 135/1208 (11%) males with the same likelihood of carrying a GPV, but with a breast cancer rather than OC family history. Testing the familial OC cohort at a universal 5% threshold in OC families would detect ~11 carriers for 229 tests compared with ~8 carriers for 278 tests following NG241 criteria. CONCLUSION: Our data highlight additional factors needing to be considered before the NICE Guideline NG241 can be implemented by regional genetics services.This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.RDUH staff can access the full-text of this article by clicking on the 'Additional Link' above and logging in with NHS OpenAthens if prompted
Type 1 diabetes presenting in adults: Trends, diagnostic challenges and unique features
No full textType 1 diabetes (T1D) has been historically regarded as a childhood-onset disease; however, recent epidemiological data indicate that adult-onset T1D accounts for a substantial proportion of cases worldwide. There is evidence that adult-onset T1D is associated with the classic T1D triad of elevated genetic risk, the presence of islet-specific autoantibodies and progression to severe insulin deficiency. In this article, we review our understanding of the commonalities and differences between childhood and adult-onset T1D, and we highlight significant knowledge gaps in our understanding of the diagnosis, incidence, trajectory and treatment of adult-onset T1D. Compared to children, adults presenting with T1D exhibit differences in genetic risk, immunologic profiles and metabolic outcomes, including differences in the type and number of autoantibodies present, genetic associations and total genetic burden, rates of C-peptide decline, the persistence of C-peptide in long-duration disease and glycaemic control. In addition, obesity and metabolic syndrome are increasingly common in adults, which not only blurs the clinical distinction of adult-onset T1D from type 2 diabetes (T2D) but also likely contributes to differences in metabolic outcomes and rates of progression. Because T2D is so prevalent in the adult population, adult-onset T1D is misclassified as T2D in at least one in three cases, leading to delays in appropriate treatment. Current diagnostic tools, including autoantibody testing and C-peptide measurement, are underutilised or lack specificity in distinguishing adult-onset T1D from atypical T2D. Additionally, the impact of different responses to disease-modifying therapy between adults and children is unclear. Addressing these knowledge gaps requires expanded epidemiological studies, diverse patient registries and refined classification criteria to improve early detection and treatment strategies. A deeper understanding of adult-onset T1D will be critical to reduce the burden of misdiagnosis, lead to earlier diagnosis and treatment and optimise population-based screening approaches in this under-recognised population. PLAIN LANGUAGE SUMMARY: Type 1 diabetes (T1D) is an autoimmune disease that causes metabolic and nutritional complications due to the destruction of insulin-producing pancreatic β cells. T1D was formerly known as juvenile diabetes" because it was assumed that most cases occurred in childhood; however, recent epidemiological data show that nearly half of all T1D cases are diagnosed in adulthood. Despite the high prevalence of adult-onset T1D, there are challenges with correctly diagnosing T1D in adulthood, and significant knowledge gaps remain regarding the incidence, trajectory, and treatment of adult-onset T1D. In this article, we summarize the current understanding of commonalities and differences between childhood and adult-onset T1D. Particularly, we highlight age-related differences in genetic risk, immunologic profiles, and metabolic outcomes and complications. Finally, we highlight key gaps in our understanding of adult-onset T1D that need to be addressed to reduce the burden of misdiagnosis and allow for better screening and treatment of T1D in adulthood."CC BY 4.0 Internationa
Anticoagulation Timing in Acute Stroke With Atrial Fibrillation According to Chronic Kidney Disease: The OPTIMAS Trial
No full textINTRODUCTION: Patients with chronic kidney disease (CKD) are at increased risk of ischemic stroke (IS) and intracerebral hemorrhage, so the safety and efficacy of early direct oral anticoagulant (DOAC) initiation in those with CKD are of interest. METHODS: OPTIMAS was a multicenter, randomized, parallel-group, open-label trial with blinded outcome assessment, recruiting patients with IS and atrial fibrillation from 100 UK hospitals between 2019 and 2024. Participants were randomized 1:1, stratified by stroke severity, to early (within 4 days of onset) or delayed (at days 7-14) DOAC initiation. CKD was defined as a past medical history of known CKD, collected according to trial protocol as part of the case report form. For this prespecified subgroup analysis, the trial cohorts were classified according to the presence or absence of CKD. Whether CKD modified the treatment effect of early DOAC initiation was determined by fitting mixed effects logistic regression models with interaction terms between CKD and treatment group. The primary outcome was a composite outcome of recurrent IS, symptomatic intracranial hemorrhage, and systemic arterial embolism. Key secondary outcomes included the individual components of the primary outcome and all-cause mortality. RESULTS: We included 