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The experimental setup of the new measurement of 239Pu fission and capture cross-section in the n_TOF time-of-flight facility at CERN is presented. The measurement aims to address the needs and demands of nuclear data users. The experiment incorporates an innovative fast Fission Fragment Detector and the n_TOF Total Absorption Calorimeter, enabling the implementation of the fission tagging technique. Preliminary results exhibit the robust performance of the detector systems, along with the high quality of the new 239Pu samples. These samples were exclusively produced for this measurement by the European Commission’s Joint Research Centre in Geel.

Neutron cross section measurements are often made relative to a neutron cross section standard. Thus, the accuracy of the neutron standards determines the best possible accuracy of the neutron measurements. The 235U(n,f) cross section is widely used as reference, while it is considered a standard at thermal point and between 0.15 to 200 MeV. For this reason, additional cross section data for the 235U(n,f) reaction are useful in order to improve the accuracy and to extend the energy range of the standard. In this work, preliminary results of the measurement of the 235U(n,f) cross-section relative to the standard 10B(n,a) reaction are presented. The high accuracy measurement was performed at the experimental area EAR-1 of the n_TOF facility at CERN, aiming at covering the energy range from the thermal region up to approximately 100 keV. The samples were produced at JRC-Geel in Belgium, while the experimental setup was based on Micromegas detectors.

In order to make a conventional building more efficient or smarter, systems feedbacks are essential. Such feedbacks can include real-time or logged data from various systems, such as temperature, humidity, lighting and CO2 levels. This is only possible by the use of a network of sensors which report to the building management system. Conventional sensors are limited due to wiring and infrastructure requirements. Wireless Sensor Networks (WSN) however, eliminates the wiring limitations but still in certain cases require periodical battery changes and maintenance. A suitable solution for WSN limitations is to use different types of ambient energy harvesters to power battery-less sensors or alternatively to charge existing batteries so as to reduce their changing requirements. Such systems are already in place using various energy harvesting techniques. Thermoelectric Generators (TEG) are one of them where the temperature gradient is used to generate electricity which is conditioned and used for WSN powering applications. Researchers in this field often face difficulty in estimating the TEG output at the low-temperature difference as manufacturers’ datasheets and performance data are not following the same standards and in most cases cover the high-temperature difference (more than 200C°). This is sufficient for industrial applications but not for WSN systems in the built environment where the temperature difference is much smaller (1-20C° is covered in this study). This paper presents a TEG experimental test setup using a temperature controlled hotplate in order to provide accurate TEG performance data at the low-temperature difference range.

This contribution is focused on the STELLA project (STELlar LAboratory), which aims at the measurement of fusion cross sections between light heavy ions like 12C+12C, 12C+16O or 16O+16O at deep subbarrier energies. The gamma-particle coincidence technique is used in order to reduce background contributions that become dominant for measurements in the nanobarn regime. The experimental setup composed of an ultra high vacuum reaction chamber, a set of 3 silicon strip detectors, up to 36 LaBr3(Ce) scintillators from the UK FATIMA collaboration, and a fast rotating target system will be described. The 12C+12C fusion reaction has been studied from Elab = 11 to 5.6 MeV using STELLA at the Andromède facility in Orsay, France. Preliminary commissioning results are presented in this article.

Abstract Background To examine urban Indigenous Australians' knowledge of the current Physical Activity Guidelines (PAG) and identify their preferred sources of assistance or advice regarding physical activity. Method Self-completed questionnaire data were collected from 194 participants; the questionnaires sought information on standard demographics including an assessment of their perceived physical activity level relative to peers. Outcome measures were agreement with five statements from the current PAG and indicators of preferred sources of assistance or advice regarding physical activity. Results Most participants demonstrated excellent knowledge of the current PAG, with 92% to 88% of participants agreeing with the statements. Significantly more older participants (> 44 years) identified that 'blocks of 10 minutes of activity are OK' compared to younger participants (aged 18–44 years: 60%; X2 = 6.23; p = .04). Significantly more higher educated participants agreed (96%) that 'brisk walking for half an hour most days was good for health' compared to the less educated participants (85%; X2 = 8.08; p = .02). The most preferred source of physical activity advice identified by men was the GP/health professional (62% vs. 53%; men and women respectively, NS), while for women it was a group to be active with (60% vs. 42%; women and men respectively; X2 = 6.09; p = .01). Conclusion Urban Indigenous Australians have similar levels of knowledge regarding the PAG to non-Indigenous Australians. However, the option of accumulating 10-minute activity bouts needs to be better communicated to younger Indigenous people. Most participants expressed a preference for advice about physical activity to be delivered via health professionals, and groups to be active with. Indigenous and age-specific resources that promote the unique aspects of the current PAG (e.g., that vigorous exercise is not essential for health and blocks of 10 minutes of activity are OK) should be developed and disseminated.

