The cost of primary care consultations associated with long COVID in non-hospitalised patients: a retrospective cohort study using UK primary care data (preprint)

Background: Over 2 million people in the UK self-reported long COVID (symptoms continuing >12 weeks after the first COVID-19 infection) as of December 2022. Long COVID can lead to significant patient burden; however, the economic impact of managing long COVID in primary care is unknown. Objectives: To assess incremental costs of primary care consultations associated with post-Covid-19 condition or long COVID, to estimate associated national costs for the United Kingdom population, and to assess risk factors associated with increased costs. Design: A retrospective cohort study using a propensity score matching approach with an incremental cost method to estimate primary care consultation costs associated with long COVID. Setting: UK-based primary care general practitioner (GP), nurse and physiotherapist consultation data from the Clinical Practice Research Datalink Aurum primary care database from 31st January 2020 to 15th April 2021. Participants: 472,173 non-hospitalised adults with confirmed SARS-CoV-2 infection were 1:1 propensity score matched to a pool of eligible patients with the same index date, the same number of prior consultations, and similar background characteristics, but without a record of COVID-19. Patients diagnosed with Long COVID (3,871) and those with World Health Organisation (WHO) defined symptoms of long COVID (30,174) formed two subgroups within the cohort with confirmed SARS-CoV-2 infection. Methods: Costs were calculated using a bottom-up costing approach with consultation cost per working hour in the British pound sterling (GBP) obtained from the Personal Social Services Research Unit, Unit Costs of Health and Social Care 2021. The average incremental cost in comparison to patients with no record of COVID-19 was produced for each patient group, considering only consultation costs at least 12 weeks from the SARS-CoV-2 infection date or matched date for the comparator group (from 15th April 2020 to 15th April 2021). A sensitivity analysis was undertaken which restricted the study population to only those who had at least 24 weeks of follow-up. National costs were estimated by extrapolating incremental costs to the cumulative incidence of COVID-19 in the UK Office for National Statistics COVID-19 Infection Survey. The impacts of risk factors on the cost of consultations beyond 12 weeks from SARS-CoV-2 infection were assessed using an econometric ordinary least squares (OLS) regression model, where coefficients were interpreted as the percentage change in cost due to a unit increase in the specific factor. Results The incremental cost of primary care consultations potentially associated with long COVID was 2.44 GBP per patient with COVID-19 per year. This increased to 5.72 GBP in the sensitivity analysis. Extrapolating this to the UK population produced a cost estimate of 23,382,452 GBP (90% credible interval: 21,378,567 GBP to 25,526,052 GBP) or 54,814,601 GBP (90% credible interval: 50,116,967 GBP to 59,839,762 GBP) in the sensitivity analysis. Among patients with COVID-19 infection, a long COVID diagnosis and longer-term reporting of symptoms were associated with a 43% and 44% increase in primary care consultation costs respectively, compared to patients without long COVID symptoms. Older age (49% relative increase in costs in those aged 80 years or older compared to those aged 18 to 29 years), female sex (4% relative increase in costs compared to males), obesity (4% relative increase in costs compared to those of normal weight), comorbidities and the number of prior consultations were all associated with an increase in the cost of primary care consultations. By contrast, those from black ethnic groups had a 6% reduced relative cost compared to those from white ethnic groups. Conclusions: The costs of primary care consultations associated with long COVID in non-hospitalised adults are substantial. Costs are significantly higher among those diagnosed with long COVID, those with long COVID symptoms, older adults, females, and those with obesity and comorbidities.