3601 patients (mean age, 78±10 years; 45% female), 543 with CKD. There were 116 primary outcome events: 97 (3.2%) in the normal kidney function group and 19 (3.5%) in the CKD group. There was no difference between early and delayed DOAC initiation for the primary outcome in either the normal kidney function group (odds ratio, 1.01 [95% CI, 0.67-1.51]) or the CKD group (odds ratio, 0.90 [95% CI, 0.36-2.25]; P(interaction)=0.822). Similarly, for the secondary outcomes, we detected no modification of the treatment effect according to CKD (P(interaction) values of 0.637, 0.386, and 0.107 for IS, symptomatic intracranial hemorrhage, and all-cause mortality, respectively). CONCLUSIONS: Our findings suggest that CKD does not modify the effects of early versus delayed DOAC initiation after acute IS. Based on these results, early DOAC initiation should not be withheld in patients with CKD. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03759938.CC BY 4.0 (Creative Commons Attribution
OCT-Derived Biomarkers in Optic Disc Pit Maculopathy Are Associated with Age, Visual Function, and Natural History
No full textINTRODUCTION: Optic disc pit maculopathy (ODP-M) describes the variable intra- (IRF) and/or subretinal fluid (SRF) accumulation complicating a congenital optic disc anomaly that is primarily observed in young adults. This study aimed to explore the morphological variance in ODP-M, in order to measure associations between demographic and functional characteristics and investigate the natural course of the disease. METHODS: A single-centre, retrospective, observational study was performed. Subjects with ODP-M were identified through electronic notes review. Demographic characteristics, visual acuity, and anatomical features were analysed with respect to a predefined OCT-based sub-categorisation: type 1a: IRF only; type 1b: IRF + outer lamellar hole (OLH) +/- SRF; type 2: SRF +/- IRF (no OLH). RESULTS: Fifty eyes (50 subjects) were sub-categorised according to fluid distribution into type 1a (34%), type 1b (28%), and type 2 ODP-M (38%). Those with type 2 were found to be significantly younger than those with types 1a/b ODP-M (p < 0.001) and accounted for 93% of cases occurring in subjects ≤20 years old. The presence of OLH (i.e., type 1b) was noted to be independently associated with worse final VA (p = 0.013) and higher likelihood of proceeding to surgery (p = 0.002). CONCLUSION: There appears to be an age-related variation in ODP-M morphology, indicating the possibility of separate pathoanatomical processes, with distinct clinical courses and potentially different optimal management strategies. Sub-categorisation of ODP-M according to the reported structural features may help guide management of this rare condition.All rights reserve
Angiogenic and immune predictors of neoadjuvant axitinib response in renal cell carcinoma with venous tumour thrombus
No full textVenous tumour thrombus (VTT), where the primary tumour invades the renal vein and inferior vena cava, affects 10-15% of renal cell carcinoma (RCC) patients. Curative surgery for VTT is high-risk, but neoadjuvant therapy may improve outcomes. The NAXIVA trial demonstrated a 35% VTT response rate after 8 weeks of neoadjuvant axitinib, a VEGFR-directed therapy. However, understanding non-response is critical for better treatment. Here we show that response to axitinib in this setting is characterised by a distinct and predictable set of features. We conduct a multiparametric investigation of samples collected during NAXIVA using digital pathology, flow cytometry, plasma cytokine profiling and RNA sequencing. Responders have higher baseline microvessel density and increased induction of VEGF-A and PlGF during treatment. A multi-modal machine learning model integrating features predict response with an AUC of 0.868, improving to 0.945 when using features from week 3. Key predictive features include plasma CCL17 and IL-12. These findings may guide future treatment strategies for VTT, improving the clinical management of this challenging scenario.CC BY 4.0 (Creative Commons Attribution
AI versus the spinal surgeons in the management of controversial spinal surgery scenarios
No full textAIMS: The use of artificial intelligence (AI) in spinal surgery is expanding, yet its ability to match the diagnostic and treatment planning accuracy of human surgeons remains unclear. This study aims to compare the performance of AI models-ChatGPT-3.5, ChatGPT-4, and Google Bard-with that of experienced spinal surgeons in controversial spinal scenarios. METHODS: A questionnaire comprising 54 questions was presented to ten spinal surgeons on two occasions, four weeks apart, to assess consistency. The same questionnaire was also presented to ChatGPT-3.5, ChatGPT-4, and Google Bard, each generating five responses per question. Responses were analyzed for consistency and agreement with human surgeons using Kappa values. Thematic analysis of AI responses identified common themes and evaluated the depth and accuracy of AI recommendations. RESULTS: Test-retest reliability among surgeons showed Kappa values from 0.535 to 1.00, indicating moderate to perfect reliability. Inter-rater agreement between surgeons and AI models was generally low, with nonsignificant p-values. Fair agreements were observed between surgeons' second occasion responses and ChatGPT-3.5 (Kappa = 0.24) and ChatGPT-4 (Kappa = 0.27). AI responses were detailed and structured, while surgeons provided more concise answers. CONCLUSIONS: AI large language models are not yet suitable for complex spinal surgery decisions but hold potential for preliminary information gathering and emergency triage. Legal, ethical, and accuracy issues must be addressed before AI can be reliably integrated into clinical practice.All rights reserve
