Evidence map›Paper›PMID 29706364›Full record

Observational studyLancet (London, England)2018

Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study.

Thomas Gilbert, Jenny Neuburger, Joshua Kraindler, Eilis Keeble, Paul Smith, Cono Ariti, Sandeepa Arora, Andrew Street, Stuart Parker, Helen C Roberts and 2 more

Registry-linked trialAbstract readObservational StudyValidation Study
In one paragraph

Observational study in Lancet (London, England), 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 809 papers.

0numbers the graph read from it
0cells of the map it votes in
809citing papers in PubMed
–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

809 citing papers in PubMed.

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  5. Long-term incidence of infective endocarditis according to the type of cardiac implantable electronic device: a nationwide comparison with moderate- and high-risk patient groups.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026
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  7. Inequalities in oral anticoagulant treatment patterns: a two-part model to estimate healthcare costs associated with atrial fibrillation.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
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  18. In-hospital cardiac arrest in a Singaporean tertiary hospital: Epidemiology, outcomes, and potential suboptimal end-of-life planning.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2026
    Article
  19. Review
  20. Article

749 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Thomas GilbertDepartment of Geriatric Medicine, Lyon Teaching Hospital, Lyon, France.
Jenny NeuburgerThe Nuffield Trust, London, UK.
Joshua KraindlerThe Nuffield Trust, London, UK.
Eilis KeebleThe Nuffield Trust, London, UK.
Paul SmithDepartment of Business Intelligence, Manchester University NHS Foundation Trust, Manchester, UK.
Cono AritiDepartment of Public Health, Cardiff University, Cardiff, UK.
Sandeepa AroraThe Nuffield Trust, London, UK.
Andrew StreetDepartment of Health Policy, London School of Economics, London, UK.
Stuart ParkerInstitute for Ageing, Newcastle University, Newcastle, UK.
Helen C RobertsAcademic Geriatric Medicine, University of Southampton, Southampton, UK.
Martin BardsleyData Analytics Team, The Health Foundation, London, UK.
Simon ConroyDepartment of Health Sciences, College of Life Sciences, University of Leicester, Leicester, UK. Electronic address: spc3@le.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOlder people are increasing users of health care globally. We aimed to establish whether older people with characteristics of frailty and who are at risk of adverse health-care outcomes could be identified using routinely collected data.

methodsA three-step approach was used to develop and validate a Hospital Frailty Risk Score from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes. First, we carried out a cluster analysis to identify a group of older people (≥75 years) admitted to hospital who had high resource use and diagnoses associated with frailty. Second, we created a Hospital Frailty Risk Score based on ICD-10 codes that characterised this group. Third, in separate cohorts, we tested how well the score predicted adverse outcomes and whether it identified similar groups as other frailty tools.

findingsIn the development cohort (n=22 139), older people with frailty diagnoses formed a distinct group and had higher non-elective hospital use (33·6 bed-days over 2 years compared with 23·0 bed-days for the group with the next highest number of bed-days). In the national validation cohort (n=1 013 590), compared with the 429 762 (42·4%) patients with the lowest risk scores, the 202 718 (20·0%) patients with the highest Hospital Frailty Risk Scores had increased odds of 30-day mortality (odds ratio 1·71, 95% CI 1·68-1·75), long hospital stay (6·03, 5·92-6·10), and 30-day readmission (1·48, 1·46-1·50). The c statistics (ie, model discrimination) between individuals for these three outcomes were 0·60, 0·68, and 0·56, respectively. The Hospital Frailty Risk Score showed fair overlap with dichotomised Fried and Rockwood scales (kappa scores 0·22, 95% CI 0·15-0·30 and 0·30, 0·22-0·38, respectively) and moderate agreement with the Rockwood Frailty Index (Pearson's correlation coefficient 0·41, 95% CI 0·38-0·47).

interpretationThe Hospital Frailty Risk Score provides hospitals and health systems with a low-cost, systematic way to screen for frailty and identify a group of patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful.

fundingNational Institute for Health Research.

Indexed as

Outcome Assessment, Health CareAgedAged, 80 and overDatabases, FactualElectronic Health RecordsFemaleFrail ElderlyGeriatric AssessmentHospitalizationHumansMalePredictive Value of TestsRetrospective StudiesRisk Factors

Identifiers

PMID29706364
PMCPMC5946808

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.