Evidence map›Paper›PMID 33770164›Full record

Observational studyAge and ageing2021

Predicting readmission and death after hospital discharge: a comparison of conventional frailty measurement with an electronic health record-based score.

Yong Yong Tew, Juen Hao Chan, Polly Keeling, Susan D Shenkin, Alasdair MacLullich, Nicholas L Mills, Martin A Denvir, Atul Anand

Open access · hybridAbstract readObservational Study
In one paragraph

Observational study in Age and ageing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 3 pooled it
1.9field-weighted citation impact, top 16% 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

20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Guideline
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Observational
  12. Review
  13. Article
  14. Article
  15. Article
  16. Development of an Electronic Frailty Index for Hospitalized Older Adults in Sweden.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022
    Article
  17. Article
  18. The future of frailty: Opportunity is knocking.Journal of the American Geriatrics Society · 2022
    Article
  19. 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 at 1 institution in 1 country.

Yong Yong TewBHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Juen Hao ChanUniversity of Edinburgh Medical School, Edinburgh, UK.
Polly KeelingUniversity of Edinburgh Medical School, Edinburgh, UK.
Susan D ShenkinGeriatric Medicine Research Group, University of Edinburgh, Edinburgh, UK.ORCID 0000-0001-7375-4776
Alasdair MacLullichGeriatric Medicine Research Group, University of Edinburgh, Edinburgh, UK.
Nicholas L MillsBHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Martin A DenvirBHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Atul AnandBHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.ORCID 0000-0002-6428-4554
University of Edinburgh · GB

Funding

British Heart Foundation CH/F/21/90010British Heart Foundation FS/16/14/32023British Heart Foundation RE/18/5/34216Chief Scientist Office PCL/18/05
6 · The paper itself

Abstract

backgroundfrailty measurement may identify patients at risk of decline after hospital discharge, but many measures require specialist review and/or additional testing.

objectiveto compare validated frailty tools with routine electronic health record (EHR) data at hospital discharge, for associations with readmission or death.

designobservational cohort study.

settinghospital ward. SUBJECTS: consented cardiology inpatients ≥70 years old within 24 hours of discharge.

methodspatients underwent Fried, Short Physical Performance Battery (SPPB), PRISMA-7 and Clinical Frailty Scale (CFS) assessments. An EHR risk score was derived from the proportion of 31 possible frailty markers present. Electronic follow-up was completed for a primary outcome of 90-day readmission or death. Secondary outcomes were mortality and days alive at home ('home time') at 12 months.

resultsin total, 186 patients were included (79 ± 6 years old, 64% males). The primary outcome occurred in 55 (30%) patients. Fried (hazard ratio [HR] 1.47 per standard deviation [SD] increase, 95% confidence interval [CI] 1.18-1.81, P < 0.001), CFS (HR 1.24 per SD increase, 95% CI 1.01-1.51, P = 0.04) and EHR risk scores (HR 1.35 per SD increase, 95% CI 1.02-1.78, P = 0.04) were independently associated with the primary outcome after adjustment for age, sex and co-morbidity, but the SPPB and PRISMA-7 were not. The EHR risk score was independently associated with mortality and home time at 12 months.

conclusionsfrailty measurement at hospital discharge identifies patients at risk of poorer outcomes. An EHR-based risk score appeared equivalent to validated frailty tools and may be automated to screen patients at scale, but this requires further validation.

Indexed as

FrailtyAgedAged, 80 and overElectronic Health RecordsFemaleFrail ElderlyHospitalsHumansMalePatient DischargePatient Readmissionco-morbidityfrailtyolder peoplerisk prediction

Identifiers

PMID33770164
PMCPMC8437069
OpenAlexW3139330095

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.