Evidence map›Paper›PMID 38773946›Full record

ArticleAge and ageing2024

Predictors of mortality shortly after entering a long-term care facility.

Robert N Jorissen, Steve L Wesselingh, Craig Whitehead, John Maddison, John Forward, Alice Bourke, Gillian Harvey, Maria Crotty, STAAR-SA Study Collaborators, Maria C Inacio

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Age and ageing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Robert N JorissenRegistry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
Steve L WesselinghSouth Australian Health and Medical Research Institute, Adelaide, SA, Australia; and National Health and Medical Research Council, ACT, Australia.
Craig WhiteheadSouthern Adelaide Local Health Network, SA Health, Adelaide, SA, Australia.
John MaddisonNorthern Adelaide Local Health Network, SA Health, Adelaide, SA, Australia.
John ForwardNorthern Adelaide Local Health Network, SA Health, Adelaide, SA, Australia.
Alice BourkeCentral Adelaide Local Health Network, SA Health, Adelaide, SA, Australia.
Gillian HarveyCollege of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia.
Maria CrottySouthern Adelaide Local Health Network, SA Health, Adelaide, SA, Australia.
STAAR-SA Study Collaborators
Maria C InacioRegistry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, SA, Australia.ORCID 0000-0001-8261-2665

Funding

Australian Government's Medical Research Future FundNational Health and Medical Research Council APP119378Rapid Applied Research Translation ProgramThe Hospital Research Foundation Mid-Career Fellowship MCF-27-2019
6 · The paper itself

Abstract

objectiveMoving into a long-term care facility (LTCF) requires substantial personal, societal and financial investment. Identifying those at high risk of short-term mortality after LTCF entry can help with care planning and risk factor management. This study aimed to: (i) examine individual-, facility-, medication-, system- and healthcare-related predictors for 90-day mortality at entry into an LTCF and (ii) create risk profiles for this outcome.

designRetrospective cohort study using data from the Registry of Senior Australians. SUBJECTS: Individuals aged ≥ 65 years old with first-time permanent entry into an LTCF in three Australian states between 01 January 2013 and 31 December 2016.

methodsA prediction model for 90-day mortality was developed using Cox regression with the purposeful variable selection approach. Individual-, medication-, system- and healthcare-related factors known at entry into an LTCF were examined as predictors. Harrell's C-index assessed the predictive ability of our risk models.

results116,192 individuals who entered 1,967 facilities, of which 9.4% (N = 10,910) died within 90 days, were studied. We identified 51 predictors of mortality, five of which were effect modifiers. The strongest predictors included activities of daily living category (hazard ratio [HR] = 5.41, 95% confidence interval [CI] = 4.99-5.88 for high vs low), high level of complex health conditions (HR = 1.67, 95% CI = 1.58-1.77 for high vs low), several medication classes and male sex (HR = 1.59, 95% CI = 1.53-1.65). The model out-of-sample Harrell's C-index was 0.773.

conclusionsOur mortality prediction model, which includes several strongly associated factors, can moderately well identify individuals at high risk of mortality upon LTCF entry.

Indexed as

Long-Term CareActivities of Daily LivingAgedAged, 80 and overAustraliaFemaleHomes for the AgedHumansMaleNursing HomesProportional Hazards ModelsRegistriesRetrospective StudiesRisk AssessmentRisk FactorsTime Factorslong-term caremortalitynursing homesolder peoplepredictors

Identifiers

PMID38773946
PMCPMC11109518

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LicenceCC BY-NC-ND
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Registered trials

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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.