Long Covid symptoms and diagnosis in primary care: a cohort study using structured and unstructured data in The Health Improvement Network primary care database (preprint)

BACKGROUND: Long Covid is a widely recognised consequence of COVID-19 infection, but little is known about the burden of symptoms that patients present with in primary care, as these are typically recorded only in free text clinical notes. Our objectives were to compare symptoms in patients with and without a history of COVID-19, and investigate symptoms associated with a Long Covid diagnosis. METHODS: We used primary care electronic health record data from The Health Improvement Network (THIN), a Cegedim database. We included adults registered with participating practices in England, Scotland or Wales. We extracted information about 89 symptoms and 'Long Covid' diagnoses from free text using natural language processing. We calculated hazard ratios (adjusted for age, sex, baseline medical conditions and prior symptoms) for each symptom from 12 weeks after the COVID-19 diagnosis. FINDINGS: We compared 11,015 patients with confirmed COVID-19 and 18,098 unexposed controls. Only 20% of symptom records were coded, with 80% in free text. A wide range of symptoms were associated with COVID-19 at least 12 weeks post-infection, with strongest associations for fatigue (adjusted hazard ratio (aHR) 3.99, 95% confidence interval (CI) 3.59, 4.44), shortness of breath (aHR 3.14, 95% CI 2.88, 3.42), palpitations (aHR 2.75, 95% CI 2.28, 3.32), and phlegm (aHR 2.88, 95% CI 2.30, 3.61). However, a limited subset of symptoms were recorded within 7 days prior to a Long Covid diagnosis in more than 20% of cases: shortness of breath, chest pain, pain, fatigue, cough, and anxiety / depression. INTERPRETATION: Numerous symptoms are reported to primary care at least 12 weeks after COVID-19 infection, but only a subset are commonly associated with a GP diagnosis of Long Covid.

COVID-19 Vaccination in Pregnancy: The Impact of Multimorbidity and Smoking Status on Vaccine Hesitancy, a Cohort Study of 25,111 Women in Wales, UK (preprint)

Abstract Background Multimorbidity and pregnancy are two risk factors for more severe outcomes after a SARS-CoV-2 infection, thus vaccination uptake is important for pregnant women living with multimorbidity. This study aimed to examine the impact of multimorbidity, smoking status, and demographics (age, ethnic group, area of deprivation) on vaccine hesitancy among pregnant women in Wales using electronic health records (EHR) linkage. Methods This cohort study utilised routinely collected, individual-level, anonymised population-scale linked data within the Secure Anonymised Information Linkage (SAIL) Databank. Pregnant women were identified from 13th April 2021 to 31st December 2021. Survival analysis was utilised to examine and compare the length of time to vaccination uptake in pregnancy by multimorbidity and smoking status, as well as depression, diabetes, asthma, and cardiovascular conditions independently. Variation in uptake by; multimorbidity, smoking status, and demographics was examined jointly and separately for the independent conditions using hazard ratios (HR) from the Cox regression model. A bootstrapping internal validation was conducted to assess the performance of the models. Results Within the population cohort, 8,203 (32.7%) received at least one dose of the COVID-19 vaccine during pregnancy, with 8,572 (34.1%) remaining unvaccinated throughout the follow-up period, and 8,336 (33.2%) receiving the vaccine postpartum. Women aged 30 years or older were more likely to have the vaccine in pregnancy. Those who had depression were slightly but significantly more likely to have the vaccine compared to those without depression (HR = 1.08, 95% CI 1.03 to 1.14, p = 0.02). Women living with multimorbidity (> 1 health condition) were 1.12 times more likely to have the vaccine compared to those living without multimorbidity (HR = 1.12, 95% CI 1.04 to 1.19, p = 0.001). Vaccine uptakes were significantly lower among both current smokers and former smokers compared to never smokers (HR = 0.87, 95% CI 0.81 to 0.94, p < 0.001 and HR = 0.92, 95% CI 0.85 to 0.98, p = 0.015 respectively). Uptake was also lower among those living in the most deprived areas compared to those living in the most affluent areas (HR = 0.89, 95% CI 0.83 to 0.96, p = 0.002). The validated model had similar performance and revealed that multimorbidity, smoking status, age, and deprivation level together have a significant impact on vaccine hesitancy (p < 0.05 for all). Conclusion Younger women, living without multimorbidity (zero or only one health condition), current and former smokers, and those living in the more deprived areas are less likely to have the vaccine, thus, a targeted approach to vaccinations may be required for these groups. Women living with multimorbidity are slightly but significantly less likely to be hesitant about COVID-19 vaccination when pregnant.