Background and Aims: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation., Methods: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal-external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis., Results: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor., Conclusions: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Objective: Up to one in five patients with axial spondyloarthritis (AxSpA) or psoriatic arthritis (PsA) newly initiated on opioids transition to long-term use within the first year. This study aimed to investigate individual factors associated with long-term opioid use among opioid new users with AxSpA/PsA., Methods: Adult patients with AxSpA/PsA and without prior cancer who initiated opioids between 2006-2021 were included from Clinical Practice Research Datalink Gold, a national UK primary care database. Long-term opioid use was defined as having ≥3 opioid prescriptions issued within 90 days, or ≥ 90 days of opioid supply, in the first year of follow-up. Individual factors assessed included sociodemographic, lifestyle factors, medication use and comorbidities. A mixed-effects logistic regression model with patient-level random intercept was used to examine the association of individual characteristics with the odds of long-term opioid use., Results: In total 10 300 opioid initiations were identified from 8,212 patients (3037 AxSpA; 5175 PsA). The following factors were associated with long-term opioid use: being a current smoker (OR : 1.62; 95%CI : 1.38,1.90), substance use disorder (OR : 2.34, 95%CI : 1.05,5.21), history of suicide/self-harm (OR : 1.84; 95%CI : 1.13,2.99), co-existing fibromyalgia (OR : 1.62; 95%CI : 1.11,2.37), higher Charlson Comorbidity Index (OR : 3.61; 95%CI : 1.69,7.71 for high scores), high MME/day at initiation (OR : 1.03; 95%CI : 1.02,1.03) and gabapentinoid (OR : 2.35; 95%CI : 1.75,3.16) and antidepressant use (OR : 1.69; 95%CI : 1.45,1.98)., Conclusions: In AxSpA/PsA patients requiring pain relief, awareness of lifestyle, sociodemographic and prescribing characteristics associated with higher risk of long-term opioid use can prompt timely interventions such as structured medication reviews and smoking cessation to promote safer prescribing and better patient outcomes., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Background: Heart failure (HF) with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, and compare phenogroup characteristics and outcomes, from electronic health record data., Methods: 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction ≥ 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups., Results: Three phenogroups were identified: (1) Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; (2) More frail patients, with higher rates of lung disease and atrial fibrillation; (3) Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p < 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF., Conclusions: Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles., (© 2024. The Author(s).)

