Evidence map›Paper›PMID 31615508›Full record

ArticleBMC health services research2019

Comorbidity and thirty-day hospital readmission odds in chronic obstructive pulmonary disease: a comparison of the Charlson and Elixhauser comorbidity indices.

Russell G Buhr, Nicholas J Jackson, Gerald F Kominski, Steven M Dubinett, Michael K Ong, Carol M Mangione

Open access · goldAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
4.0field-weighted citation impact, top 5% 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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Russell G BuhrDivision of Pulmonary and Critical Care Medicine, David Geffen School of Medicine, University of California, 1100 Glendon Ave, Suite 850, Los Angeles, CA, 90024, USA. rbuhr@mednet.ucla.edu.ORCID http://orcid.org/0000-0001-6159-7435
Nicholas J JacksonDepartment of Medicine Statistics Core, University of California, Los Angeles, CA, USA.
Gerald F KominskiDepartment of Health Policy and Management, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA, USA.
Steven M DubinettDivision of Pulmonary and Critical Care Medicine, David Geffen School of Medicine, University of California, 1100 Glendon Ave, Suite 850, Los Angeles, CA, 90024, USA.
Michael K OngDepartment of Health Policy and Management, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA, USA.
Carol M MangioneDepartment of Health Policy and Management, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA, USA.
University of California, Los Angeles · US

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
The Center for Health Improvement of Minority ElderlyP30AG021684 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Homero Erwin del Pino · 2002 to 2026
$19.7M
NRSA Training CoreTL1TR001883 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ALEX BUI, Nina Thawata Harawa · 2016 to 2026
$8.6M
National Heart, Lung, and Blood Institute (US) L30HL134025NCATS NIH HHS TL1 TR001883NCATS NIH HHS TL1TR001883-01NCATS NIH HHS UL1TR001881NHLBI NIH HHS L30 HL134025NIA NIH HHS P30AG021684
6 · The paper itself

Abstract

backgroundReadmissions following exacerbations of chronic obstructive pulmonary disease (COPD) are prevalent and costly. Multimorbidity is common in COPD and understanding how comorbidity influences readmission risk will enable health systems to manage these complex patients.

objectivesWe compared two commonly used comorbidity indices published by Charlson and Elixhauser regarding their ability to estimate readmission odds in COPD and determine which one provided a superior model.

methodsWe analyzed discharge records for COPD from the Nationwide Readmissions Database spanning 2010 to 2016. Inclusion and readmission criteria from the Hospital Readmissions Reduction Program were utilized. Elixhauser and Charlson Comorbidity Index scores were calculated from published methodology. A mixed-effects logistic regression model with random intercepts for hospital clusters was fit for each comorbidity index, including year, patient-level, and hospital-level covariates to estimate odds of thirty-day readmissions. Sensitivity analyses included testing age inclusion thresholds and model stability across time.

resultsIn analysis of 1.6 million COPD discharges, readmission odds increased by 9% for each half standard deviation increase of Charlson Index scores and 13% per half standard deviation increase of Elixhauser Index scores. Model fit was slightly better for the Elixhauser Index using information criteria. Model parameters were stable in our sensitivity analyses.

conclusionsBoth comorbidity indices provide meaningful information in prediction readmission odds in COPD with slightly better model fit in the Elixhauser model. Incorporation of comorbidity information into risk prediction models and hospital discharge planning may be informative to mitigate readmissions.

Indexed as

Severity of Illness IndexAgedComorbidityFemaleHospitalsHumansLogistic ModelsMaleMiddle AgedPatient DischargePatient ReadmissionPrevalencePulmonary Disease, Chronic ObstructiveRetrospective StudiesCharlson comorbidity indexCOPDElixhauser comorbidity indexHospital readmissionNationwide readmissions database

Identifiers

PMID31615508
PMCPMC6794890
OpenAlexW2980692967

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LicenceCC BY
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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.