COVID-19 Vaccination in Pregnancy: The Impact of Multimorbidity and Smoking Status on Vaccine Hesitancy, a Cohort Study of 25,111 Women in Wales, UK (preprint)

Background Multimorbidity and pregnancy are two risk factors for more severe outcomes after a SARS-CoV-2 infection, thus vaccination uptake is important for pregnant women living with multimorbidity. This study aimed to examine the impact of multimorbidity, smoking status, and demographics (age, ethnic group, area of deprivation) on vaccine hesitancy among pregnant women in Wales using electronic health records (EHR) linkage. Methods This cohort study utilised routinely collected, individual-level, anonymised population-scale linked data within the Secure Anonymised Information Linkage (SAIL) Databank. Pregnant women were identified from 13th April 2021 to 31st December 2021. Survival analysis was utilised to examine and compare the length of time to vaccination uptake in pregnancy by multimorbidity and smoking status, as well as depression, diabetes, asthma, and cardiovascular conditions independently. Variation in uptake by; multimorbidity, smoking status, and demographics was examined jointly and separately for the independent conditions using hazard ratios (HR) from the Cox regression model. Results Within the population cohort, 8,203 (32.7%) received at least one dose of the COVID-19 vaccine during pregnancy, with 8,572 (34.1%) remaining unvaccinated throughout the follow-up period, and 8,336 (33.2%) receiving the vaccine postpartum. Women aged 30 years or older were more likely to have the vaccine in pregnancy. Those who had depression were slightly but significantly more likely to have the vaccine compared to those without depression (HR = 1.08, 95% CI 1.03 to 1.14, p = 0.02). Women living with multimorbidity were 1.12 times more likely to have the vaccine compared to those living without multimorbidity (HR = 1.12, 95% CI 1.04 to 1.19, p = 0.001). Vaccine uptakes were significantly lower among both current smokers and former smokers compared to never smokers (HR = 0.87, 95% CI 0.81 to 0.94, p < 0.001 and HR = 0.92, 95% CI 0.85 to 0.98, p = 0.015 respectively). Uptake was also lower among those living in the most deprived areas compared to those living in the most affluent areas (HR = 0.89, 95% CI 0.83 to 0.96, p = 0.002). Conclusion Younger women, living without multimorbidity, current and former smokers, and those living in the more deprived areas are less likely to have the vaccine, thus, a targeted approach to vaccinations may be required for these groups. Women living with multimorbidity are slightly but significantly less likely to be hesitant about COVID-19 vaccination when pregnant.

The Incidence of Immune Mediated Inflammatory Diseases Following COVID-19: a Matched Cohort Study in UK Primary Care (preprint)

