Evidence map›Paper›PMID 37391265›Full record

ArticleThe Lancet. Digital health2023

Patient emergency health-care use before hospital admission for COVID-19 and long-term outcomes in Scotland: a national cohort study.

Annemarie B Docherty, James Farrell, Mathew Thorpe, Conor Egan, Sarah Dunn, Lisa Norman, Catherine A Shaw, Andrew Law, Gary Leeming, Lucy Norris and 13 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in The Lancet. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 31% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors at 4 institutions in 1 country.

Annemarie B DochertyCentre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK. Electronic address: annemarie.docherty@ed.ac.uk.
James FarrellCentre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
Mathew ThorpeCentre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
Conor EganCentre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
Sarah DunnCentre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
Lisa NormanCentre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
Catherine A ShawCentre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
Andrew LawRoslin Institute, University of Edinburgh, Edinburgh, UK.
Gary LeemingDepartment of Biostatistics, University of Liverpool, Liverpool, UK.
Lucy NorrisBayes Centre, University of Edinburgh, Edinburgh, UK.
Andrew BrooksBayes Centre, University of Edinburgh, Edinburgh, UK.
Bianca ProdanBayes Centre, University of Edinburgh, Edinburgh, UK.
Ruairidh MacLeodBayes Centre, University of Edinburgh, Edinburgh, UK.
Robert BaxterBayes Centre, University of Edinburgh, Edinburgh, UK.
Carole MorrisPublic Health Scotland, Edinburgh, UK.
Diane RenniePublic Health Scotland, Edinburgh, UK.
Wilna OosthuyzenRoslin Institute, University of Edinburgh, Edinburgh, UK.
Malcolm G SempleNIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
J Kenneth BaillieRoslin Institute, University of Edinburgh, Edinburgh, UK.
Riinu PiusCentre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
Sohan SethSchool of Informatics, University of Edinburgh, Edinburgh, UK.
Ewen M HarrisonCentre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
Nazir I LoneCentre for Population Health Sciences, Usher Institute, University of Edinburgh, Edinburgh, UK.
University of Edinburgh · GBPublic Health Scotland · GBHealth Protection Research Unit in Emerging and Zoonotic Infections at University of Liverpool · GBUniversity of Liverpool · GB

Funding

Chief Scientist OfficeMedical Research Council MC_PC_19025Medical Research Council MC_PC_20004Medical Research Council MC_PC_20029
6 · The paper itself

Abstract

backgroundIt is unclear what effect the pattern of health-care use before admission to hospital with COVID-19 (index admission) has on the long-term outcomes for patients. We sought to describe mortality and emergency readmission to hospital after discharge following the index admission (index discharge), and to assess associations between these outcomes and patterns of health-care use before such admissions.

methodsWe did a national, retrospective, complete cohort study by extracting data from several national databases and linking the databases for all adult patients admitted to hospital in Scotland with COVID-19. We used latent class trajectory modelling to identify distinct clusters of patients on the basis of their emergency admissions to hospital in the 2 years before the index admission. The primary outcomes were mortality and emergency readmission up to 1 year after index admission. We used multivariable regression models to explore associations between these outcomes and patient demographics, vaccination status, level of care received in hospital, and previous emergency hospital use.

findingsBetween March 1, 2020, and Oct 25, 2021, 33 580 patients were admitted to hospital with COVID-19 in Scotland. Overall, the Kaplan-Meier estimate of mortality within 1 year of index admission was 29·6% (95% CI 29·1-30·2). The cumulative incidence of emergency hospital readmission within 30 days of index discharge was 14·4% (95% CI 14·0-14·8), with the number increasing to 35·6% (34·9-36·3) patients at 1 year. Among the 33 580 patients, we identified four distinct patterns of previous emergency hospital use: no admissions (n=18 772 [55·9%]); minimal admissions (n=12 057 [35·9%]); recently high admissions (n=1931 [5·8%]), and persistently high admissions (n=820 [2·4%]). Patients with recently or persistently high admissions were older, more multimorbid, and more likely to have hospital-acquired COVID-19 than patients with no or minimal admissions. People in the minimal, recently high, and persistently high admissions groups had an increased risk of mortality and hospital readmission compared with those in the no admissions group. Compared with the no admissions group, mortality was highest in the recently high admissions group (post-hospital mortality HR 2·70 [95% CI 2·35-2·81]; p<0·0001) and the risk of readmission was highest in the persistently high admissions group (3·23 [2·89-3·61]; p<0·0001).

interpretationLong-term mortality and readmission rates for patients hospitalised with COVID-19 were high; within 1 year, one in three patients had died and a third had been readmitted as an emergency. Patterns of hospital use before index admission were strongly predictive of mortality and readmission risk, independent of age, pre-existing comorbidities, and COVID-19 vaccination status. This increasingly precise identification of individuals at high risk of poor outcomes from COVID-19 will enable targeted support.

fundingChief Scientist Office Scotland, UK National Institute for Health Research, and UK Research and Innovation.

Indexed as

COVID-19COVID-19 VaccinesAdultCohort StudiesHospitalsHumansRetrospective StudiesCOVID-19 Vaccines

Identifiers

PMID37391265
PMCPMC10306342
OpenAlexW4382359238

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.