Evidence map›Paper›PMID 33082154›Full record

ArticleBMJ (Clinical research ed.)2020

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.

Ash K Clift, Carol A C Coupland, Ruth H Keogh, Karla Diaz-Ordaz, Elizabeth Williamson, Ewen M Harrison, Andrew Hayward, Harry Hemingway, Peter Horby, Nisha Mehta and 13 more

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

Article in BMJ (Clinical research ed.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 322 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
322citing papers in PubMed, 8 pooled it
43.3field-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

322 citing papers in PubMed, 8 syntheses or guidelines pooled it, 608 citations in OpenAlex.

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  7. The association between stroke and COVID-19-related mortality: a systematic review and meta-analysis based on adjusted effect estimates.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022
    Pooled it
  8. Mendelian Randomization Analysis Reveals No Causal Relationship Between Nonalcoholic Fatty Liver Disease and Severe COVID-19.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2022
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262 more citing papers are in PubMed but not listed here.

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 15 institutions in 1 country.

Ash K CliftNuffield Department of Primary Care Health Sciences, Radcliffe Observatory Quarter, Oxford OX2 6GG, UK.
Carol A C CouplandDivision of Primary Care, School of Medicine, University of Nottingham, Nottingham, UK.
Ruth H KeoghFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Karla Diaz-OrdazFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Elizabeth WilliamsonFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Ewen M HarrisonUsher Institute, University of Edinburgh, Edinburgh, UK.
Andrew HaywardUCL Institute of Epidemiology and Health Care, University College London, London, UK.
Harry HemingwayUCL Institute for Health Informatics, University College London, London, UK.
Peter HorbyCentre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
Nisha MehtaOffice of the Chief Medical Officer, Department of Health and Social Care, London, UK.
Jonathan BengerNHS Digital, Leeds, UK.
Kamlesh KhuntiDiabetes Research Centre, University of Leicester, Leicester, UK.
David SpiegelhalterWinton Centre for Risk and Evidence Communication, Faculty of Mathematics, University of Cambridge, Cambridge, UK.
Aziz SheikhUsher Institute, University of Edinburgh, Edinburgh, UK.
Jonathan ValabhjiNHS England, London, UK.
Ronan A LyonsSwansea University, Swansea, UK.
John RobsonCentre for Primary Care and Public Health, Queen Mary University of London, London, UK.
Malcolm G SempleInstitute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
Frank KeeCentre for Public Health, Queen's University Belfast, Belfast, UK.
Peter JohnsonNHS England, London, UK.
Susan JebbNuffield Department of Primary Care Health Sciences, Radcliffe Observatory Quarter, Oxford OX2 6GG, UK.
Tony WilliamsAssociation of Local Authority Medical Advisors, London, UK.
Julia Hippisley-CoxNuffield Department of Primary Care Health Sciences, Radcliffe Observatory Quarter, Oxford OX2 6GG, UK julia.hippisley-cox@phc.ox.ac.uk.ORCID 0000-0002-2479-7283
London School of Hygiene & Tropical Medicine · GBNHS EnglandUniversity College London · GBUniversity of Edinburgh · GBUniversity of Oxford · GBDepartment of Health and Social Care · GBLocal Government Association · GBNHS Digital · GBQueen Mary University of London · GBQueen's University Belfast · GBSwansea University · GBUniversity of Cambridge · GBUniversity of Leicester · GBUniversity of Liverpool · GBUniversity of Nottingham · GB

Funding

Department of Health 05/40/04Department of Health RP-PG-0407-10314Medical Research Council G0902393Medical Research Council MC_PC_13041Medical Research Council MC_PC_19004Medical Research Council MC_PC_19075Medical Research Council MC_PC_20029Medical Research Council MR/K006584/1Medical Research Council MR/M501633/2Medical Research Council MR/S017968/1
6 · The paper itself

Abstract

objectiveTo derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.

designPopulation based cohort study. SETTING AND

participantsQResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020.

main outcome measuresThe primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period.

results4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R

conclusionThe QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.

Indexed as

AlgorithmsClinical Decision RulesCoronavirus InfectionsMortalityPandemicsPneumonia, ViralRisk AssessmentAdultAged, 80 and overBetacoronavirusCohort StudiesCOVID-19Databases, FactualEnglandFemaleHospitalization

Identifiers

PMID33082154
PMCPMC7574532
OpenAlexW3094542430

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.