Abstract Objective To assess whether there is an association between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) infection and the incidence of immune mediated inflammatory diseases (IMIDs). Design Matched cohort study. Setting Primary care electronic health record data from the Clinical Practice Research Datalink Aurum database. Participants The exposed cohort included 458,147 adults aged 18 years and older with a confirmed SARS CoV-2 infection by reverse transcriptase polymerase chain reaction (RT-PCR) or lateral flow antigen test, and no prior diagnosis of IMIDs. They were matched on age, sex, and general practice to 1,818,929 adults in the unexposed cohort with no diagnosis of confirmed or suspected SARS CoV-2 infection and no prior diagnosis of IMIDs. Main Outcome Measures The primary outcome measure was a composite of the incidence of any of the following IMIDs: 1. autoimmune thyroiditis, 2. coeliac disease, 3. inflammatory bowel disease (IBD), 4. myasthenia gravis, 5. pernicious anaemia, 6. psoriasis, 7. rheumatoid arthritis (RA), 8. Sjogrens syndrome, 9. systemic lupus erythematosus (SLE), 10. type 1 diabetes mellitus (T1DM), and 11. vitiligo. The secondary outcomes were the incidence of each of these conditions separately. Cox proportional hazards models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI) for the primary and secondary outcomes comparing the exposed to the unexposed cohorts, and adjusting for age, sex, ethnic group, smoking status, body mass index, relevant infections, and medications. Results 537 patients (0.11%) in the exposed cohort developed an IMID during the follow-up period over 0.29 person years, giving a crude incidence rate of 3.54 per 1000 person years. This was compared 1723 patients (0.09%) over 0.29 person years in the unexposed cohort, with an incidence rate of 2.82 per 1000 person years. Patients in the exposed cohort had a 22% relative increased risk of developing an IMID, compared to the unexposed cohort (aHR 1.22, 95% CI 1.10 to 1.34). The incidence of three IMIDs were statistically significantly associated with SARS CoV-2 infection. These were T1DM (aHR 1.56, 95% CI 1.09 to 2.23), IBD (1.52, 1.23 to 1.88), and psoriasis (1.23, 1.05 to 1.42). Conclusions SARS CoV-2 was associated with an increased incidence of IMIDs including T1DM, IBD and psoriasis. Further research is needed to replicate these findings in other populations and to measure autoantibody profiles in cohorts of individuals with COVID-19, including Long COVID and matched controls.

Systematic approach to outcome assessment from coded electronic healthcare records in the DaRe2THINK NHS-embedded randomised trial (preprint)

ABSTRACT Background Improving the efficiency of clinical trials is key to their continued importance in directing evidence-based patient care. Digital innovations, in particular the use of electronic healthcare records (EHR), allow for large-scale screening and follow-up of participants. However, it is critical these developments are accompanied by robust and transparent methods that can support high quality and high clinical value research. Methods The DaRe2THINK trial includes a series of novel processes, including nationwide pseudonymised pre-screening of the primary care EHR across England, digital enrolment, remote e-consent, and ‘no-visit’ follow-up by linking all primary and secondary care health data with patient-reported outcomes. Findings DaRe2THINK is a pragmatic, healthcare-embedded randomised trial testing whether earlier use of direct oral anticoagulants in patients with prior or current atrial fibrillation can prevent thromboembolic events and cognitive decline ( www.birmingham.ac.uk/dare2think ). This paper outlines the systematic approach and methodology employed to define patient information and outcome events. This includes transparency on all medical code lists and phenotypes used in the trial across a variety of national data sources, including Clinical Practice Research Datalink Aurum (primary care), Hospital Episode Statistics (secondary care) and the Office for National Statistics (mortality). Interpretation Co-designed by a patient and public involvement team, DaRe2THINK presents an opportunity to transform the approach to randomised trials in the setting of routine healthcare, providing high-quality evidence generation in populations representative of the community at-risk.

Assessment of 115 symptoms for Long COVID (post-COVID-19 condition) and their risk factors in non-hospitalised individuals: a retrospective matched cohort study in UK primary care (preprint)

