Evidence map›Paper›PMID 35690576›Full record

ArticleThe Lancet. Digital health2022

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records.

Johan H Thygesen, Christopher Tomlinson, Sam Hollings, Mehrdad A Mizani, Alex Handy, Ashley Akbari, Amitava Banerjee, Jennifer Cooper, Alvina G Lai, Kezhi Li and 13 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
8.5field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.

  1. Pooled it
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  7. Review
  8. From test to rest: evaluating socioeconomic differences along the COVID-19 care pathway in the Netherlands.The European journal of health economics : HEPAC : health economics in prevention and care · 2024
    Observational
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  14. Complex patterns of multimorbidity associated with severe COVID-19 and Long COVID.medRxiv : the preprint server for health sciences · 2024
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors at 12 institutions in 1 country.

Johan H ThygesenInstitute of Health Informatics, University College London, London, UK.
Christopher TomlinsonInstitute 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, UK.
Sam HollingsNHS Digital, Leeds, UK.
Mehrdad A MizaniInstitute of Health Informatics, University College London, London, UK.
Alex HandyInstitute of Health Informatics, University College London, London, UK.
Ashley AkbariPopulation Data Science, Swansea University, Swansea, UK.
Amitava BanerjeeInstitute of Health Informatics, University College London, London, UK.
Jennifer CooperPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Alvina G LaiInstitute of Health Informatics, University College London, London, UK.
Kezhi LiInstitute 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.
Bilal A MateenInstitute of Health Informatics, University College London, London, UK; The Wellcome Trust, London, UK.
Naveed SattarInstitute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
Reecha SofatInstitute 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.
Ana TorralboInstitute of Health Informatics, University College London, London, UK.
Honghan WuInstitute of Health Informatics, University College London, London, UK.
Angela WoodBritish Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, and Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK.
Jonathan A C SternePopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Christina PagelClinical Operational Research Unit, University College London, London, UK.
William N WhiteleyCentre 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.
Cathie SudlowCentre 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.
Harry HemingwayInstitute 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.
Spiros DenaxasInstitute 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 Centre, Health Data Research UK, London, UK; Health Data Research UK, London, UK. Electronic address: s.denaxas@ucl.ac.uk.
Longitudinal Health and Wellbeing COVID-19 National Core Study and the CVD-COVID-UK/COVID-IMPACT Consortium
University College London · GBUniversity of Bristol · GBBritish Heart Foundation · GBUniversity of Oxford · GBUniversity of Cambridge · GBHealth Data Research UK · GBNational Institute for Health Research · GBNHS Digital · GBSwansea University · GBUniversity of Glasgow · GBUniversity College Hospital · GBUniversity of Liverpool · GB

Funding

British Heart Foundation AA/18/6/24223British Heart Foundation CH/17/1/32804British Heart Foundation FS/11/38/28864British Heart Foundation FS/14/76/30933British Heart Foundation FS/18/5/33319British Heart Foundation RG/13/13/30194British Heart Foundation RG/18/13/33946British Heart Foundation SP/19/3/34678Medical Research Council MC_PC_18029Medical Research Council MC_PC_19005Medical Research Council MC_PC_20030Medical Research Council MC_PC_20051Medical Research Council MC_PC_20058Medical Research Council MC_PC_20059Medical Research Council MC_UU_00004/08Medical Research Council MC_UU_00019/1Medical Research Council MC_UU_00019/2Medical Research Council MC_UU_00019/4Medical Research Council MR/K006584/1Medical Research Council MR/L003120/1Medical Research Council MR/L018942/1Medical Research Council MR/S004149/1Medical Research Council MR/S004149/2Medical Research Council MR/T021780/1Wellcome Trust 204841/Z/16/Z
6 · The paper itself

Abstract

backgroundUpdatable 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.

methodsIn 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.

findingsAmong 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.

interpretationOur 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.

fundingBritish Heart Foundation Data Science Centre, led by Health Data Research UK.

Indexed as

COVID-19Cohort StudiesCOVID-19 TestingElectronic Health RecordsEnglandHumansSARS-CoV-2State Medicine

Identifiers

PMID35690576
PMCPMC9179175
OpenAlexW4282027168

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.