Characterizing the incidence of adverse events of special interest for COVID-19 vaccines across eight countries: a multinational network cohort study (preprint)

BackgroundAs large-scale immunization programs against COVID-19 proceed around the world, safety signals will emerge that need rapid evaluation. We report population-based, age- and sex- specific background incidence rates of potential adverse events of special interest (AESI) in eight countries using thirteen databases. MethodsThis multi-national network cohort study included eight electronic medical record and five administrative claims databases from Australia, France, Germany, Japan, Netherlands, Spain, the United Kingdom, and the United States, mapped to a common data model. People observed for at least 365 days before 1 January 2017, 2018, or 2019 were included. We based study outcomes on lists published by regulators: acute myocardial infarction, anaphylaxis, appendicitis, Bells palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain-Barre syndrome, hemorrhagic and non-hemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, and transverse myelitis. We calculated incidence rates stratified by age, sex, and database. We pooled rates across databases using random effects meta-analyses. We classified meta-analytic estimates into Council of International Organizations of Medical Sciences categories: very common, common, uncommon, rare, or very rare. FindingsWe analysed 126,661,070 people. Rates varied greatly between databases and by age and sex. Some AESI (e.g., myocardial infarction, Guillain-Barre syndrome) increased with age, while others (e.g., anaphylaxis, appendicitis) were more common in young people. As a result, AESI were classified differently according to age. For example, myocardial infarction was very rare in children, rare in women aged 35-54 years, uncommon in men and women aged 55-84 years, and common in those aged [≥]85 years. InterpretationWe report robust baseline rates of prioritised AESI across 13 databases. Age, sex, and variation between databases should be considered if background AESI rates are compared to event rates observed with COVID-19 vaccines.

Alpha-1 blockers and susceptibility to COVID-19 in benign prostate hyperplasia patients : an international cohort study (preprint)

Alpha-1 blockers, often used to treat benign prostate hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storms release. We conducted a prevalent-user active-comparator cohort study to assess association between alpha-1 blocker use and risks of three COVID-19 outcomes: diagnosis, hospitalization, and hospitalization requiring intensive services. Our study included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH therapy during the period between November 2019 and January 2020, found in electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We found no differential risk for any of COVID-19 outcome, pointing to the need for further research on potential COVID-19 therapies.

Characterizing Post-Acute Sequelae of SARS-CoV-2 Infection across Claims and Electronic Health Record Databases (preprint)

Structured Abstract Importance: Post-acute sequelae of SARS-CoV-2 infection (PASC) is emerging as a major public health issue. Objective: We characterized the incidence of PASC, or related symptoms and diagnoses, for COVID-19 and influenza patients. Design: Retrospective cohort study. Setting: Our data sources were the IBM MarketScan Commercial Claims and Encounters (CCAE), Optum Electronic Health Record (EHR) and Columbia University Irving Medical Center (CUIMC) databases that were transformed to the Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM) and were part of the Observational Health Sciences and Informatics (OHDSI) network. Participants: The COVID-19 cohort consisted of patients with a diagnosis of COVID-19 or positive lab test of SARS-CoV-2 after January 1st 2020 with a follow up period of at least 30 days. The influenza cohort consisted of patients with a diagnosis of influenza between October 1, 2018 and May 1, 2019 with a follow up period of at least 30 days. Intervention: Infection with COVID-19 or influenza. Main Outcomes and Measures: Post-acute sequelae of SARS-CoV-2 infection (PASC), or related diagnoses, for COVID-19 and influenza patients. Results: In aggregate, we characterized the post-acute experience for over 440,000 patients who were diagnosed with COVID-19 or tested positive for SARS-COV-2. The long term sequelae that had a higher incidence in the COVID-19 compared to Influenza cohorts were altered smell or taste, myocarditis, acute kidney injury, dyspnea and alopecia. Additionally, the long term incidences of respiratory illness, musculoskeletal disease, and psychiatric disorders for the COVID-19 population were higher than expected. Conclusions and Relevance: The long term sequelae of COVID-19 and influenza may be different. Further characterization of PASC on large scale observational healthcare databases is warranted.