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS CoV-2) infection is frequently associated with a wide range of persistent symptoms, now referred to as post-COVID-19 condition, or Long COVID. The objectives of this study were to assess which symptoms are associated with confirmed SARS CoV-2 beyond 12 weeks post-infection in non-hospitalised individuals, and the risk factors associated with developing persistent symptoms. We undertook a retrospective matched cohort study between 31st January 2020 and 15th April 2021 using data from a large database of UK-based primary care electronic health records, Clinical Practice Research Datalink (CPRD) Aurum. We selected 486,149 adult patients with a confirmed diagnosis of SARS CoV-2 infection that had not been hospitalised within 28 days of the diagnosis (infected cohort). We propensity score matched them to 1,944,580 patients without a coded record of either confirmed or suspected COVID-19 (uninfected cohort). Outcomes were the presence of 115 separate symptoms at ≥12 weeks post-infection, and Long COVID, defined as having at least one of the symptoms included in the World Health Organisation case definition. Separate Cox proportional hazards models were used to estimate adjusted hazard ratios (aHR) for individual symptoms and Long Covid. 62 symptoms were significantly associated with prior exposure to SARS CoV-2 after 12 weeks. The largest adjusted hazard ratios (aHR) were for anosmia (aHR 6.49, 95% CI 5.02 to 8.39), hair loss (3.99, 3.63 to 4.39), sneezing (2.77, 1.40 to 5.50), difficulties with ejaculation (2.63, 1.61 to 4.28), reduced libido (2.36, 1.61 to 3.47), shortness of breath at rest (2.20, 1.57 to 3.08), fatigue (1.92, 1.81 to 2.03), pleuritic chest pain (1.86, 1.41 to 2.46), hoarse voice (1.78, 1.44 to 2.20), and fever (1.75, 1.54 to 1.98). Among the infected cohort, risk factors for Long COVID included younger age (aHR 0.75, 95% CI 0.70 to 0.81, for those aged ≥70 years compared to those aged 18 to 30 years), female sex (1.52, 1.48 to 1.56), belonging to an ethnic minority group (1.14 [1.07 to 1.22] for mixed race, 1.21 [1.10 to 1.34] for black ethnic groups, and 1.06 [1.03 to 1.10] for other ethnic minority groups, compared to white ethnic groups), socioeconomic deprivation (1.11 [1.07 to 1.16] for the most compared to the least socioeconomically deprived quintile), smoking (1.12, 1.08 to 1.15), obesity (1.10, 1.07 to 1.14), and a wide range of comorbidities such as COPD. SARS CoV-2 in non-hospitalised individuals is associated with a plethora of symptoms being reported at ≥12 weeks post-infection, with a higher risk associated with younger age, female sex, ethnic minority groups, socioeconomic deprivation, smoking, obesity, and several comorbidities.

Therapies for Long COVID in non-hospitalised individuals - from symptoms, patient-reported outcomes, and immunology to targeted therapies (The TLC Study): Study protocol (preprint)

Introduction Individuals 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 analysis A 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 (CPRD) and invited by their general practitioners to participate on a digital platform (Atom5TM). Individuals will report symptoms, quality of life, work capability, and patient reported outcome measures. Data will be collected monthly for one year. Statistical clustering methods will be used to identify distinct Long COVID symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear sub-study which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy. We will review existing evidence on interventions for post-viral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulated 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 dissemination Ethical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). The study is registered on the ISRCTN Registry (1567490). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers.

Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care (preprint)

Objectives Existing UK prognostic models for patients admitted to 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, intensive therapy unit (ITU) admission) in UK secondary care; and externally validate the existing 4C score. Design Candidate predictors included demographic variables, symptoms, physiological measures, imaging, laboratory tests. Final models used logistic regression with stepwise selection. Setting Model development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset. Participants Patients with COVID-19 admitted to UHB January-August 2020 were included. Main outcome measures Death and ITU admission within 28 days of admission. Results 1040 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 curve (AUROC) for mortality was 0.791 (95%CI 0.761-0.822) in UHB and 0.767 (95%CI 0.754-0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95%CI 0.883-0.929) in UHB and 0.811 (95%CI 0.795-0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the 4C score in the UHB dataset was 0.754 (95%CI 0.721-0.786). Conclusions The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and outperformed the existing 4C score. The models can be integrated into electronic medical records systems to calculate each individual patients probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.

Development and External Validation of Prognostic Models for COVID-19 to Support Risk Stratification in Secondary Care (preprint)

