The Royal Berkshire Hospital (RBH) is a National Health Service hospital in the town of Reading in the English county of Berkshire. It provides acute hospital services to the residents of the western and central portions of Berkshire, and is managed by the Royal Berkshire NHS Foundation Trust.The hospital provides approximately 813 inpatient beds (627 acute, 66 paediatrics and 120 maternity), together with 204-day beds and spaces. In doing so, it employs over 5,000 staff and has an annual budget of £228 million.
Whereas most people with diabetes mellitus have type 1 (T1DM) or type 2 (T2DM) diabetes, there are a number of other inherited forms of diabetes and insulin resistance syndromes, which represent up to about 5% of all cases of diabetes. Monogenic forms of pancreatic β-cell dysfunction include maturity-onset diabetes of the young (MODY) and neonatal diabetes mellitus (NDM), with MODY being the most common form of inherited diabetes. The long-term response to sulfonylurea drugs in MODY caused by HNF1A and HNF4A mutations and NDM caused by KCNJ11 and ABCC8 mutations is an excellent example of precision medicine. Mitochondrial diabetes is maternally inherited and usually associated with sensorineural deafness and other neurological features. Monogenic severe insulin resistance can be divided into adipose tissue defects (lipodystrophies, characterized by abnormal fat distribution) and disorders of insulin signalling. The molecular diagnosis of inherited diabetes has important implications for patients, allowing personalized management and screening of their relatives. Misclassification of monogenic diabetes or severe insulin resistance as T1DM and T2DM is common, and new tools for prioritizing suspected cases for genetic testing are needed.
e19507 Background: Elranatamab is a B-cell maturation antigen–CD3 bispecific antibody approved for the treatment of adult patients with relapsed or refractory multiple myeloma (RRMM) in several countries. The efficacy and safety of elranatamab were demonstrated in previous clinical trials, and real-world data for elranatamab are currently being accumulated. This interim analysis provides an early look into the results from a real-world primary data collection study of elranatamab in patients with RRMM. Methods: MagnetisMM-16 (EUPAS106401) is a prospective, international, multicenter, non-interventional post-authorization safety study evaluating the effectiveness and safety of elranatamab in patients aged ≥18 years with RRMM treated in real-world settings. Patients are being recruited from hematology/oncology clinics, primary care settings, and academic centers in the United Kingdom, Germany, Spain, and Italy. Investigators were asked in the study protocol to follow recommendations in the local health authority–approved product label, including infection precautions and antimicrobial prophylaxis. This interim analysis is based on a September 15, 2025 data cut and describes preliminary results. Results: A total of 52 patients were included in the analysis. The median age of patients was 70.0 years (range, 48.0-87.0), 59.6% were male, and 88.5% were white. The patients were heavily pre-treated with more than half of the patients (61.5%) receiving ≥4 prior lines of therapy, 94.2% were double-class exposed to prior anti-CD38 and proteasome inhibitor, and 84.6% were triple-class exposed to prior anti-CD38, proteasome inhibitor, and immunomodulatory therapy. International Staging System disease stage at baseline was reported for 32 patients, 46.9% (n=15) had stage III and 34.4% (n=11) had stage II disease. The most common (≥10%) comorbidities were hypertension (32.6%), atrial fibrillation (16.3%), chronic kidney disease (11.6%), and type 2 diabetes mellitus (14.0%). Conclusions: Real-world effectiveness and safety data for elranatamab will be presented for this heavily pre-treated RRMM patient population at the congress. Clinical trial information: EUPAS106401.
INTRODUCTION:Nosocomial infections (NIs) in cirrhosis are associated with high mortality but could be preventable. Logistic regression (LR) models have failed to identify high-risk patients. We aimed to develop machine learning (ML) models to predict NI. METHODS:The CLEARED consortium consists of prospectively enrolled cirrhosis inpatients from >120 centers. Using day-of-admission clinical data, 3 ML approaches (random forest [RF], extreme gradient boosting, and neural networks [NNs]) were used to predict NI. Data were split 80:20 for training and testing stratified by the outcome. Models were compared using area under the receiver operating characteristic curve (AUC). RESULTS:In total, 8,263 patients (55.90 ± 13.34 years; 64.1% men) from 127 centers in 37 countries were included. NI developed in 869 (10.5%), a median of 6 (4-11) days of postadmission. Major NIs were respiratory (29.6%) and urinary tract infection (15.7%), and spontaneous bacterial peritonitis (13.5%). NIs occurred more frequently in patients from low/low-middle income countries and those with severe liver disease, alcohol etiology, and admission infections. NIs were associated with inpatient mortality (31.9% vs 8.1%, P < 0.001) and liver transplantation (4.8% vs 1.9%, P < 0.001). Although the RF model (AUC 0.69) showed good calibration (Brier score 0.09), outperforming extreme gradient boosting, neural network, and LR models (AUC 0.66 for all; LR comparison P = 0.043), no model achieved AUC ≥0.80 for clinical utility. At 10% predicted probability threshold, the RF model demonstrated only 75.4% sensitivity, 52.9% specificity, and 15.9% positive predictive value (PPV). DISCUSSION:NIs cannot be accurately predicted from day-of-admission data using ML models, even in a large, prospective, global cirrhosis cohort. Every hospitalized patient with cirrhosis should receive protocolized infection control measures.
BACKGROUND:Colon capsule endoscopy (CCE) is an increasingly used noninvasive alternative to colonoscopy for colonic investigation. However, its widespread adoption is constrained by suboptimal completion rates (CR), inadequate bowel preparation, and a high follow-up endoscopy rate (FER), collectively undermining cost-effectiveness and service efficiency. Evidence on patient-level predictors of these outcomes remains fragmented. METHODS:A systematic review and meta-analysis were conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE, Embase, CENTRAL, and PubMed Central were searched. Patient-level factors associated with completion rate, bowel preparation adequacy, successful CCE, and FER were extracted. Random-effects meta-analyses were performed to pool unadjusted and adjusted odds ratios. RESULTS:Ten studies comprising 4374 participants met inclusion criteria. The pooled completion rate was 73% (95% CI 68%-78%), and pooled bowel preparation adequacy was 72% (95% CI 60%-81%). Chronic opioid use was consistently associated with reduced performance, demonstrating lower completion rates (unadjusted OR 0.54, 95% CI 0.40-0.73; adjusted OR 0.55, 95% CI 0.28-1.09) and poorer bowel preparation adequacy (adjusted OR 0.49, 95% CI 0.26-0.96). Diabetes was independently associated with inadequate bowel preparation (adjusted OR 0.40, 95% CI 0.36-0.45). Increasing age showed borderline statistical significance but minimal clinical impact. The pooled follow-up endoscopy rate was 61% (95% CI 56%-67%). CONCLUSION:CCE performance is strongly influenced by patient-level factors, particularly chronic opioid use and diabetes. Targeted patient selection is the most immediately actionable strategy to improve efficiency, whereas individual participant data meta-analyses are needed to enable robust risk stratification of CCE.