GRASS-UK: The Global Register of Alopecia areata disease Severity and treatment Safety- United Kingdom: importance of real-world data in alopecia areata
No full textRDUH staff can access the full-text of this article by clicking on the 'Additional Link' above and logging in with NHS OpenAthens if prompted
Knowledge translation in Anglo-American paramedicine: a scoping review
No full textOBJECTIVE: To map what is currently known about knowledge translation (KT) in Anglo-American paramedicine. The review focuses on reported barriers and facilitators to the implementation of new knowledge, and the use of models, theories and frameworks to guide implementation practice. DESIGN: Scoping review reported as per both the Joanna Briggs Institute and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews reporting guidelines. DATA SOURCES: CINAHL (EBSCO Host) and Medline (OVID interface) were searched from January 2000 to May 2023. Reference lists of all included papers were reviewed, and several key professional journals were hand-searched. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Primary sources that focused on KT models, theories or frameworks, or barriers and facilitators to KT implementation, involving paramedics or Emergency Medical Technicians (Paramedic in America) working in an out-of-hospital, Anglo-American Emergency Medical Service (EMS) system were eligible for inclusion. DATA EXTRACTION AND SYNTHESIS: One reviewer used a data extraction template developed for this review and 10% of the papers were checked by the second author. Findings were summarised in tables and synthesised both quantitatively and qualitatively. RESULTS: The search yielded 1268 primary sources, of which 48 were included in the review. Thirty-two papers examining KT interventions and 16 papers examining the barriers and facilitators to KT were found. Only one randomised controlled trial was found, and only one paper made explicit use of any KT framework. Overall, eight themes describing barriers and facilitators to KT arose from the qualitative literature, with clinicians' perception of the evidence being the dominant theme. All 32 papers describing KT interventions included some form of educational intervention. CONCLUSIONS: Overall, there is little depth and breadth in the literature, with many papers focusing on trauma and airway management. There are large gaps in the evidence surrounding the use of KT theories and frameworks in Anglo-American EMS. Further research is needed to identify appropriate KT models and frameworks that are contextualised to EMS to ensure that paramedic-led research finds its way to the clinicians needing to use it.This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial.This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial
Machine learning to predict stroke risk from routine hospital data: A systematic review
No full textPURPOSE: Stroke remains a leading cause of morbidity and mortality. Despite this, current risk stratification tools such as CHA(2)DS(2)-VASc and QRISK3 are of limited accuracy, particularly in those without a diagnosis of atrial-fibrillation. Hence, there is a need for more accurate stroke risk prediction models. Machine-learning (ML) may provide a solution to this by leveraging existing routine hospital databases to build accurate stroke risk prediction models and identify novel risk factors for stroke. AIMS: In this systematic review we appraise current research using ML to predict stroke risk from routine hospital data. Based on these findings we then highlight common methodological limitations and recommendations for future research. METHODS: In this review we identify 49 original research (38 in the general population and 11 in AF specific populations) articles from the PUBMED database from January-2013 to December-2024 using ML and routine hospital data to predict the risk of stroke. RESULTS: ML models were able to accurately predict stroke risk in both AF specific and general populations, with AUCs ranging from 0.64 to 0.99. Where tested, ML also consistently outperformed traditional risk stratification tool, such as CHA(2)DS(2)-VASc. ML also appeared useful in identifying several novel risk factors from electrocardiogram, laboratory test and echocardiography data. However, the quality of datasets were often limited, there was a high suspicion of overfitting and models often lacked calibration, external validation and explainability analysis. CONCLUSION: Whilst ML has shown great potential in stroke prediction and identifying novel risk factors for stroke, improvements in study methodology is required prior to integration of ML into routine healthcare. Future research should adhere to the EQUATOR guidance on prediction models and encourage interdisciplinary collaboration between computer scientists and clinicians. Further prospective RCTs are also required to validate models in the clinical setting and the identify barriers of integrating ML into routine healthcare.This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Not hel