Highly functional virus-specific cellular immune response in asymptomatic SARS-CoV-2 infection (preprint)

The efficacy of virus-specific T cells in clearing pathogens involves a fine balance between their antiviral and inflammatory features. SARS-CoV-2-specific T cells in individuals who clear SARS-CoV-2 infection without symptoms or disease could reveal non-pathological yet protective characteristics. We therefore compared the quantity and function of SARS-CoV-2-specific T cells in a cohort of asymptomatic individuals (n=85) with that of symptomatic COVID-19 patients (n=76), at different time points after antibody seroconversion. We quantified T cells reactive to structural proteins (M, NP and Spike) using ELISpot assays, and measured the magnitude of cytokine secretion (IL-2, IFN-{gamma}, IL-4, IL-6, IL-1{beta}, TNF- and IL-10) in whole blood following T cell activation with SARS-CoV-2 peptide pools as a functional readout. Frequencies of T cells specific for the different SARS-CoV-2 proteins in the early phases of recovery were similar between asymptomatic and symptomatic individuals. However, we detected an increased IFN-{gamma} and IL-2 production in asymptomatic compared to symptomatic individuals after activation of SARS-CoV-2-specific T cells in blood. This was associated with a proportional secretion of IL-10 and pro-inflammatory cytokines (IL-6, TNF- and IL-1{beta}) only in asymptomatic infection, while a disproportionate secretion of inflammatory cytokines was triggered by SARS-CoV-2-specific T cell activation in symptomatic individuals. Thus, asymptomatic SARS-CoV-2 infected individuals are not characterized by a weak antiviral immunity; on the contrary, they mount a robust and highly functional virus-specific cellular immune response. Their ability to induce a proportionate production of IL-10 might help to reduce inflammatory events during viral clearance.

Unheeded SARS-CoV-2 protein? Look deep into negative-sense RNA (preprint)

SARS-CoV-2 is a novel ssRNA+ virus from the Coronaviridae family, which has caused the global COVID-19 pandemic. The genome of SARS-CoV-2 is one of the largest of RNA viruses, comprising of 26 known protein-coding loci. This study aimed to explore the coding potential of negative-strand RNA intermediate for its potential to contain additional protein coding-loci. Surprisingly, we have found several putative ORFs and one brandt new functional SARS-CoV-2 protein-coding loci and called it Avo1 (Ambient viral ORF1). This sequence is located on negative-sense RNA intermediate and bona fide coding for 81 amino acid residues long protein and contains strong Kozak sequence for translation on eukaryotic ribosomes. In silico translated protein Avo1 has a predominantly alpha-helical structure. The existence of Avo1 gene is supported also by its evolutionarily and structural conservation in RaTG13 bat coronavirus. The nucleotide sequence of Avo1 also contains a unique SREBP2 binding site which is closely related to the so-called cytokine storm in severe COVID-19 patients. Altogether, our results suggest the existence of still undescribed SARS-CoV-2 protein, which may play an important role in the viral lifecycle and COVID-19 pathogenesis.

A Novel Cell Therapy for COVID-19 and Potential Future Pandemics: Virus Induced Lymphocytes (VIL) (preprint)

The a priori T cell repertoire and immune response against SARS-CoV-2 viral antigens may explain the varying clinical course and prognosis of patients having a mild COVID-19 infection as opposed to those developing more fulminant multisystem organ failure and associated mortality. Using a novel SARS-Cov-2-specific artificial antigen presenting cell (aAPC), coupled with a rapid expansion protocol (REP) as practiced in tumor infiltrating lymphocytes (TIL) therapy, we generate an immune catalytic quantity of Virus Induced Lymphocytes (VIL). Using T cell receptor (TCR)-specific aAPCs carrying co-stimulatory molecules and major histocompatibility complex (MHC) class-I immunodominant SARS-CoV-2 peptide-pentamer complexes, we expand virus-specific VIL derived from peripheral blood mononuclear cells (PBMC) of convalescent COVID-19 patients up to 1,000-fold. This is achieved in a clinically relevant 7-day vein-to-vein time-course as a potential adoptive cell therapy (ACT) for COVID-19. We also evaluate this approach for other viral pathogens using Cytomegalovirus (CMV)-specific VIL from donors as a control. Rapidly expanded VIL are enriched in virus antigen-specificity and show an activated, polyfunctional cytokine profile and T effector memory phenotype which may contribute to a robust immune response. Virus-specific T cells can also be delivered allogeneically via MHC-typing and patient human leukocyte antigen (HLA)-matching to provide pragmatic treatment in a large-scale therapeutic setting. These data suggest that VIL may represent a novel therapeutic option that warrants further clinical investigation in the armamentarium against COVID-19 and other possible future pandemics.

Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis (preprint)

Objective: Patients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza. Design: Multinational network cohort study Setting: Electronic health records data from Columbia University Irving Medical Center (CUIMC) (NYC, United States [US]), Optum [US], Department of Veterans Affairs (VA) (US), Information System for Research in Primary Care-Hospitalisation Linked Data (SIDIAP-H) (Spain), and claims data from IQVIA Open Claims (US) and Health Insurance and Review Assessment (HIRA) (South Korea). Participants: All patients with prevalent autoimmune diseases, diagnosed and/or hospitalised between January and June 2020 with COVID-19, and similar patients hospitalised with influenza in 2017-2018 were included. Main outcome measures: 30-day complications during hospitalisation and death Results: We studied 133,589 patients diagnosed and 48,418 hospitalised with COVID-19 with prevalent autoimmune diseases. The majority of participants were female (60.5% to 65.9%) and aged [≥]50 years. The most prevalent autoimmune conditions were psoriasis (3.5 to 32.5%), rheumatoid arthritis (3.9 to 18.9%), and vasculitis (3.3 to 17.6%). Amongst hospitalised patients, Type 1 diabetes was the most common autoimmune condition (4.8% to 7.5%) in US databases, rheumatoid arthritis in HIRA (18.9%), and psoriasis in SIDIAP-H (26.4%). Compared to 70,660 hospitalised with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2% to 4.3% versus 6.3% to 24.6%). Conclusions: Patients with autoimmune diseases had high rates of respiratory complications and 30-day mortality following a hospitalization with COVID-19. Compared to influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality. Future studies should investigate predictors of poor outcomes in COVID-19 patients with autoimmune diseases.

Using Real World Data to Understand HIV and COVID-19 in the U.S.A. and Spain: Characterizing Co-Infected Patients Across the Care Cascade (preprint)

Objective: Most patients severely affected by COVID-19 have been elderly and patients with underlying chronic disease such as diabetes, cardiovascular disease, or respiratory disease. People living with HIV (PLHIV) may have greater risk of contracting or developing severe COVID-19 due to the underlying HIV infection or higher prevalence of comorbidities. Design: This is a cohort study, including PLHIV diagnosed, hospitalized, or requiring intensive services for COVID-19. Methods: Data sources include routine electronic medical record or claims data from the U.S. and Spain. Patient demographics, comorbidities, and medication history are described. Results: Four data sources had a population of HIV/COVID-19 coinfected patients ranging from 288 to 4606 lives. PLHIV diagnosed with COVID-19 were younger than HIV-negative patients diagnosed with COVID-19. PLHIV diagnosed with COVID-19 diagnosis had similar comorbidities as HIV-negative COVID-19 patients with higher prevalence of those comorbidities and history of severe disease. Treatment regimens were similar between PLHIV diagnosed with COVID-19 or PLHIV requiring intensive services. Conclusions: Our study uses routine practice data to explore HIV impact on COVID-19, providing insight into patient history prior to COVID-19. We found that HIV and COVID-19 coinfected patients have higher prevalence of underlying comorbidities such as cardiovascular and respiratory disease as compared to HIV-negative COVID-19 infected patients. We also found that, across the care cascade, co-infected patients who received intensive services were more likely to have more serious underlying disease or a history of more serious events as compared to PLHIV who were diagnosed with COVID-19.

Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States (preprint)

Early identification of symptoms and comorbidities most predictive of COVID-19 is critical to identify infection, guide policies to effectively contain the pandemic, and improve health systems' response. Here, we characterised socio-demographics and comorbidity in 3,316,107 persons tested and 219,072 persons tested positive for SARS-CoV-2 since January 2020, and their key health outcomes in the month following the first positive test. Routine care data from primary care electronic health records (EHR) from Spain, hospital EHR from the United States (US), and claims data from South Korea and the US were used. The majority of study participants were women aged 18-65 years old. Positive/tested ratio varied greatly geographically (2.2:100 to 31.2:100) and over time (from 50:100 in February-April to 6.8:100 in May-June). Fever, cough and dyspnoea were the most common symptoms at presentation. Between 4%-38% required admission and 1-10.5% died within a month from their first positive test. Observed disparity in testing practices led to variable baseline characteristics and outcomes, both nationally (US) and internationally. Our findings highlight the importance of large scale characterization of COVID-19 international cohorts to inform planning and resource allocation including testing as countries face a second wave.