Background: Existing UK prognostic models for patients admitted to 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, intensive therapy unit (ITU) admission) in UK secondary care; and externally validate the existing 4C score. Methods: Patients with COVID-19 admitted to University Hospitals Birmingham (UHB) January-August 2020 were included. Candidate predictors included demographic variables, symptoms, physiological measures, imaging, laboratory tests. Final models used logistic regression with stepwise selection. External validation was performed in the CovidCollab dataset. Findings: 1040 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 curve (AUROC) for mortality was 0.791 (95%CI 0.761-0.822) in UHB and 0.767 (95%CI 0.754-0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95%CI 0.883-0.929) in UHB and 0.811 (95%CI 0.795-0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the 4C score in the UHB dataset was 0.754 (95%CI 0.721-0.786). Interpretation: The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and outperformed the existing 4C score. 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. Funding: Medical Research Council UK Research and Innovation.Declaration of Interests: NJA, ES, KN, MP, AD, CS, TT and YT report a grant from UKRI MRC during the conduct of the study. ES reports grants from National Institute for Health Research (NIHR), Wellcome Trust, MRC, Health Data Research UK (HDR-UK), British Lung Foundation, and Alpha 1 Foundation outside the submitted work. KN reports grants from MRC and HDR-UK outside the submitted work. DP reports grants from NIHR, MRC, and Chernakovsky Foundation outside the submitted work. All other authors have nothing to declare.Ethics Approval Statement: Ethical approval was provided by the East Midlands – Derby REC (reference: 20/EM/0158) for the PIONEER Research Database.

Renin-Angiotensin System Inhibitors and Susceptibility to COVID-19 in Patients With Hypertension: A Propensity Score-Matched Cohort Study in Primary Care (preprint)

Introduction: Renin-angiotensin system (RAS) inhibitors have been postulated to influence susceptibility to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This study investigated whether there is an association between their prescription and the incidence of COVID-19 and all-cause mortality. Methods: We conducted a propensity-score matched cohort study comparing the incidence of COVID-19 among patients with hypertension prescribed ACE inhibitors or ARBs to those treated with calcium channel blockers (CCBs) in a large UK-based primary care database (The Health Improvement Network). We estimated crude incidence rates for confirmed/suspected COVID-19 in each drug exposure group. We used Cox proportional hazards models to produce adjusted hazard ratios for COVID-19. We assessed all-cause mortality as a secondary outcome. Results: The incidence rate of COVID-19 among users of ACE inhibitors and CCBs was 9.3 per 1000 person-years (83 of 18,895 users [0.44%]) and 9.5 per 1000 person-years (85 of 18,895 [0.45%]), respectively. The adjusted hazard ratio was 0.92 (95% CI 0.68 to 1.26). The incidence rate among users of ARBs was 15.8 per 1000 person-years (79 out of 10,623 users [0.74%]). The adjusted hazard ratio was 1.38 (95% CI 0.98 to 1.95). There were no significant associations between use of RAS inhibitors and all-cause mortality. Conclusion: Use of ACE inhibitors was not associated with the risk of COVID-19 whereas use of ARBs was associated with a statistically non-significant increase compared to the use of CCBs. However, no significant associations were observed between prescription of either ACE inhibitors or ARBs and all-cause mortality.

Discovery of SARS-CoV-2 antiviral synergy between remdesivir and approved drugs in human lung cells (preprint)

The SARS coronavirus 2 (SARS-CoV-2) has caused an ongoing global pandemic with currently 29 million confirmed cases and close to a million deaths. At this time, there are no FDA-approved vaccines or therapeutics for COVID-19, but Emergency Use Authorization has been granted for remdesivir, a broad-spectrum antiviral nucleoside analog. However, remdesivir is only moderately efficacious against SARS-CoV-2 in the clinic, and improved treatment strategies are urgently needed. To accomplish this goal, we devised a strategy to identify compounds that act synergistically with remdesivir in preventing SARS-CoV-2 replication. We conducted combinatorial high-throughput screening in the presence of submaximal remdesivir concentrations, using a human lung epithelial cell line infected with a clinical isolate of SARS-CoV-2. We identified 20 approved drugs that act synergistically with remdesivir, many with favorable pharmacokinetic and safety profiles. Strongest effects were observed with established antivirals, Hepatitis C virus nonstructural protein 5 A (HCV NS5A) inhibitors velpatasvir and elbasvir. Combination with their partner drugs sofosbuvir and grazoprevir further increased efficacy, increasing remdesivir's apparent potency 25-fold. We therefore suggest that the FDA-approved Hepatitis C therapeutics Epclusa (velpatasvir/sofosbuvir) and Zepatier (elbasvir/grazoprevir) should be fast-tracked for clinical evaluation in combination with remdesivir to improve treatment of acute SARS-CoV-2 infections.

