Evidence map›Paper›PMID 29546362›Full record

ArticleAge and ageing2018

Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study.

Daniel Stow, Fiona E Matthews, Stephen Barclay, Steve Iliffe, Andrew Clegg, Sarah De Biase, Louise Robinson, Barbara Hanratty

Registry-linked trialAbstract read
In one paragraph

Article in Age and ageing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06965972 (Prospective Cohort for the Study of Phenotypic Clusters, Progression Paths and Outcomes of Frailty and Dependence), which is not on this map. Cited by 29 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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.

NCT06965972 recruitingnot on this mapstarted 2022, after this paper: background citation

Prospective Cohort for the Study of Phenotypic Clusters, Progression Paths and Outcomes of Frailty and Dependence: The Spanish CohorFES

TypeobservationalSponsorParc de Salut MarRan2022 to 2035Enrolled1,500ConditionsFrailty Syndrome
3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  9. Review
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  12. Change in a Claims-Based Frailty Index, Mortality, and Health Care Costs in Medicare Beneficiaries.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023
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  13. Article
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  16. Review
  17. Frailty Changes Predict Mortality in 4 Longitudinal Studies of Aging.The journals of gerontology. Series A, Biological sciences and medical sciences · 2021
    Article
  18. Article
  19. Changes in a Frailty Index and Association with Mortality.Journal of the American Geriatrics Society · 2021
    Article
  20. 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

8 authors.

Daniel StowInstitute of Health and Society, Newcastle University, Newcastle upon Tyne.
Fiona E MatthewsInstitute of Health and Society, Newcastle University, Newcastle upon Tyne.
Stephen BarclayDepartment of Public Health and Primary Care, University of Cambridge, Cambridge.
Steve IliffeResearch Department of Primary Care and Population Health, University College London, London.
Andrew CleggAcademic Unit of Elderly Care & Rehabilitation, University of Leeds, Bradford.
Sarah De BiaseHealthy Ageing Collaborative Bradford Institute for Health Research, Temple Bank House, Bradford Royal Infirmary, Duckworth Lane, Bradford.
Louise RobinsonInstitute of Health and Society, Newcastle University, Newcastle upon Tyne.
Barbara HanrattyInstitute of Health and Society, Newcastle University, Newcastle upon Tyne.

Funding

Department of Health NIHR-RP-011-043Medical Research Council MC_U105292687
6 · The paper itself

Abstract

backgroundrecognising that a patient is nearing the end of life is essential, to enable professional carers to discuss prognosis and preferences for end of life care.

objectiveinvestigate whether an electronic frailty index (eFI) generated from routinely collected data, can be used to predict mortality at an individual level.

designhistorical prospective case control study.

settingUK primary care electronic health records. SUBJECTS: 13,149 individuals age 75 and over who died between 01/01/2015 and 01/01/2016, 1:1 matched by age and sex to individuals with no record of death in the same time period.

methodstwo subsamples were randomly selected to enable development and validation of the association between eFI 3 months prior to death and mortality. Receiver operator characteristic (ROC) analyses were used to examine diagnostic accuracy of eFI at 3 months prior to death.

resultsan eFI > 0.19 predicted mortality in the development sample at 75% sensitivity and 69% area under received operating curve (AUC). In the validation dataset this cut point gave 76% sensitivity, 53% specificity.

conclusionsthe eFI measured at a single time point has low predictive value for individual risk of death, even 3 months prior to death. Although the eFI is a strong predictor or mortality at a population level, its use for individuals is far less clear.

Indexed as

AgingElectronic Health RecordsAgedAged, 80 and overAge FactorsCase-Control StudiesFemaleFrailtyGeriatric AssessmentHumansMalePredictive Value of TestsPrognosisProspective StudiesReproducibility of ResultsRisk Assessmentend of life carefrailtyolder peoplepalliative careprimary care

Identifiers

PMID29546362
PMCPMC6014267

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LicenceCC BY-NC
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Registered trials

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.