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BackgroundFew datasets have been established that capture the full breadth of COVID-19 patient interactions with a health system. Our first objective was to create a COVID-19 dataset that linked primary care data to COVID-19 testing, hospitalisation, and mortality data at a patient level. Our second objective was to provide a descriptive analysis of COVID-19 outcomes among the general population and describe the characteristics of the affected individuals. MethodsWe mapped patient-level data from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). More than 3,000 data quality checks were performed to assess the readiness of the database for research. Subsequently, to summarise the COVID-19 population captured, we established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or positive test results for SARS-CoV-2, hospitalisations with COVID-19, and COVID-19 deaths during follow-up, which went up until 30th June 2021. FindingsMapping data to the OMOP CDM was performed and high data quality was observed. The mapped database was used to identify a total of 5,870,274 individuals, who were included in the general population cohort as of 1st March 2020. Over follow up, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation with COVID-19, 5,642 had an ICU admission with COVID-19, and 11,233 had a COVID-19 death. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised in general and those who died. InterpretationWe have established a comprehensive dataset that captures COVID-19 diagnoses, test results, hospitalisations, and deaths in Catalonia, Spain. Extensive data checks have shown the data to be fit for use. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19 outcomes over time were described. FundingGeneralitat de Catalunya and European Health Data and Evidence Network (EHDEN).
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ObjectivesTo calculate the observed rates of thrombosis and thrombocytopenia following vaccination against SARS-CoV-2, infection with SARS-CoV-2, and to compare them to background (expected) rates in the general population. DesignCohort study using routinely collected primary care records. SettingRoutine practice in the United Kingdom. ParticipantsTwo mutually exclusive vaccinated cohorts included people vaccinated with either ChAdOx1 or BNT162b2 between 8 December 2020 and 6 March 2021. A third cohort consisted of people newly infected with SARS-Cov-2 identified by a first positive RT-PCR test between 1 September 2020 and 28 February 2021. The fourth general population cohort for background rates included those people with a visit between 1 January 2017 and 31 December 2019. In total, we included 1,868,767 ChAdOx1 and 1,661,139 BNT162b2 vaccinees, 299,311 people infected with SARS-CoV-2, and 2,290,537 people from the general population. InterventionsFirst-dose of either ChAdOx1 or BNT162b2 Main outcome measuresOutcomes included venous thrombosis, arterial thrombosis, thrombocytopenia, and thrombosis with thrombocytopenia. Outcome rates were estimated for recipients of the ChAdOx1 or BNT162b2 vaccines, for people infected with SARS-CoV-2, and background rates in the general population. Indirectly standardized incidence ratios (SIR) were estimated. ResultsWe included 1,868,767 ChAdOx1 and 1,661,139 BNT162b2 vaccinees, 299,311 people infected with SARS-CoV-2, and 2,290,537 people from the general population for background rates. The SIRs for pulmonary embolism were 1.23 [95% CI, 1.09-1.39] after vaccination with ChAdOx1, 1.21 [1.07-1.36] after vaccination with BNT162b2, and 15.31 [14.08 to 16.65] for infection with SARS-CoV-2. The SIRs for thrombocytopenia after vaccination were 1.25 [1.19 to 1.31] for ChAdOx1 and 0.99 (0.94 to 1.04) for BNT162b2. Rates of deep vein thrombosis and arterial thrombosis were similar among those vaccinated and the general population. ConclusionsChAdOx1 and BNT162b2 had broadly similar safety profiles. Thrombosis rates after either vaccine were mostly similar to those of the general population. Rates of pulmonary embolism increased 1.2-fold after either vaccine and 15-fold with SARS-CoV-2 infection. Thrombocytopenia was more common among recipients of ChAdOx1 but not of BNT162b2. Summary boxO_ST_ABSWhat is already known on this topicC_ST_ABSO_LISpontaneous reports of unusual and severe thrombosis with thrombocytopenia syndrome (TTS) raised concerns regarding the safety of adenovirus-based vaccines against SARS-CoV-2 C_LIO_LIIn a cohort study including over 280,000 people aged 18-65 years vaccinated with ChAdOx1 in Denmark and Norway, Potteg[a]rd et al reported increased rates of venous thromboembolic events as well as thrombocytopenia among vaccine recipients. C_LI What this study addsO_LIIn this cohort study, ChAdOx1 and BNT162b2 were seen to have broadly similar safety profiles. C_LIO_LIRates of thrombosis after either vaccine were generally similar to those of the general population. Rates of pulmonary embolism were though 1.2-fold higher than background rates after either vaccine, which compared to 15-fold higher after SARS-CoV-2 infection. C_LIO_LIThrombocytopenia was more common among recipients of ChAdOx1 but not of BNT162b2. C_LI
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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.
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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 3,316,107persons 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.
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IntroductionAngiotensin 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. MethodsUsing 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) users 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. ResultsFollowing 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. ConclusionThere 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.
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BackgroundSARS-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. MethodsWe 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. FindingsThe 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. InterpretationThe 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.
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ObjectiveTo develop and externally validate COVID-19 Estimated Risk (COVER) scores that quantify a patients risk of hospital admission (COVER-H), requiring intensive services (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis. MethodsWe analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries. We developed and validated 3 scores using 6,869,127 patients with a general practice, emergency room, or outpatient visit with diagnosed influenza or flu-like symptoms any time prior to 2020. The scores were validated on patients with confirmed or suspected COVID-19 diagnosis across five databases from South Korea, Spain and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death iii) death in the 30 days after index date. ResultsOverall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated 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: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration was overall acceptable. ConclusionsA 9-predictor model performs well for COVID-19 patients for predicting hospitalization, intensive services and fatality. 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.