Objectives: Fibromyalgia is frequently treated with opioids due to limited therapeutic options. Long-term opioid use is associated with several adverse outcomes. Identifying factors associated with long-term opioid use is the first step in developing targeted interventions. The aim of this study was to evaluate risk factors in fibromyalgia patients newly initiated on opioids using machine learning., Methods: A retrospective cohort study was conducted using a nationally representative primary care dataset from the UK, from the Clinical Research Practice Datalink. Fibromyalgia patients without prior cancer who were new opioid users were included. Logistic regression, a random forest model and Boruta feature selection were used to identify risk factors related to long-term opioid use. Adjusted ORs (aORs) and feature importance scores were calculated to gauge the strength of these associations., Results: In this study, 28 552 fibromyalgia patients initiating opioids were identified of which 7369 patients (26%) had long-term opioid use. High initial opioid dose (aOR: 31.96, mean decrease accuracy (MDA) 135), history of self-harm (aOR: 2.01, MDA 44), obesity (aOR: 2.43, MDA 36), high deprivation (aOR: 2.00, MDA 31) and substance use disorder (aOR: 2.08, MDA 25) were the factors most strongly associated with long-term use., Conclusions: High dose of initial opioid prescription, a history of self-harm, obesity, high deprivation, substance use disorder and age were associated with long-term opioid use. This study underscores the importance of recognising these individual risk factors in fibromyalgia patients to better navigate the complexities of opioid use and facilitate patient-centred care., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The association between the glycaemic index and the glycaemic load with type 2 diabetes incidence is controversial. We aimed to evaluate this association in an international cohort with diverse glycaemic index and glycaemic load diets., Methods: The PURE study is a prospective cohort study of 127 594 adults aged 35-70 years from 20 high-income, middle-income, and low-income countries. Diet was assessed at baseline using country-specific validated food frequency questionnaires. The glycaemic index and the glycaemic load were estimated on the basis of the intake of seven categories of carbohydrate-containing foods. Participants were categorised into quintiles of glycaemic index and glycaemic load. The primary outcome was incident type 2 diabetes. Multivariable Cox Frailty models with random intercepts for study centre were used to calculate hazard ratios (HRs)., Findings: During a median follow-up of 11·8 years (IQR 9·0-13·0), 7326 (5·7%) incident cases of type 2 diabetes occurred. In multivariable adjusted analyses, a diet with a higher glycaemic index was significantly associated with a higher risk of diabetes (quintile 5 vs quintile 1; HR 1·15 [95% CI 1·03-1·29]). Participants in the highest quintile of the glycaemic load had a higher risk of incident type 2 diabetes compared with those in the lowest quintile (HR 1·21, 95% CI 1·06-1·37). The glycaemic index was more strongly associated with diabetes among individuals with a higher BMI (quintile 5 vs quintile 1; HR 1·23 [95% CI 1·08-1·41]) than those with a lower BMI (quintile 5 vs quintile 1; 1·10 [0·87-1·39]; p interaction=0·030)., Interpretation: Diets with a high glycaemic index and a high glycaemic load were associated with a higher risk of incident type 2 diabetes in a multinational cohort spanning five continents. Our findings suggest that consuming low glycaemic index and low glycaemic load diets might prevent the development of type 2 diabetes., Funding: Full funding sources are listed at the end of the Article., Competing Interests: Declaration of interests VMi reports a research grant from the Canadian Institutes of Health Research during the conduct of the study. DAJ reports research grants from the Soy Nutrition Institute and the Canadian Institutes of Health Research; in-kind supplies for trials as a research support from the Almond Board of California, Walnut Council of California, the Peanut Institute, Barilla, Unilever, Unico, Primo, Loblaw Companies, Quaker (Pepsico), Pristine Gourmet, Bunge, Kellogg Canada, and WhiteWave Foods; payment or honoraria for lectures or presentations from Nutritional Fundamentals for Health–Nutramedica, Saint Barnabas Medical Center Rutgers New Jersey Medical School, The University of Chicago, 2020 China Glycemic Index International Conference, Atlantic Pain Conference, Academy of Life Long Learning; and travel support from NUTS 2022 and the 40th International Symposium on Diabetes and Nutrition. DAJ is a co-chair of the International Carbohydrate Quality Consortium and has been invited by the International Diabetes Federation to join the committee on diabetes treatment and to take the lead in writing the dietary guidelines for the treatment of diabetes. His wife, Alexandra L Jenkins, is a director and partner of INQUIS Clinical Research for the Food Industry; his two daughters, Wendy Jenkins and Amy Jenkins, have published a vegetarian book that promotes the use of the foods described here (Jenkins WM, Jenkins AE, Jenkins AL, Brydson C. The portfolio diet for cardiovascular disease risk reduction. London: Elsevier, 2019); and his sister, Caroline Brydson, received funding through a grant from the St Michael's Hospital Foundation to develop a cookbook for one of his studies. AR reports funding from the Swedish state, the Swedish Research Council, and the Swedish Heart and Lung Foundation. AA reports consulting fees from NovoNordisk; payment or Honoria for lectures or presentations from NovoNordisk Scientific Board; support for attending meetings or travel to the European Society Cardiology Meeting from NovoNordisk; and participation on data safety monitoring board or advisory board for OPTIMAL-Stroke, OPTIMAL-Diabetes, and IMPACT-BP. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: It was apparent from the early phase of the SARS-CoV-2 virus (COVID-19) pandemic that a multi-system syndrome can develop in the weeks following a COVID-19 infection, now referred to as Long COVID. Given that people living with diabetes are at increased risk of hospital admission/poor outcomes following COVID-19 infection we hypothesised that they may also be more susceptible to developing Long COVID. We describe here the prevalence of Long COVID in people living with diabetes when compared to matched controls in a Northwest UK population., Methods: This was a retrospective cohort study of people who had a recorded diagnosis of type 1 diabetes (T1D) or type 2 diabetes (T2D) who were alive on 1st January 2020 and who had a proven COVID-19 infection. We used electronic health record data from the Greater Manchester Care Record collected from 1st January 2020 to 16th September 2023, we determined the prevalence of Long COVID in people with T1D and T2D vs matched individuals without diabetes (non-DM)., Findings: There were 3087 T1D individuals with 14,077 non-diabetes controls and 3087 T2D individuals with 14,077 non-diabetes controls and 29,700 T2D individuals vs 119,951 controls. For T1D, there was a lower proportion of Long COVID diagnosis and/or referral to a Long COVID service at 0.33% vs 0.48% for matched controls. The prevalence of Long COVID In T2D individuals was 0.53% vs 1:3 matched controls 0.54%. For T2D, there were differences by sex in the prevalence of Long COVID in comparison with 1:3 matched controls. For Long COVID between males with T2D and their matched controls, the prevalence was lower in matched controls at 0.46%.vs 0.54% (0.008). When considering the prevalence of LC between females with T2D and their matched controls, the prevalence was higher in matched controls at 0.61% vs 0.53% (0.007). The prevalence of Long COVID in males with T2D vs females was not different. T2D patients at older vs younger age were at reduced risk of developing Long COVID (OR 0.994 [95% CI) [0.989, 0.999]). For females there was a minor increase of risk (OR 1.179, 95% CI [1.002, 1.387]). Presence of a higher body mass index (BMI) was also associated an increased risk of developing Long COVID (OR 1.013, 95% CI [1.001, 1.026]). The estimated general population prevalence of Long COVID based on general practice coding (not self-reported) of this diagnosis was 0.5% of people with a prior acute COVID-19 diagnosis., Interpretation: Recorded Long COVID was more prevalent in men with T2D than in matched non-T2D controls with the opposite seen for T2D women, with recorded Long COVID rates being similar for T2D men and women. Younger age, female sex and higher BMI were all associated with a greater likelihood of developing Long COVID when taken as individual variables. There remains an imperative for continuing awareness of Long COVID as a differential diagnosis for multi-system symptomatic presentation in the context of a previous acute COVID-19 infection., Funding: The time of co-author RW was supported by the NIHR Applied Research Collaboration Greater Manchester (NIHR200174) and the NIHR Manchester Biomedical Research Centre (NIHR203308)., Competing Interests: No author has any conflict of interest., (© 2024 Published by Elsevier Ltd.)

