Evidence map›Paper›PMID 33619733›Full record

ArticleJournal of the American Geriatrics Society2021

Frailty and comorbidity in predicting community COVID-19 mortality in the U.K. Biobank: The effect of sampling.

Jonathan K L Mak, Ralf Kuja-Halkola, Yunzhang Wang, Sara Hägg, Juulia Jylhävä

Open access · hybridAbstract read
In one paragraph

Article in Journal of the American Geriatrics Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 4 pooled it
5.5field-weighted citation impact, top 3% 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

35 citing papers in PubMed, 4 syntheses or guidelines pooled it, 59 citations in OpenAlex.

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  9. Machine learning prediction of survival in centenarians after age 100: a retrospective, population-based cohort study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
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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

5 authors at 1 institution in 1 country.

Jonathan K L MakDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0003-4454-8580
Ralf Kuja-HalkolaDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Yunzhang WangDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Sara HäggDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Juulia JylhäväDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0003-0250-4491
Karolinska Institutet · SE

Funding

Karolinska Institutet FoundationsLoo & Hans Osterman FoundationStrategic Research Program in Epidemiology at Karolinska InstitutetVetenskapsrådet 2018-02077Vetenskapsrådet 2019-01272
6 · The paper itself

Abstract

BACKGROUND/

objectivesFrailty has been linked to increased risk of COVID-19 mortality, but evidence is mainly limited to hospitalized older individuals. This study aimed to assess and compare predictive abilities of different frailty and comorbidity measures for COVID-19 mortality in a community sample and COVID-19 inpatients.

designPopulation-based cohort study.

settingCommunity.

participantsWe analyzed (i) the full sample of 410,199 U.K. Biobank participants in England, aged 49-86 years, and (ii) a subsample of 2812 COVID-19 inpatients with COVID-19 data from March 1 to November 30, 2020. MEASUREMENTS: Frailty was defined using the physical frailty phenotype (PFP), frailty index (FI), and Hospital Frailty Risk Score (HFRS), and comorbidity using the Charlson Comorbidity Index (CCI). PFP and FI were available at baseline, whereas HFRS and CCI were assessed both at baseline and concurrently with the start of the pandemic. Inpatient COVID-19 cases were confirmed by PCR and/or hospital records. COVID-19 mortality was ascertained from death registers.

resultsOverall, 514 individuals died of COVID-19. In the full sample, all frailty and comorbidity measures were associated with higher COVID-19 mortality risk after adjusting for age and sex. However, the associations were stronger for the concurrent versus baseline HFRS and CCI, with odds ratios of 20.40 (95% confidence interval = 16.24-25.63) comparing high (>15) to low (<5) concurrent HFRS risk category and 1.53 (1.48-1.59) per point increase in concurrent CCI. Moreover, only the concurrent HFRS or CCI significantly improved predictive ability of a model including age and sex, yielding areas under the receiver operating characteristic curve (AUC) >0.8. When restricting analyses to COVID-19 inpatients, similar improvement in AUC was not observed.

conclusionHFRS and CCI constructed from medical records concurrent with the start of the pandemic can be used in COVID-19 mortality risk stratification at the population level, but they show limited added value in COVID-19 inpatients.

Indexed as

ComorbidityCOVID-19Predictive Value of TestsAgedAged, 80 and overBiological Specimen BanksCohort StudiesEnglandFemaleFrailtyGeriatric AssessmentHumansIndependent LivingInpatientsMaleMiddle AgedagingcomorbidityCOVID-19frailtymortality

Identifiers

PMID33619733
PMCPMC8013405
OpenAlexW3129941202

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.