Renin-angiotensin system inhibitors and susceptibility to COVID-19 in patients with hypertension: a propensity score-matched cohort study in primary care (preprint)

Introduction A significant proportion of patients with Coronavirus Disease-19 (COVID-19) have hypertension and are treated with renin-angiotensin system (RAS) inhibitors, namely angiotensin-converting enzyme I inhibitors (ACE inhibitors) or angiotensin II type-1 receptor blockers (ARBs). These medications have been postulated to influence susceptibility to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The objective of this study was to assess a possible association between prescription of RAS inhibitors and the incidence of COVID-19 and all-cause mortality. Methods We conducted a propensity-score matched cohort study to assess the incidence of COVID-19 among patients with hypertension who were prescribed ACE inhibitors or ARBs compared to patients treated with calcium channel blockers (CCBs) in a large UK-based primary care database (The Health Improvement Network). We estimated crude incidence rates for confirmed/suspected COVID-19 among those prescribed ACE inhibitors, ARBs and CCBs. We used a Cox proportional hazards model to produce adjusted hazard ratios for COVID-19 comparing patients prescribed ACE inhibitors or ARBs to those prescribed CCBs. We further assessed all-cause mortality as a secondary outcome and a composite of accidents, trauma or fractures as a negative control outcome to assess for residual confounding. Results In the propensity score matched analysis, 83 of 18,895 users (0.44%) of ACE inhibitors developed COVID-19 over 8,923 person-years, an incidence rate of 9.3 per 1000 person-years. 85 of 18,895 (0.45%) users of CCBs developed COVID-19 over 8,932 person-years, an incidence rate of 9.5 per 1000 person-years. The adjusted hazard ratio for suspected/confirmed COVID-19 for users of ACE inhibitors compared to CCBs was 0.92 (95% CI 0.68 to 1.26). 79 out of 10,623 users (0.74%) of ARBs developed COVID-19 over 5010 person-years, an incidence rate of 15.8 per 1000 person-years, compared to 11.6 per 1000 person-years among users of CCBs. The adjusted hazard ratio for suspected/confirmed COVID-19 for users of ARBs compared to CCBs was 1.38 (95% CI 0.98 to 1.95). There were no significant associations between use of ACE inhibitors or ARBs and all-cause mortality, compared to use of CCBs. We found no evidence of significant residual confounding with the negative control analysis. Conclusion Current use of ACE inhibitors was not associated with the risk of suspected or confirmed COVID-19 whereas use of ARBs was associated with a statistically non-significant 38% relative increase in risk compared to use of CCBs. However, no significant associations were observed between prescription of either ACE inhibitors or ARBs and all-cause mortality during the peak of the pandemic.

The COVID-19 immune landscape is dynamically and reversibly correlated with disease severity (preprint)

Despite a rapidly growing body of literature on COVID-19, our understanding of the immune correlates of disease severity, course and outcome remains poor. Using mass cytometry, we assessed the immune landscape in longitudinal whole blood specimens from 59 patients presenting with acute COVID-19, and classified based on maximal disease severity. Hospitalized patients negative for SARS-CoV-2 were used as controls. We found that the immune landscape in COVID-19 forms three dominant clusters, which correlate with disease severity. Longitudinal analysis identified a pattern of productive innate and adaptive immune responses in individuals who have a moderate disease course, whereas those with severe disease have features suggestive of a protracted and dysregulated immune response. Further, we identified coordinate immune alterations accompanying clinical improvement and decline that were also seen in patients who received IL-6 pathway blockade. The hospitalized COVID-19 negative cohort allowed us to identify immune alterations that were shared between severe COVID-19 and other critically ill patients. Collectively, our findings indicate that selection of immune interventions should be based in part on disease presentation and early disease trajectory due to the profound differences in the immune response in those with mild to moderate disease and those with the most severe disease.