Objective: To investigate opioid prescribing trends and assess the impact of the COVID-19 pandemic on opioid prescribing in rheumatic and musculoskeletal diseases (RMDs)., Methods: Adult patients with RA, PsA, axial spondyloarthritis (AxSpA), SLE, OA and FM with opioid prescriptions between 1 January 2006 and 31 August 2021 without cancer in UK primary care were included. Age- and gender-standardized yearly rates of new and prevalent opioid users were calculated between 2006 and 2021. For prevalent users, monthly measures of mean morphine milligram equivalents (MME)/day were calculated between 2006 and 2021. To assess the impact of the pandemic, we fitted regression models to the monthly number of prevalent opioid users between January 2015 and August 2021. The time coefficient reflects the trend pre-pandemic and the interaction term coefficient represents the change in the trend during the pandemic., Results: The study included 1 313 519 RMD patients. New opioid users for RA, PsA and FM increased from 2.6, 1.0 and 3.4/10 000 persons in 2006 to 4.5, 1.8 and 8.7, respectively, in 2018 or 2019. This was followed by a fall to 2.4, 1.2 and 5.9, respectively, in 2021. Prevalent opioid users for all RMDs increased from 2006 but plateaued or dropped beyond 2018, with a 4.5-fold increase in FM between 2006 and 2021. In this period, MME/day increased for all RMDs, with the highest for FM (≥35). During COVID-19 lockdowns, RA, PsA and FM showed significant changes in the trend of prevalent opioid users. The trend for FM increased pre-pandemic and started decreasing during the pandemic., Conclusion: The plateauing or decreasing trend of opioid users for RMDs after 2018 may reflect the efforts to tackle rising opioid prescribing in the UK. The pandemic led to fewer people on opioids for most RMDs, providing reassurance that there was no sudden increase in opioid prescribing during the pandemic., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Clinical prediction models are increasingly used across healthcare to support clinical decision making. Existing methods and models are time-invariant and thus ignore the changes in populations and healthcare practice that occur over time. We aimed to compare the performance of time-invariant with time-variant models in UK National Adult Cardiac Surgery Audit data from Manchester University NHS Foundation Trust between 2009 and 2019. Data from 2009-2011 were used for initial model fitting, and data from 2012-2019 for validation and updating. We fitted four models to the data: a time-invariant logistic regression model (not updated), a logistic model which was updated every year and validated it in each subsequent year, a logistic regression model where the intercept is a function of calendar time (not updated), and a continually updating Bayesian logistic model which was updated with each new observation and continuously validated. We report predictive performance over the complete validation cohort and for each year in the validation data. Over the complete validation data, the Bayesian model had the best predictive performance.

Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (coronavirus disease 2019 [COVID-19]) pandemic revealed the vulnerability of specific population groups in relation to susceptibility to acute deterioration in their health, including hospital admission and mortality. There is less data on outcomes for people with type 1 diabetes (T1D) following SARS-CoV-2 infection than for those with type 2 diabetes (T2D). In this study we set out to determine the relative likelihood of hospital admission following SARS-CoV-2 infection in people with T1D when compared to those without T1D., Methods: This study was conducted as a retrospective cohort study and utilised an all-England dataset. Electronic health record data relating to people in a national England database (NHS England's Secure Data Environment, accessed via the BHF Data Science Centre's CVD-COVID-UK/COVID-IMPACT consortium) were analysed. The cohort consisted of patients with a confirmed SARS-CoV-2 infection, and the exposure was whether or not an individual had T1D prior to infection (77,392 patients with T1D). The patients without T1D were matched for sex, age and approximate date of the positive COVID-19 test, with three SARS-CoV-2-infected people living without diabetes (n = 223,995). Potential factors influencing the relative likelihood of the outcome of hospital admission within 28 days were ascertained using univariable and multivariable logistic regression., Results: Median age of the people living with T1D was 37 (interquartile range 25-52) years, 47.4% were female and 89.6% were of white ethnicity. Mean body mass index was 27 (standard error [SE] 0.022) kg/m 2 , and mean glycated haemoglobin (HbA1c) was 67.3 (SE 0.069) mmol/mol (8.3%). A significantly higher proportion of people with T1D (10.7%) versus matched non-diabetes individuals (3.9%) were admitted to hospital. In combined analysis including individuals with T1D and matched controls, multiple regression modelling indicated that the factors independently relating to a higher likelihood of hospital admission were: T1D (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.62-1.80]), age (OR 1.02, 95% CI 1.02-1.03), social deprivation (higher Townsend deprivation score: OR 1.07, 95% CI 1.06-1.08), lower estimated glomerular filtration rate (eGFR) value (OR 0.975, 95% CI 0.974-0.976), non-white ethnicity (OR black 1.19, 95% CI 1.06-1.33/OR Asian 1.21, 95% CI 1.05-1.39) and having asthma (OR 1.27, 95% CI 1.19-1.35]), chronic obstructive pulmonary disease (OR 2.10, 95% CI 1.89-2.32), severe mental illness (OR 1.83, 95% CI 1.57-2.12) or hypertension (OR 1.44, 95% CI 1.37-1.52)., Conclusion: In this all-England study, we describe that, following confirmed infection with SARS-CoV-2, the risk factors for hospital admission for people living with T1D are similar to people without diabetes following confirmed SARS-CoV-2 infection, although the former were more likely to be admitted to hospital. The younger age of individuals with T1D in relation to risk stratification must be taken into account in any ongoing risk reduction strategies regarding COVID-19/future viral pandemics., (© 2023. The Author(s).)

