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COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records.
Thygesen, Johan H; Tomlinson, Christopher; Hollings, Sam; Mizani, Mehrdad A; Handy, Alex; Akbari, Ashley; Banerjee, Amitava; Cooper, Jennifer; Lai, Alvina G; Li, Kezhi; Mateen, Bilal A; Sattar, Naveed; Sofat, Reecha; Torralbo, Ana; Wu, Honghan; Wood, Angela; Sterne, Jonathan A C; Pagel, Christina; Whiteley, William N; Sudlow, Cathie; Hemingway, Harry; Denaxas, Spiros.
  • Thygesen JH; Institute of Health Informatics, University College London, London, UK.
  • Tomlinson C; Institute of Health Informatics, University College London, London, UK; UK Research and Innovation Centre for Doctoral Training in AI-enabled Healthcare Systems, University College London, London, UK; University College London Hospitals Biomedical Research Centre, University College London, London,
  • Hollings S; NHS Digital, Leeds, UK.
  • Mizani MA; Institute of Health Informatics, University College London, London, UK.
  • Handy A; Institute of Health Informatics, University College London, London, UK.
  • Akbari A; Population Data Science, Swansea University, Swansea, UK.
  • Banerjee A; Institute of Health Informatics, University College London, London, UK.
  • Cooper J; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
  • Lai AG; Institute of Health Informatics, University College London, London, UK.
  • Li K; Institute of Health Informatics, University College London, London, UK; UK Research and Innovation Centre for Doctoral Training in AI-enabled Healthcare Systems, University College London, London, UK.
  • Mateen BA; Institute of Health Informatics, University College London, London, UK; The Wellcome Trust, London, UK.
  • Sattar N; Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
  • Sofat R; Institute of Health Informatics, University College London, London, UK; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK.
  • Torralbo A; Institute of Health Informatics, University College London, London, UK.
  • Wu H; Institute of Health Informatics, University College London, London, UK.
  • Wood A; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, and Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK.
  • Sterne JAC; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
  • Pagel C; Clinical Operational Research Unit, University College London, London, UK.
  • Whiteley WN; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Sudlow C; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK; Health Data Research UK, London, UK.
  • Hemingway H; Institute of Health Informatics, University College London, London, UK; University College London Hospitals Biomedical Research Centre, University College London, London, UK; Health Data Research UK, London, UK.
  • Denaxas S; Institute of Health Informatics, University College London, London, UK; British Heart Foundation Research Accelerator, University College London, London, UK; University College London Hospitals Biomedical Research Centre, University College London, London, UK; British Heart Foundation Data Science C
Lancet Digit Health ; 4(7): e542-e557, 2022 07.
Article in English | MEDLINE | ID: covidwho-1882680
ABSTRACT

BACKGROUND:

Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework.

METHODS:

In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status.

FINDINGS:

Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1.

INTERPRETATION:

Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources.

FUNDING:

British Heart Foundation Data Science Centre, led by Health Data Research UK.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Topics: Vaccines Limits: Humans Country/Region as subject: Europa Language: English Journal: Lancet Digit Health Year: 2022 Document Type: Article Affiliation country: S2589-7500(22)00091-7

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Topics: Vaccines Limits: Humans Country/Region as subject: Europa Language: English Journal: Lancet Digit Health Year: 2022 Document Type: Article Affiliation country: S2589-7500(22)00091-7