Evidence map›Paper›PMID 36691139›Full record

ArticleBMJ open2022

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study.

Martin Wiegand, Sarah L Cowan, Claire S Waddington, David J Halsall, Victoria L Keevil, Brian D M Tom, Vince Taylor, Effrossyni Gkrania-Klotsas, Jacobus Preller, Robert J B Goudie

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 49% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

10 authors at 3 institutions in 1 country.

Martin WiegandFaculty of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK Martin.Wiegand@mrc-bsu.cam.ac.uk.ORCID 0000-0003-0276-658X
Sarah L CowanCambridge University Hospitals NHS Foundation Trust, Cambridge, UK.ORCID 0000-0001-9149-3530
Claire S WaddingtonDepartment of Medicine, University of Cambridge, Cambridge, UK.ORCID 0000-0002-4120-6027
David J HalsallCambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Victoria L KeevilDepartment of Medicine, University of Cambridge, Cambridge, UK.ORCID 0000-0001-6148-0640
Brian D M TomMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0002-3335-9322
Vince TaylorCancer Research UK, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Effrossyni Gkrania-KlotsasDepartment of Infectious Diseases, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.ORCID 0000-0002-0930-8330
Jacobus PrellerCambridge University Hospitals NHS Foundation Trust, Cambridge, UK.ORCID 0000-0001-5706-816X
Robert J B GoudieMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0001-9554-1499
University of Cambridge · GBCambridge University Hospitals NHS Foundation Trust · GBCancer Research UK · GB

Funding

Cancer Research UKDepartment of Health BRC-1215-20014Medical Research Council G0701652Medical Research Council MC_UU_00002/2Medical Research Council MR/T023902/1
6 · The paper itself

Abstract

objectivesTo develop a disease stratification model for COVID-19 that updates according to changes in a patient's condition while in hospital to facilitate patient management and resource allocation.

designIn this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.

settingAll data used in this study were obtained from a single UK teaching hospital.

participantsWe developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation.

resultsOur final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88).

conclusionsOur model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient's clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool.

trial registrationThe study is registered as 'researchregistry5464' on the Research Registry (www.researchregistry.com).

Indexed as

COVID-19Hospital MortalityHospitals, TeachingHumansRetrospective StudiesRisk AssessmentUnited KingdomCOVID-19epidemiologyrisk managementstatistics & research methods

Identifiers

PMID36691139
PMCPMC9445230
OpenAlexW4294637052

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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.