Introduction: Since early 2020 the whole world has been challenged by the SARS-CoV-2 virus (COVID-19), its successive variants and the associated pandemic caused. We have previously shown that for people living with type 2 diabetes (T2DM), the risk of being admitted to hospital or dying following a COVID-19 infection progressively decreased through the first months of 2021. In this subsequent analysis we have examined how the UK COVID-19 vaccination programme impacted differentially on COVID-19 outcomes in people with T1DM or T2DM compared to appropriate controls., Methods: T1DM and T2DM affected individuals were compared with their matched controls on 3:1 ratio basis. A 28-day hospital admission or mortality was used as the binary outcome variable with diabetes status and vaccination for COVID-19 as the main exposure variables., Results: A higher proportion of T1DM individuals vs their controls was found to be vaccinated at the point of their first recorded positive COVID-19 test when compared to T2DM individuals vs their controls. Regarding the 28-day hospital admission rate, there was a greater and increasing protective effect of subsequent vaccination dosage (one, two or three) in mitigating the effects of COVID-19 infection versus no vaccination in T1DM than in T2DM individuals when compared with matched controls. Similar effects were observed in T2DM for death. Across both diabetes and non-diabetes individuals, those at greater socio-economic disadvantage were more likely to test positive for COVID-19 in the early phase of the pandemic. For T2DM individuals socio-economic disadvantage was associated with a greater likelihood of hospital admission and death, independent of vaccination status. Age and male sex were also independently associated with 28-day hospital admission in T2DM and to 28-day mortality, independent of vaccination status. African ethnicity was also an additional factor for hospital admission in people with T2DM., Conclusion: A beneficial effect of COVID-19 vaccination was seen in mitigating the harmful effects of COVID-19 infection; this was manifest in reduced hospital admission rate in T1DM individuals with a lesser effect in T2DM when compared with matched controls, regarding both hospital admission and mortality. Socio-economic disadvantage influenced likelihood of COVID-19 confirmed infection and the likelihood of hospital admission/death independent of the number of vaccinations given in T2DM., (© 2022. The Author(s).)

Background: Idiopathic pulmonary fibrosis (IPF) is an incurable lung disease characterised by progressive scarring leading to alveolar stiffness, reduced lung capacity, and impeded gas transfer. We aimed to identify genetic variants associated with declining lung capacity or declining gas transfer after diagnosis of IPF., Methods: We did a genome-wide meta-analysis of longitudinal measures of forced vital capacity (FVC) and diffusing capacity of the lung for carbon monoxide (DLCO) in individuals diagnosed with IPF. Individuals were recruited to three studies between June, 1996, and August, 2017, from across centres in the US, UK, and Spain. Suggestively significant variants were investigated further in an additional independent study (CleanUP-IPF). All four studies diagnosed cases following American Thoracic Society/European Respiratory Society guidelines. Variants were defined as significantly associated if they had a meta-analysis p<5 × 10 -8 when meta-analysing across all discovery and follow-up studies, had consistent direction of effects across all four studies, and were nominally significant (p<0·05) in each study., Findings: 1329 individuals with a total of 5216 measures were included in the FVC analysis. 975 individuals with a total of 3361 measures were included in the DLCO analysis. For the discovery genome-wide analyses, 7 611 174 genetic variants were included in the FVC analysis and 7 536 843 in the DLCO analysis. One variant (rs115982800) located in an antisense RNA gene for protein kinase N2 (PKN2) showed a genome-wide significant association with FVC decline (-140 mL/year per risk allele [95% CI -180 to -100]; p=9·14 × 10 -12 )., Interpretation: Our analysis identifies a genetic variant associated with disease progression, which might highlight a new biological mechanism for IPF. We found that PKN2, a Rho and Rac effector protein, is the most likely gene of interest from this analysis. PKN2 inhibitors are currently in development and signify a potential novel therapeutic approach for IPF., Funding: Action for Pulmonary Fibrosis, Medical Research Council, Wellcome Trust, and National Institutes of Health National Heart, Lung, and Blood Institute., Competing Interests: Declaration of interests LVW reports research funding from GlaxoSmithKline and Orion Pharma, and consultancy for Galapagos, outside of the submitted work. JMO reports personal fees from Boehringer Ingelheim, Genentech, United Therapeutics, AmMax Bio, and Lupin Pharmaceuticals, outside of the submitted work. RGJ is a trustee of Action for Pulmonary Fibrosis and reports personal fees from AstraZeneca, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Chiesi, Daewoong, Galapagos, Galecto, GlaxoSmithKline, Heptares, NuMedii, PatientMPower, Pliant, Promedior, Redx, Resolution Therapeutics, Roche, Veracyte, and Vicore, outside of the submitted work. AA reports personal fees from Boehringer Ingelheim and Genentech, outside of the submitted work. NK served as a consultant to Biogen, Boehringer Ingelheim, Third Rock, Pliant, Samumed, NuMedii, TheraVance, Indalo, LifeMax, Three Lake Partners, Optikira, AstraZeneca, Rohbar, Veracyte, Augmanity, Gilead, Chiesi, Arrowhead, CSL-Behring, Galapagos, and Thyron over the past 3 years; reports Equity in Pliant and Thyron; reports being a scientific founder of Thyron; grants from Veracyte, Boehringer Ingelheim, Bristol Myers Squibb, and the Three Lakes Foundation; non-financial support from MiRagen and AstraZeneca; and has intellectual property on novel biomarkers and therapeutics in idiopathic pulmonary fibrosis licensed to Biotech. MDT reports grants or contracts from research collaborations with GlaxoSmithKline and Orion Pharma. RBH reports grants or contracts from Galapagos for serving on a trial adjudication outcome committee for an IPF trial, from AstraZeneca for serving on a COVID-19 vaccine safety committee, and from Boehringer Ingelheim for an IPF service provision consultation. WAF and EO are employees of GlaxoSmithKline. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

The fourth industrial revolution is based on cyber-physical systems and the connectivity of devices. It is currently unclear what the consequences are for patient safety as existing digital health technologies become ubiquitous with increasing pace and interact in unforeseen ways. In this paper, we describe the output from a workshop focused on identifying the patient safety challenges associated with emerging digital health technologies. We discuss six challenges identified in the workshop and present recommendations to address the patient safety concerns posed by them. A key implication of considering the challenges and opportunities for Patient Safety Informatics is the interdisciplinary contribution required to study digital health technologies within their embedded context. The principles underlying our recommendations are those of proactive and systems approaches that relate the social, technical and regulatory facets underpinning patient safety informatics theory and practice.

