Evidence map›Paper›PMID 33588840›Full record

ArticleBMC health services research2021

Australian Injury Comorbidity Indices (AICIs) to predict burden and readmission among hospital-admitted injury patients.

Dasamal Tharanga Fernando, Janneke Berecki-Gisolf, Stuart Newstead, Zahid Ansari

Abstract read
In one paragraph

Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Dasamal Tharanga FernandoMonash University Accident Research Centre, Monash University, Clayton Campus, 21 Alliance Lane, Clayton, 3800, Victoria, Australia. tara.fernando@monash.edu.ORCID http://orcid.org/0000-0003-2423-3784
Janneke Berecki-GisolfMonash University Accident Research Centre, Monash University, Clayton Campus, 21 Alliance Lane, Clayton, 3800, Victoria, Australia.
Stuart NewsteadMonash University Accident Research Centre, Monash University, Clayton Campus, 21 Alliance Lane, Clayton, 3800, Victoria, Australia.
Zahid AnsariVictorian Agency for Health Information, 50 Lonsdale Street, Melbourne, Victoria, 3000, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExisting comorbidity measures predict mortality among general patient populations. Due to the lack of outcome specific and patient-group specific measures, the existing indices are also applied to non-mortality outcomes in injury epidemiology. This study derived indices to capture the association between comorbidity, and burden and readmission outcomes for injury populations.

methodsInjury-related hospital admissions data from July 2012 to June 2014 (161,334 patients) for the state of Victoria, Australia were analyzed. Various multivariable regression models were run and results used to derive both binary and weighted indices that quantify the association between comorbidities and length of stay (LOS), hospital costs and readmissions. The new and existing indices were validated internally among patient subgroups, and externally using data from the states of New South Wales and Western Australia.

resultsTwenty-four comorbidities were significantly associated with overnight stay, twenty-seven with LOS, twenty-eight with costs, ten with all-cause and eleven with non-planned 30-day readmissions. The number of and types of comorbidities, and their relative impact were different to the associations established with the existing Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Measure (ECM). The new indices performed equally well to the long-listed ECM and in certain instances outperformed the CCI.

conclusionsThe more parsimonious, up to date, outcome and patient-specific indices presented in this study are better suited for use in present injury epidemiology. Their use can be trialed by hospital administrations in resource allocation models and patient classification models in clinical settings.

Indexed as

ComorbidityPatient ReadmissionAdolescentAdultAgedAged, 80 and overChildChild, PreschoolFemaleHospitalsHumansInfantInfant, NewbornLength of StayMaleMiddle AgedBurdenComorbidityCostIndexInjuryReadmission

Identifiers

PMID33588840
PMCPMC7885207

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