CVD-COVID-UK/COVID-IMPACT Consortium

👨‍⚕️ Contributing clinical experience, data wrangling and analytical skills to development of COVID-19 Severity phenotypes from linked EHR data for 57 million individuals in England.

I lead my own project CCU038: Evaluating the impact of COVID-19 on critical care outcomes.

👨‍💻 Direct contributions:

• CCU013: High-throughput electronic health record phenotyping approaches (Lead: Spiros Denaxas, UCL)
  • COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records
• CCU020: Evaluation of antithrombotic use and COVID-19 outcomes (Lead: Alex Handy, UCL)
  • Evaluation of antithrombotic use and COVID-19 outcomes in a nationwide atrial fibrillation cohort
• CCU004: COVID and cardiovascular disease risk prediction. (Project lead: Angela Wood, University of Cambridge)
  • A nationwide deep learning pipeline to predict stroke and COVID-19 death in atrial fibrillation

👨‍💼 Collaborative work:

• CCU002: SARS-CoV-2 infection and vaccination and the risk of vascular events.(Project lead: William Whiteley, University of Edinburgh)
  • Association of COVID-19 with arterial and venous vascular diseases: a population-wide cohort study of 48 million adults in England and Wales
• CCU003: Direct and indirect effects of the coronavirus pandemic in individuals with cardiovascular disease. (Project lead: Ami Banerjee, UCL)
• CCU014: Assessing the impact of COVlD-19 on clinical pathways using a medicines approach. (Project lead: Reecha Sofat, University College London)
  • The adverse impact of COVID-19 pandemic on cardiovascular disease prevention and management in England, Scotland and Wales: A population-scale descriptive analysis of trends in medication data
• CCU019: Identification and personalised risk prediction for severe COVID-19 in patients with rare disorders impacting cardiovascular health. (Project lead: Honghan Wu, University College London)
• CCU029: Child hospital admission with COVID-19 — risk factors, risk groups and NHS care utilisation. (Project lead: Kate Brown, Great Ormond Street Hospital & University College London)
• CCU036: The impact of previous exposure to COVlD-19 and the safety of COVID-19 vaccination for fertility and pregnancy outcomes. (Project lead: Luisa Zuccolo, University of Bristol)
• CCU037: Improving methods to minimise bias in ethnicity data for more representative and generalisable models, using CVD in COVID-19 as an example. (Project lead: Sara Khalid, University of Oxford)
• CCU049: Healthcare utilisation in individuals with Long Covid. (Project lead: Ami Banerjee, UCL)

🏥 Datasets:

• Hospital Episode Statistics Admitted Patient Care (APC) and Adult Critical Care (CC)
• GPES data for Pandemic Planning and Research (GDPPR)
• Second Generation Surveillance System (SGSS)
• ONS deaths

👨‍💻 Work is conducted in the NHS Digital Trusted Research Environment using SQL, Python, R and Spark on Databricks.