Introduction: Since early 2020 the whole world has been challenged by the SARS-CoV-2 virus and the associated global pandemic (Covid-19). People with diabetes are particularly at high risk of becoming seriously unwell after contracting this virus., Methods: This population-based study included people living in the Greater Manchester conurbation who had a recorded diagnosis of type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) and subsequent Covid-19 infection. Each individual with T1DM (n = 862) or T2DM (n = 13,225) was matched with three Covid-19-infected non-diabetes controls., Results: For individuals with T1DM, hospital admission rate in the first 28 days after a positive Covid-19 test was 10% vs 4.7% in age/gender-matched controls [relative risk (RR) 2.1]. For individuals with T2DM, hospital admission rate after a positive Covid-19 test was 16.3% vs 11.6% in age/gender-matched controls (RR 1.4). The average Townsend score was higher in T2DM (1.8) vs matched controls (0.4), with a higher proportion of people with T2DM observed in the top two quintiles of greatest disadvantage (p < 0.001). For Covid-19-infected individuals with T1DM, factors influencing admission likelihood included age, body mass index (BMI), hypertension, HbA1c, low HDL-cholesterol, lower estimated glomerular filtration rate (eGFR), chronic obstructive pulmonary disease (COPD) and being of African/mixed ethnicity. In Covid-19-infected individuals with T2DM, factors related to a higher admission rate included age, Townsend index, comorbidity with COPD/asthma and severe mental illness (SMI), lower eGFR. Metformin prescription lowered the likelihood. For multivariate analysis in combined individuals with T2DM/controls, factors relating to higher likelihood of admission were having T2DM/age/male gender/diagnosed COPD/diagnosed hypertension/social deprivation (higher Townsend index) and non-white ethnicity (all groups)., Conclusion: In a UK population we have confirmed a significantly higher likelihood of admission in people with diabetes following Covid-19 infection. A number of factors mediate that increased likelihood of hospital admission. For T2DM, the majority of factors related to increased admission rate are common to the general population but more prevalent in T2DM. There was a protective effect of metformin in people with T2DM., (© 2022. The Author(s).)

Introduction: Research is ongoing to increase our understanding of how much a previous diagnosis of type 2 diabetes mellitus (T2DM) affects someone's risk of becoming seriously unwell following a COVID-19 infection. In this study we set out to determine the relative likelihood of death following COVID-19 infection in people with T2DM when compared to those without T2DM. This was conducted as an urban population study and based in the UK., Methods: Analysis of electronic health record data was performed relating to people living in the Greater Manchester conurbation (population 2.82 million) who had a recorded diagnosis of T2DM and subsequent COVID-19 confirmed infection. Each individual with T2DM (n = 13,807) was matched with three COVID-19-infected non-diabetes controls (n = 39,583). Data were extracted from the Greater Manchester Care Record (GMCR) database for the period 1 January 2020 to 30 June 2021. Social disadvantage was assessed through Townsend scores. Death rates were compared in people with T2DM to their respective non-diabetes controls; potential predictive factors influencing the relative likelihood of admission were ascertained using univariable and multivariable logistic regression., Results: For individuals with T2DM, their mortality rate after a COVID-19 positive test was 7.7% vs 6.0% in matched controls; the relative risk (RR) of death was 1.28. From univariate analysis performed within the group of individuals with T2DM, the likelihood of death following a COVID-19 recorded infection was lower in people taking metformin, a sodium-glucose cotransporter 2 inhibitor (SGLT2i) or a glucagon-like peptide 1 (GLP-1) agonist. Estimated glomerular filtration rate (eGFR) and hypertension were associated with increased mortality and had odds ratios of 0.96 (95% confidence interval 0.96-0.97) and 1.92 (95% confidence interval 1.68-2.20), respectively. Likelihood of death following a COVID-19 infection was also higher in those people with a diagnosis of chronic obstructive pulmonary disease (COPD) or severe enduring mental illness but not with asthma, and in people taking aspirin/clopidogrel/insulin. Smoking in people with T2DM significantly increased mortality rate (odds ratio of 1.46; 95% confidence interval 1.29-1.65). In a combined analysis of patients with T2DM and controls, multiple regression modelling indicated that the factors independently relating to a higher likelihood of death (accounting for 26% of variance) were T2DM, age, male gender and social deprivation (higher Townsend score)., Conclusion: Following confirmed infection with COVID-19 a number of factors are associated with mortality in individuals with T2DM. Prescription of metformin, SGLT2is or GLP-1 agonists and non-smoking status appeared to be associated with a reduced the risk of death for people with T2DM. Age, male sex and social disadvantage are associated with an increased risk of death., (© 2022. The Author(s).)