Systemic corticosteroids show no benefit in severe and critical COVID-19 patients in Wuhan, China: A retrospective cohort study (preprint)

Background: Systemic corticosteroids are recommended by some treatment guidelines and used in severe and critical COVID-19 patients, though evidence supporting such use is limited. Methods: From December 26, 2019 to March 15, 2020, 1514 severe and 249 critical hospitalized COVID-19 patients were collected from two medical centers in Wuhan, China. We performed multivariable Cox models, Cox model with time-varying exposure and propensity score analysis (both inverse-probability-of-treatment-weighting (IPTW) and propensity score matching (PSM)) to estimate the association of corticosteroid use with the risk of in-hospital mortality among severe and critical cases. Results: Corticosteroids were administered in 531 (35.1%) severe and 159 (63.9%) critical patients. Compared to no corticosteroid use group, systemic corticosteroid use showed no benefit in reducing in-hospital mortality in both severe cases (HR=1.77, 95% CI: 1.08-2.89, p=0.023), and critical cases (HR=2.07, 95% CI: 1.08-3.98, p=0.028). In the time-varying Cox analysis that with time varying exposure, systemic corticosteroid use still showed no benefit in either population (for severe patients, HR=2.83, 95% CI: 1.72-4.64, p< 0.001; for critical patients, HR=3.02, 95% CI: 1.59-5.73, p=0.001). Baseline characteristics were matched after IPTW and PSM analysis. For severe COVID-19 patients at admission, corticosteroid use was not associated with improved outcome in either the IPTW analysis. For critical COVID-19 patients at admission, results were consistent with former analysis that corticosteroid use did not reduce in-hospital mortality. Conclusions: Corticosteroid use showed no benefit in reducing in-hospital mortality for severe or critical cases. The routine use of systemic corticosteroids among severe and critical COVID-19 patients was not recommended.

Ethnicity and risk of death in patients hospitalised for COVID-19 infection: an observational cohort study in an urban catchment area (preprint)

BackgroundStudies suggest that certain Black and Asian Minority Ethnic groups experience poorer outcomes from COVID-19 but these studies have not provided insight into potential reasons for this. We hypothesised that outcomes would be poorer for those of South Asian ethnicity hospitalised from a confirmed SARS-CoV-2 infection, once confounding factors, health seeking behaviours and community demographics were considered and that this might reflect a more aggressive disease course in these patients. MethodsPatients with confirmed SARS-CoV-2 infection requiring admission to University Hospitals Birmingham NHS Foundation Trust(UHB) in Birmingham UK between 10th March 2020-17th April 2020 were included. Standardised Admission Ratio(SAR) and Standardised Mortality Ratio(SMR) were calculated using observed COVID-19 admissions/deaths and 2011 census data. Hazard Ratio (aHR) for mortality was estimated using Cox proportional hazard model adjusting and propensity score matching. ResultsAll patients admitted to UHB with COVID-19 during the study period were included (2217 in total). Fifty-eight percent were male, 69.5% White and the majority (80.2%) had co-morbidities. Eighteen and a half percent were of South Asian ethnicity, and these patients were more likely to be younger, have no co-morbidities but twice the prevalence of diabetes than White patients. SAR and SMR suggested more admissions and deaths in South Asian patients than would be predicted and they were more likely to present with severe disease despite no delay in presentation since symptom onset. South Asian ethnicity was associated with an increased risk of death; both by Cox regression (Hazard Ratio 1.4 (95%CI 1.2-1.8) after adjusting for age, sex, deprivation and comorbidities and by propensity score matching, matching for the same factors but categorising ethnicity into South Asian or not (Hazard ratio 1.3 (1.0-1.6)). ConclusionsThose of South Asian ethnicity appear at risk of worse COVID-19 outcomes, further studies need to establish the underlying mechanistic pathways.