Development and internal validation of prehospital prediction models for identifying intracerebral haemorrhage in suspected stroke patients.

Introduction: Distinguishing patients with intracerebral haemorrhage (ICH) from other suspected stroke cases in the prehospital setting is crucial for determining the appropriate level of care and minimising the onset-to-treatment time, thereby potentially improving outcomes. Therefore, we developed prehospital prediction models to identify patients with ICH among suspected stroke cases. Methods: Data were obtained from the Field Administration of Stroke Therapy-Magnesium prehospital stroke trial, where paramedics evaluated multiple variables in suspected stroke cases within the first 2 hours from the last known well time. A total of 19 candidate predictors were included to minimise overfitting and were subsequently refined through the backward exclusion of non-significant predictors. We used logistic regression and eXtreme Gradient Boosting (XGBoost) models to evaluate the performance of the predictors. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), confusion matrix metrics and calibration measures. Additionally, models were internally validated and corrected for optimism through bootstrapping. Furthermore, a nomogram was built to facilitate paramedics in estimating the probability of ICH. Results: We analysed 1649 suspected stroke cases, of which 373 (23%) were finally diagnosed with ICH. From the 19 candidate predictors, 9 were identified as independently associated with ICH (p<0.05). Male sex, arm weakness, worsening neurological status and high systolic blood pressure were positively associated with ICH. Conversely, a history of hyperlipidaemia, atrial fibrillation, coronary artery disease, ischaemic stroke and improving neurological status were associated with other diagnoses. Both logistic regression and XGBoost demonstrated good calibration and predictive performance, with optimism-corrected sensitivities ranging from 47% to 49%, specificities from 89% to 90% and AUCs from 0.796 to 0.801. Conclusions: Our models demonstrate good predictive performance in distinguishing patients with ICH from other diagnoses, making them potentially useful tools for prehospital ICH management.

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts.

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.

Factors associated with long-term opioid use among patients with axial spondyloarthritis or psoriatic arthritis who initiated opioids.

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.

Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data.

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.

The prevalence of long COVID in people with diabetes mellitus-evidence from a UK cohort.

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).

Machine learning identifies risk factors associated with long-term opioid use in fibromyalgia patients newly initiated on an opioid.

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.

Associations of the glycaemic index and the glycaemic load with risk of type 2 diabetes in 127 594 people from 20 countries (PURE): a prospective cohort study.

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.

Comparing Predictive Performance of Time Invariant and Time Variant Clinical Prediction Models in Cardiac Surgery.

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.

Trends for opioid prescribing and the impact of the COVID-19 pandemic in patients with rheumatic and musculoskeletal diseases between 2006 and 2021.

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.

Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England.

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/m2, 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.

COVID-19 Vaccination and Diabetes Mellitus: How Much Has It Made a Difference to Outcomes Following Confirmed COVID-19 Infection?

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.

Longitudinal lung function and gas transfer in individuals with idiopathic pulmonary fibrosis: a genome-wide association study.

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.

Patient Safety Informatics: Meeting the Challenges of Emerging Digital Health.

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.

Mortality in People with Type 2 Diabetes Following SARS-CoV-2 Infection: A Population Level Analysis of Potential Risk Factors.

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.

The Risk Factors Potentially Influencing Hospital Admission in People with Diabetes, Following SARS-CoV-2 Infection: A Population-Level Analysis.

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.