Introduction: There are clinical reports that the incorporation of dasatinib may increase the frequency of osteonecrosis in acute lymphoblastic leukemia (ALL) treatment regimens. No rigorous testing of this hypothesis is available to guide clinicians., Methods: We tested whether oral dasatinib increased the frequency of dexamethasone-induced osteonecrosis in a murine model and tested its effects on dexamethasone's antileukemic efficacy in a murine BCR-ABL + model of ALL., Results: Dasatinib did not change the frequency of osteonecrosis (p = .99) nor of arteriopathy (p = .36) in dexamethasone-treated mice when given at dosages that achieved clinically relevant steady-state dasatinib plasma concentrations of 53.1 ng/ml (95% CI: 43.5-57.3 ng/ml). These dasatinib exposures were not associated with increased dexamethasone plasma exposure in nonleukemia-bearing mice. These same dosages were not associated with any decrement in antileukemic efficacy of dexamethasone in a responsive BCR-ABL + model of ALL., Conclusions: Based on the results of our preclinical murine studies, we conclude that dasatinib is unlikely to increase the osteonecrotic effects of dexamethasone in ALL regimens., (© 2021 Wiley Periodicals LLC.)

Competing Interests: There are conflicts of interest.

Competing Interests: There are no conflicts of interest.

Background: Surveillance of temporal trends in clinically treated self-harm is an important component of suicide prevention in the dynamic context of COVID-19. There is little evidence beyond the initial months following the onset of the pandemic, despite national and regional restrictions persisting to mid-2021., Methods: Descriptive time series analysis utilizing de-identified, primary care health records of 2.8 million patients from the Greater Manchester Care Record. Frequencies of self-harm episodes between 1st January 2019 and 31st May 2021 were examined, including stratification by sex, age group, ethnicity, and index of multiple deprivation quintile., Findings: There were 33,444 episodes of self-harm by 13,148 individuals recorded during the study period. Frequency ratios of incident and all episodes of self-harm were 0.59 (95% CI 0.51 to 0.69) and 0.69 (CI 0.63 to 0.75) respectively in April 2020 compared to February 2020. Between August 2020 and May 2021 frequency ratios were 0.92 (CI 0.88 to 0.96) for incident episodes and 0.86 (CI 0.84 to 0.88) for all episodes compared to the same months in 2019. Reductions were largest among men and people living in the most deprived neighbourhoods, while an increase in all-episode self-harm was observed for adolescents aged 10-17., Interpretation: Reductions in primary care-recorded self-harm persisted to May 2021, though they were less marked than in April 2020 during the first national lockdown. The observed reductions could represent longer term reluctance to seek help from health services. Our findings have implications for the ability for services to offer recommended care for patients who have harmed themselves., Competing Interests: NK reports grants and personal fees from the UK Department of Health and Social Care, the National Institute of Health Research (NIHR), the National Institute for Health and Care Excellence (NICE), and the Healthcare Quality and Improvement Partnership, outside the submitted work; works with NHS England on national quality improvement initiatives for suicide and self-harm; is a member of the advisory group for the National Suicide Prevention Strategy of the Department of Health and Social Care; has chaired NICE guideline committees for Self-harm and Depression; and is currently the Topic Advisor for the new NICE Guidelines for self-harm. All other authors report no conflicts of interest., (© 2021 The Author(s).)

Background: Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. This study aims to investigate methods for the inclusion of RWE in NMA and its impact on the level of uncertainty around the effectiveness estimates, with particular interest in effectiveness of fingolimod., Methods: A range of methods for inclusion of RWE in evidence synthesis were investigated by applying them to an illustrative example in relapsing remitting multiple sclerosis (RRMS). A literature search to identify RCTs and RWE evaluating treatments in RRMS was conducted. To assess the impact of inclusion of RWE on the effectiveness estimates, Bayesian hierarchical and adapted power prior models were applied. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior., Results: Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. 'Power prior' NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs, however, this also increased the level of uncertainty., Conclusion: The 'power prior' method for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted., (© 2021. The Author(s).)

Background: Patients presenting with chest pain represent a large proportion of attendances to emergency departments. In these patients clinicians often consider the diagnosis of acute myocardial infarction (AMI), the timely recognition and treatment of which is clinically important. Clinical prediction models (CPMs) have been used to enhance early diagnosis of AMI. The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid is currently in clinical use across Greater Manchester. CPMs have been shown to deteriorate over time through calibration drift. We aim to assess potential calibration drift with T-MACS and compare methods for updating the model., Methods: We will use routinely collected electronic data from patients who were treated using TMACS at two large NHS hospitals. This is estimated to include approximately 14,000 patient episodes spanning June 2016 to October 2020. The primary outcome of acute myocardial infarction will be sourced from NHS Digital's admitted patient care dataset. We will assess the calibration drift of the existing model and the benefit of updating the CPM by model recalibration, model extension and dynamic updating. These models will be validated by bootstrapping and one step ahead prequential testing. We will evaluate predictive performance using calibrations plots and c-statistics. We will also examine the reclassification of predicted probability with the updated TMACS model., Discussion: CPMs are widely used in modern medicine, but are vulnerable to deteriorating calibration over time. Ongoing refinement using routinely collected electronic data will inevitably be more efficient than deriving and validating new models. In this analysis we will seek to exemplify methods for updating CPMs to protect the initial investment of time and effort. If successful, the updating methods could be used to continually refine the algorithm used within TMACS, maintaining or even improving predictive performance over time., Trial Registration: ISRCTN number: ISRCTN41008456., (© 2021. The Author(s).)

