Bookmarks

BHF Data Science Centre
Datasets (4)
Secure Data Environment for CVD-COVID-UK / COVID-IMPACT (England)
Dataset population size: 57,000,000
Health and disease, Registry, Treatments/Interventions, Measurements/Tests, Socioeconomic, Lifestyle, Environment and energy
Trusted Research Environment for CVD-COVID-UK (Wales)
Dataset population size: 3,200,000
Health and disease, Registry, Treatments/Interventions, Measurements/Tests, Socioeconomic, Lifestyle, Environment and energy
Trusted Research Environment for CVD-COVID-UK (Scotland)
Dataset population size: 5,500,000
Health and disease, Registry, Treatments/Interventions, Measurements/Tests, Socioeconomic, Lifestyle, Environment and energy
Trusted Research Environment for CVD-COVID-UK (Wales + Census)
Dataset population size: 3,200,000
Health and disease, Registry, Treatments/Interventions, Measurements/Tests, Socioeconomic, Lifestyle, Environment and energy
Collections (1)
Analysis Scripts & Software (1)
Data Uses (87)
CCU018: COVID infection during pregnancy on cardiovascular disease and related risk factors
University of Cambridge
CCU014: Assessing the impact of COVID-19 on clinical pathways using a medicines approach
University of Liverpool
CCU013: High-throughput electronic health record phenotyping approaches
University College London
CCU023: Repurposing medicines used to treat CVD risk to prevent COVID-19
University of Liverpool
Publications (46)
COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records.
Thygesen JH, Tomlinson C, Hollings S, Mizani MA, Handy A, Akbari A, Banerjee A, Cooper J, Lai AG, Li K, Mateen BA, Sattar N, Sofat R, Torralbo A, Wu H, Wood A, Sterne JAC, Pagel C, Whiteley WN, Sudlow C, Hemingway H, Denaxas S, Longitudinal Health and Wellbeing COVID-19 National Core Study and the CVD-COVID-UK/COVID-IMPACT Consortium.
Evaluation of antithrombotic use and COVID-19 outcomes in a nationwide atrial fibrillation cohort.
Handy A, Banerjee A, Wood AM, Dale C, Sudlow CLM, Tomlinson C, Bean D, Thygesen JH, Mizani MA, Katsoulis M, Takhar R, Hollings S, Denaxas S, Walker V, Dobson R, Sofat R, CVD-COVID-UK Consortium.
A retrospective cohort study predicting and validating impact of the COVID-19 pandemic in individuals with chronic kidney disease.
Dashtban A, Mizani MA, Denaxas S, Nitsch D, Quint J, Corbett R, Mamza JB, Morris T, Mamas M, Lawlor DA, Khunti K, CVD-COVID-UK Consortium, Sudlow C, Hemingway H, Banerjee A.
Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19-a data-driven retrospective cohort study.
Mizani MA, Dashtban A, Pasea L, Lai AG, Thygesen J, Tomlinson C, Handy A, Mamza JB, Morris T, Khalid S, Zaccardi F, Macleod MJ, Torabi F, Canoy D, Akbari A, Berry C, Bolton T, Nolan J, Khunti K, Denaxas S, Hemingway H, Sudlow C, Banerjee A, CVD-COVID-UK Consortium.
Risk of myocarditis and pericarditis following BNT162b2 and ChAdOx1 COVID-19 vaccinations
Ip S, Torabi F, Denaxas S, Akbari A, Abbasizanjani H, Knight R, Cooper J, Denholm R, Keene S, Bolton T, Hollings S, Omigie E, North T, Suseeladevi AK, Angelantonio ED, Khunti K, Sterne JAC, Sudlow C, Whiteley W, Wood A, Walker V.
A nationwide deep learning pipeline to predict stroke and COVID-19 death in atrial fibrillation
Handy A, Wood A, Sudlow C, Tomlinson C, Kee F, Thygesen JH, Mamouei M, Sofat R, Dobson R, Ip S, Denaxas S.