Clinical characteristics, symptoms, management and health outcomes in 8,598 pregnant women diagnosed with COVID-19 compared to 27,510 with seasonal influenza in France, Spain and the US: a network cohort analysis (preprint)

OBJECTIVES: To describe comorbidities, symptoms at presentation, medication use, and 30-day outcomes after a diagnosis of COVID-19 in pregnant women, in comparison to pregnant women with influenza. DESIGN: Multinational network cohort SETTING: A total of 6 databases consisting of electronic medical records and claims data from France, Spain, and the United States. PARTICIPANTS: Pregnant women with [≥] 1 year in contributing databases, diagnosed and/or tested positive, or hospitalized with COVID-19. The influenza cohort was derived from the 2017-2018 influenza season. OUTCOMES: Baseline patient characteristics, comorbidities and presenting symptoms; 30-day inpatient drug utilization, maternal complications and pregnancy-related outcomes following diagnosis/hospitalization. RESULTS: 8,598 women diagnosed (2,031 hospitalized) with COVID-19 were included. Hospitalized women had, compared to those diagnosed, a higher prevalence of pre-existing comorbidities including renal impairment (2.2% diagnosed vs 5.1% hospitalized) and anemia (15.5% diagnosed vs 21.3% hospitalized). The ten most common inpatient treatments were systemic corticosteroids (29.6%), enoxaparin (24.0%), immunoglobulins (21.4%), famotidine (20.9%), azithromycin (18.1%), heparin (15.8%), ceftriaxone (7.9%), aspirin (7.0%), hydroxychloroquine (5.4%) and amoxicillin (3.5%). Compared to 27,510 women with influenza, dyspnea and anosmia were more prevalent in those with COVID-19. Women with COVID-19 had higher frequency of cesarean-section (4.4% vs 3.1%), preterm delivery (0.9% vs 0.5%), and poorer maternal outcomes: pneumonia (12.0% vs 2.7%), ARDS (4.0% vs 0.3%) and sepsis (2.1% vs 0.7%). COVID-19 fatality was negligible (N<5 in each database respectively). CONCLUSIONS: Comorbidities that were more prevalent with COVID-19 hospitalization (compared to COVID-19 diagnosed) in pregnancy included renal impairment and anemia. Multiple medications were used to treat pregnant women hospitalized with COVID-19, some with little evidence of benefit. Anosmia and dyspnea were indicative symptoms of COVID-19 in pregnancy compared to influenza, and may aid differential diagnosis. Despite low fatality, pregnancy and maternal outcomes were worse in COVID-19 than influenza.

Heterogeneity and temporal variation in the management of COVID-19: a multinational drug utilization study including 71,921 hospitalized patients from China, South Korea, Spain, and the United States of America (preprint)

Objectives: A plethora of medicines have been repurposed or used as adjunctive therapies for COVID-19. We characterized the utilization of medicines as prescribed in routine practice amongst patients hospitalized for COVID-19 in South Korea, China, Spain, and the USA. Design: International network cohort Setting: Hospital electronic health records from Columbia University Irving Medical Centre (NYC, USA), Stanford (CA, USA), Tufts (MA, USA), Premier (USA), Optum EHR (USA), department of veterans affairs (USA), NFHCRD (Honghu, China) and HM Hospitals (Spain); and nationwide claims from HIRA (South Korea) Participants: patients hospitalized for COVID-19 from January to June 2020 Main outcome measures: Prescription/dispensation of any medicine on or 30 days after hospital admission date Analyses: Number and percentage of users overall and over time Results: 71,921 people were included: 304 from China, 2,089 from Spain, 7,599 from South Korea, and 61,929 from the USA. A total of 3,455 medicines were identified. Common repurposed medicines included hydroxychloroquine (<2% in NFHCRD to 85.4% in HM), azithromycin (4.9% in NFHCRD to 56.5% in HM), lopinavir/ritonavir (<3% in all US but 34.9% in HIRA and 56.5% in HM), and umifenovir (0% in all except 78.3% in NFHCRD). Adjunctive medicines were used with great variability, with the ten most used treatments being (in descending order): bemiparin, enoxaparin, heparin, ceftriaxone, aspirin, vitamin D, famotidine, vitamin C, dexamethasone, and metformin. Hydroxychloroquine and azithromycin increased rapidly in use in March-April but declined steeply in May-June. Conclusions: Multiple medicines were used in the first months of COVID-19 pandemic, with substantial geographic and temporal variation. Hydroxychloroquine, azithromycin, lopinavir-ritonavir, and umifenovir (in China only) were the most prescribed repurposed medicines. Antithrombotics, antibiotics, H2 receptor antagonists and corticosteroids were often used as adjunctive treatments. Research is needed on the comparative risk and benefit of these treatments in the management of COVID-19.

Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets (preprint)

Background: SARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision making during the pandemic. However, the model is at high risk of bias according to the Prediction model Risk Of Bias ASsessment Tool and has not been externally validated. Methods: We followed the OHDSI framework for external validation to assess the reliability of the C-19 model. We evaluated the model on two different target populations: i) 41,381 patients that have SARS-CoV-2 at an outpatient or emergency room visit and ii) 9,429,285 patients that have influenza or related symptoms during an outpatient or emergency room visit, to predict their risk of hospitalization with pneumonia during the following 0 to 30 days. In total we validated the model across a network of 14 databases spanning the US, Europe, Australia and Asia. Findings: The internal validation performance of the C-19 index was a c-statistic of 0.73 and calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data the model obtained c-statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US and South Korean datasets respectively. The calibration was poor with the model under-estimating risk. When validated on 12 datasets containing influenza patients across the OHDSI network the c-statistics ranged between 0.40-0.68. Interpretation: The results show that the discriminative performance of the C-19 model is low for influenza cohorts, and even worse amongst COVID-19 patients in the US, Spain and South Korea. These results suggest that C-19 should not be used to aid decision making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

Renin-angiotensin system blockers and susceptibility to COVID-19: a multinational open science cohort study (preprint)

Introduction: Angiotensin converting enzyme inhibitors (ACEs) and angiotensin receptor blockers (ARBs) could influence infection risk of coronavirus disease (COVID-19). Observational studies to date lack pre-specification, transparency, rigorous ascertainment adjustment and international generalizability, with contradictory results. Methods: Using electronic health records from Spain (SIDIAP) and the United States (Columbia University Irving Medical Center and Department of Veterans Affairs), we conducted a systematic cohort study with prevalent ACE, ARB, calcium channel blocker (CCB) and thiazide diuretic (THZ) use to determine relative risk of COVID-19 diagnosis and related hospitalization outcomes. The study addressed confounding through large-scale propensity score adjustment and negative control experiments. Results: Following over 1.1 million antihypertensive users identified between November 2019 and January 2020, we observed no significant difference in relative COVID-19 diagnosis risk comparing ACE/ARB vs CCB/THZ monotherapy (hazard ratio: 0.98; 95% CI 0.84 - 1.14), nor any difference for mono/combination use (1.01; 0.90 - 1.15). ACE alone and ARB alone similarly showed no relative risk difference when compared to CCB/THZ monotherapy or mono/combination use. Directly comparing ACE vs. ARB demonstrated a moderately lower risk with ACE, non-significant for monotherapy (0.85; 0.69 - 1.05) and marginally significant for mono/combination users (0.88; 0.79 - 0.99). We observed, however, no significant difference between drug- classes for COVID-19 hospitalization or pneumonia risk across all comparisons. Conclusion: There is no clinically significant increased risk of COVID-19 diagnosis or hospitalization with ACE or ARB use. Users should not discontinue or change their treatment to avoid COVID-19.

Seek COVER: Development and validation of a personalized risk calculator for COVID-19 outcomes in an international network (preprint)

Abstract Importance COVID-19 is causing high mortality worldwide. Developing models to quantify the risk of poor outcomes in infected patients could help develop strategies to shield the most vulnerable during de-confinement. Objective To develop and externally validate COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission (COVER-H), requiring intensive services (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis. Design Multinational, distributed network cohorts. Setting We analyzed a federated network of electronic medical records and administrative claims data from 13 data sources and 6 countries, mapped to a common data model. Participants Model development used a patient population consisting of >2 million patients with a general practice (GP), emergency room (ER), or outpatient (OP) visit with diagnosed influenza or flu-like symptoms any time prior to 2020. The model was validated on patients with a GP, ER, or OP visit in 2020 with a confirmed or suspected COVID-19 diagnosis across four databases from South Korea, Spain and the United States. Outcomes Age, sex, historical conditions, and drug use prior to index date were considered as candidate predictors. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results Overall, 43,061 COVID-19 patients were included for model validation, after initial model development and validation using 6,869,127 patients with influenza or flu-like symptoms. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, and kidney disease) which combined with age and sex could discriminate which patients would experience any of our three outcomes. The models achieved high performance in influenza. When transported to COVID-19 cohorts, the AUC ranges were, COVER-H: 0.73-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.82-0.90. Calibration was overall acceptable, with overestimated risk in the most elderly and highest risk strata. Conclusions and relevance A 9-predictor model performs well for COVID-19 patients for predicting hospitalization, intensive services and death. The models could aid in providing reassurance for low risk patients and shield high risk patients from COVID-19 during de-confinement to reduce the virus' impact on morbidity and mortality.