Background: Osteonecrosis is a devastating side effect of acute lymphoblastic leukemia (ALL) therapy. Associations between bone density loss and osteonecrosis have sparked interest in using bisphosphonates to reduce this complication., Procedure: We assessed the impact of zoledronic acid (ZA) on the development of osteonecrosis in murine models when used either throughout therapy (continuous administration) or late in therapy after vascular lesions have developed but before osteonecrosis has occurred. Effects on bone density were measured using microcomputed tomography (μCT)-assessed tibial cortical thickness, while osteonecrosis was assessed histologically in the distal femur. Effects on antileukemic efficacy of chemotherapy were evaluated in both immunocompetent/syngeneic and patient-derived xenograft (PDX) models., Results: Continuous administration of ZA with chemotherapy prevented chemotherapy-associated bone loss (p < .001) and reduced osteonecrosis (p = .048). Late initiation of ZA diminished bone loss (p < .001) but had no impact on the development of osteonecrosis (p = .93). In the immunocompetent murine ALL model, mice treated with ZA and chemotherapy succumbed to leukemia sooner than mice treated with chemotherapy alone (p = .046). Analysis using PDX showed a nonsignificant decrease in survival with ZA (p = .17)., Conclusion: Our data indicate ZA may prevent osteonecrosis if begun with chemotherapy but showed no benefit when administered later in therapy. However, ZA may also reduce the antileukemic efficacy of chemotherapy., (© 2021 Wiley Periodicals LLC.)

Recent clinical trials in children with acute lymphoblastic leukemia (ALL) indicate that severe hypertriglyceridemia (> 1000 mg/dL) during therapy is associated with increased frequency of symptomatic osteonecrosis. Interventions to lower triglycerides have been considered, but there have been no pre-clinical studies investigating impact of lowering triglycerides on osteonecrosis risk, nor whether such interventions interfere with the antileukemic efficacy of ALL treatment. We utilized our clinically relevant mouse model of dexamethasone-induced osteonecrosis to determine if fenofibrate decreased osteonecrosis. To test whether fenofibrate affected the antileukemic efficacy of dexamethasone, we utilized a BCR-ABL+ model of ALL. Serum triglycerides were reduced with fenofibrate throughout treatment, with the most pronounced 4.5-fold decrease at week 3 (p<1x10-6). Both frequency (33% versus 74%, p=0.006) and severity (median necrosis score of 0 versus 75; p=6x10-5) of osteonecrosis were reduced with fenofibrate. Fenofibrate had no impact on BCR-ABL+ ALL survival (p=0.65) nor on the antileukemic properties of dexamethasone (p=0.49). These data suggest that lowering triglycerides with fenofibrate reduces dexamethasone-induced osteonecrosis while maintaining antileukemic efficacy, and thus may be considered for clinical trials.

Background: Observational data have reported that being overweight or obese, compared to being normal weight, is associated with a lower risk for death - the "obesity paradox". We used Mendelian randomization (MR) to estimate causal effects of body mass index (BMI) on mortality risks in people with coronary heart disease (CHD), type 2 diabetes mellitus (T2DM) or malignancy in whom this paradox has been often reported., Methods: We studied 457,746 White British UK Biobank participants including three subgroups with T2DM (n = 19,737), CHD (n = 21,925) or cancer (n = 42,612) at baseline and used multivariable-adjusted Cox models and MR approaches to describe relationships between BMI and mortality risk., Results: Observational Cox models showed J-shaped relationships between BMI and mortality risk including within disease subgroups in which the BMI values associated with minimum mortality risk were within overweight/obese ranges (26.5-32.5 kg/m 2 ). In all participants, MR analyses showed a positive linear causal effect of BMI on mortality risk (HR for mortality per unit higher BMI: 1.05; 95% CI: 1.03-1.08), also evident in people with CHD (HR: 1.08; 95% CI: 1.01-1.14). Point estimates for hazard ratios across all BMI values in participants with T2DM and cancer were consistent with overall positive linear effects but confidence intervals included the null., Conclusion: These data support the idea that population efforts to promote intentional weight loss towards the normal BMI range would reduce, not enhance, mortality risk in the general population including, importantly, individuals with CHD., Competing Interests: Declaration of Competing Interest MKR reports speaker fees from Novo Nordisk and modest stock ownership in GSK. NS has consulted for Astrazeneca, Boehringer Ingelheim, Eli-Lilly, Napp, Novo Nordisk, Pfizer and Sanofi. The remaining authors have nothing to disclose., (Copyright © 2021. Published by Elsevier B.V.)

Background: The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. For many applications, this is perfectly acceptable. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions., Aims: We aimed to identify published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference. We aimed to identify the main methodological approaches, their underlying assumptions, targeted estimands, and potential pitfalls and challenges with using the method. Finally, we aimed to highlight unresolved methodological challenges., Methods: We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used for predictions under hypothetical interventions. We included both methodologies proposed in statistical/machine learning literature and methodologies used in applied studies., Results: We identified 4919 papers through database searches and a further 115 papers through manual searches. Of these, 87 papers were retained for full-text screening, of which 13 were selected for inclusion. We found papers from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation., Conclusions: There exist two broad methodological approaches for allowing prediction under hypothetical intervention into clinical prediction models: (1) enriching prediction models derived from observational studies with estimated causal effects from clinical trials and meta-analyses and (2) estimating prediction models and causal effects directly from observational data. These methods require extending to dynamic treatment regimes, and consideration of multiple interventions to operationalise a clinical decision support system. Techniques for validating 'causal prediction models' are still in their infancy.