Objective. To identify whether active use of nonsteroidal antiinflammatory drugs (NSAIDs) increases susceptibility to developing suspected or confirmed coronavirus disease 2019 (COVID-19) compared to the use of other common analgesics.Methods. We performed a propensity score-matched cohort study with active comparators, using a large UK primary care data set. The cohort consisted of adult patients age ≥18 years with osteoarthritis (OA) who were followed up from January 30 to July 31, 2020. Patients prescribed an NSAID (excluding topical preparations) were compared to those prescribed either co-codamol (paracetamol and codeine) or co-dydramol (paracetamol and dihydrocodeine). A total of 13,202 patients prescribed NSAIDs were identified, compared to 12,457 patients prescribed the comparator drugs. The primary outcome measure was the documentation of suspected or confirmed COVID-19, and the secondary outcome measure was all-cause mortality.Results. During follow-up, the incidence rates of suspected/confirmed COVID-19 were 15.4 and 19.9 per 1,000 person-years in the NSAID-exposed group and comparator group, respectively. Adjusted hazard ratios for suspected or confirmed COVID-19 among the unmatched and propensity score-matched OA cohorts, using data from clinical consultations in primary care settings, were 0.82 (95% confidence interval [95% CI] 0.62-1.10) and 0.79 (95% CI 0.57-1.11), respectively, and adjusted hazard ratios for the risk of all-cause mortality were 0.97 (95% CI 0.75-1.27) and 0.85 (95% CI 0.61-1.20), respectively. There was no effect modification by age or sex. Conclusion.No increase in the risk of suspected or confirmed COVID-19 or mortality was observed among patients with OA in a primary care setting who were prescribed NSAIDs as compared to those who received comparator drugs. These results are reassuring and suggest that in the absence of acute illness, NSAIDs can be safely prescribed during the ongoing pandemic.

Sodium‐glucose co‐transporter‐2 (SGLT2) inhibitors are widely prescribed in people with type 2 diabetes. We aimed to investigate whether SGLT2 inhibitor prescription is associated with COVID‐19, when compared with an active comparator. We performed a propensity‐score‐matched cohort study with active comparators and a negative control outcome in a large UK‐based primary care dataset. Participants prescribed SGLT2 inhibitors (n = 9948) and a comparator group prescribed dipeptidyl peptidase‐4 (DPP‐4) inhibitors (n = 14 917) were followed up from January 30 to July 27, 2020. The primary outcome was confirmed or clinically suspected COVID‐19. The incidence rate of COVID‐19 was 19.7/1000 person‐years among users of SGLT2 inhibitors and 24.7/1000 person‐years among propensity‐score‐matched users of DPP‐4 inhibitors. The adjusted hazard ratio was 0.92 (95% confidence interval 0.66 to 1.29), and there was no evidence of residual confounding in the negative control analysis. We did not observe an increased risk of COVID‐19 in primary care amongst those prescribed SGLT2 inhibitors compared to DPP‐4 inhibitors, suggesting that clinicians may safely use these agents in the everyday care of people with type 2 diabetes during the COVID‐19 pandemic.

Objective Objective Objective Objective To determine risk of cardiovascular diseases (CVD), microvascular complications and mortality in patients with type 2 diabetes who subsequently develop obstructive sleep apnoea(OSA) compared to patients with type 2 diabetes without a diagnosis of OSA. Research Design and Research Design and Research Design and Research Design and Methods Methods Methods Methods An age-, sex-, body mass index-and diabetes duration-matched cohort study using data from a UK primary care database from 01/01/2005 to 17/01/2018. Participants aged ≥16 years with type 2 diabetes were included. Exposed participants were those who developed OSA after their diabetes diagnosis; unexposed participants were those without diagnosed OSA. Outcomes were composite CVD (ischaemic heart disease(IHD), stroke/transient ischaemic attack(TIA), heart failure(HF)); peripheral vascular disease(PVD); atrial fibrillation(AF); peripheral neuropathy(PN); diabetes-related foot disease(DFD); referable retinopathy; chronic kidney disease(CKD); all-cause mortality. The same outcomes were explored in patients with pre-existing OSA before a diagnosis of type 2 diabetes versus diabetes without diagnosed OSA. Results Results Results Results 3,667 exposed participants and 10,450 matched controls were included. Adjusted hazard ratios for the outcomes were:

IntroductionIndividuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4–12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies.Methods and analysisA cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink, and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability and patient-reported outcome measures. Data will be collected monthly for 1 year.Statistical clustering methods will be used to identify distinct Long COVID-19 symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear substudy which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy.We will review existing evidence on interventions for postviral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulative evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation.Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group.Ethics and disseminationEthical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers.Trial registration number1567490.

ObjectivesExisting UK prognostic models for patients admitted to the hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death and intensive therapy unit (ITU) admission) in UK secondary care and externally validate the existing 4C score.DesignCandidate predictors included demographic variables, symptoms, physiological measures, imaging and laboratory tests. Final models used logistic regression with stepwise selection.SettingModel development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset.ParticipantsPatients with COVID-19 admitted to UHB January–August 2020 were included.Main outcome measuresDeath and ITU admission within 28 days of admission.Results1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating characteristic curve (AUROC) for mortality was 0.791 (95% CI 0.761 to 0.822) in UHB and 0.767 (95% CI 0.754 to 0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95% CI 0.883 to 0.929) in UHB and 0.811 (95% CI 0.795 to 0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the International Severe Acute Respiratory and Emerging Infection Consortium 4C score in the UHB dataset was 0.753 (95% CI 0.720 to 0.785).ConclusionsThe novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and performed at least as well as the existing 4C score using only routinely collected patient information. The models can be integrated into electronic medical records systems to calculate each individual patient’s probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.