Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Health Services Research
Published in: BMC Health Services Research 1/2019

Open Access 01-12-2019 | Chronic Obstructive Lung Disease | Research article

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

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

Published in: BMC Health Services Research | Issue 1/2019

Login to get access

Abstract

Background

Readmissions 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.

Objectives

We 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.

Methods

We 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.

Results

In 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.

Conclusions

Both 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.
Appendix
Available only for authorised users
Literature
1.
2.
Mannino DM, Homa DM, Akinbami LJ, Ford ES, Redd SC. Chronic obstructive pulmonary disease surveillance--United States, 1971-2000. MMWR Surveill Summ. 2002;51(6):1–16.PubMed
3.
Murphy SL, Xu JQ, Kochanek KD, Arias E. Mortality in the United States, 2017. In: Centers for Disease Control and Prevention. Hyattsville: National Center for Health Statistics; 2018.
4.
Guarascio AJ, Ray SM, Finch CK, Self TH. The clinical and economic burden of chronic obstructive pulmonary disease in the USA. Clinicoecon Outcomes Res. 2013;5:235–45.PubMedPubMedCentral
5.
Press VG, Konetzka RT, White SR. Insights about the economic impact of chronic obstructive pulmonary disease readmissions post implementation of the hospital readmission reduction program. Curr Opin Pulm Med. 2018;24(2):138–46.CrossRefPubMedPubMedCentral
6.
7.
Kahnert K, Alter P, Young D, Lucke T, Heinrich J, Huber RM, et al. The revised GOLD 2017 COPD categorization in relation to comorbidities. Respir Med. 2018;134:79–85.CrossRefPubMed
8.
9.
Zewari S, Hadi L, van den Elshout F, Dekhuijzen R, Heijdra Y, Vos P. Obesity in COPD: comorbidities with practical consequences? COPD. 2018;15(5):464–71.CrossRefPubMed
10.
Gudmundsson G, Gislason T, Lindberg E, Hallin R, Ulrik CS, Brondum E, et al. Mortality in COPD patients discharged from hospital: the role of treatment and co-morbidity. Respir Res. 2006;7:109.CrossRefPubMedPubMedCentral
11.
Roberts CM, Stone RA, Lowe D, Pursey NA, Buckingham RJ. Co-morbidities and 90-day outcomes in hospitalized COPD exacerbations. COPD. 2011;8(5):354–61.CrossRefPubMed
12.
Prudente R, Franco EAT, Mesquita CB, Ferrari R, de Godoy I, Tanni SE. Predictors of mortality in patients with COPD after 9 years. Int J Chron Obstruct Pulmon Dis. 2018;13:3389–98.CrossRefPubMedPubMedCentral
13.
Jeong SH, Lee H, Carriere KC, Shin SH, Moon SM, Jeong BH, et al. Comorbidity as a contributor to frequent severe acute exacerbation in COPD patients. Int J Chron Obstruct Pulmon Dis. 2016;11:1857–65.CrossRefPubMedPubMedCentral
14.
Westney G, Foreman MG, Xu J, Henriques King M, Flenaugh E, Rust G. Impact of comorbidities among Medicaid enrollees with chronic obstructive pulmonary disease, United States, 2009. Prev Chronic Dis. 2017;14:E31.CrossRefPubMedPubMedCentral
15.
Thompson MP, Kaplan CM, Cao Y, Bazzoli GJ, Waters TM. Reliability of 30-day readmission measures used in the hospital readmission reduction program. Health Serv Res. 2016;51(6):2095–114.CrossRefPubMedPubMedCentral
16.
Austin SR, Wong Y-N, Uzzo RG, Beck JR, Egleston BL. Why summary comorbidity measures such as the Charlson comorbidity index and Elixhauser score work. Med Care. 2015;53(9):e65–72.CrossRefPubMedPubMedCentral
17.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.CrossRefPubMed
18.
Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–51.CrossRefPubMed
19.
Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–9.CrossRefPubMed
20.
Spece LJ, Epler EM, Donovan LM, Griffith MF, Collins MP, Feemster LC, et al. Role of comorbidities in treatment and outcomes after chronic obstructive pulmonary disease exacerbations. Ann Am Thor Soc. 2018;15(9):1033–8.CrossRef
21.
Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27.CrossRefPubMed
22.
van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009;47(6):626–33.CrossRefPubMed
23.
Thompson NR, Fan Y, Dalton JE, Jehi L, Rosenbaum BP, Vadera S, et al. A new Elixhauser-based comorbidity summary measure to predict in-hospital mortality. Med Care. 2015;53(4):374–9.CrossRefPubMedPubMedCentral
24.
Moore BJ, White S, Washington R, Coenen N, Elixhauser A. Identifying increased risk of readmission and in-hospital mortality using hospital administrative data: the AHRQ Elixhauser comorbidity index. Med Care. 2017;55(7):698–705.CrossRefPubMed
25.
McFerrin C, Raza SJ, May A, Davaro F, Siddiqui S, Hamilton Z. Charlson comorbidity score is associated with readmission to the index operative hospital after radical cystectomy and correlates with 90-day mortality risk. Int Urol Nephrol. 2019.
26.
Voskuijl T, Hageman M, Ring D. Higher Charlson comorbidity index scores are associated with readmission after orthopaedic surgery. Clin Orthop Relat Res. 2014;472(5):1638–44.CrossRefPubMed
27.
Mehta HB, Dimou F, Adhikari D, Tamirisa NP, Sieloff E, Williams TP, et al. Comparison of comorbidity scores in predicting surgical outcomes. Med Care. 2016;54(2):180–7.CrossRefPubMedPubMedCentral
28.
Sprah L, Dernovsek MZ, Wahlbeck K, Haaramo P. Psychiatric readmissions and their association with physical comorbidity: a systematic literature review. BMC Psychiatry. 2017;17(1):2.CrossRefPubMedPubMedCentral
29.
Arora S, Lahewala S, Hassan Virk HU, Setareh-Shenas S, Patel P, Kumar V, et al. Etiologies, trends, and predictors of 30-day readmissions in patients with diastolic heart failure. Am J Cardiol. 2017;120(4):616–24.CrossRefPubMed
30.
Gadre SK, Shah M, Mireles-Cabodevila E, Patel B, Duggal A. Epidemiology and predictors of 30-day readmission in patients with sepsis. Chest. 2019;155(3):483–90.CrossRefPubMed
31.
Dhakal B, Giri S, Levin A, Rein L, Fenske TS, Chhabra S, et al. Factors associated with unplanned 30-day readmissions after hematopoietic cell transplantation among US hospitals. JAMA Netw Open. 2019;2(7):e196476.CrossRefPubMed
32.
Austin PC, Stanbrook MB, Anderson GM, Newman A, Gershon AS. Comparative ability of comorbidity classification methods for administrative data to predict outcomes in patients with chronic obstructive pulmonary disease. Ann Epidemiol. 2012;22(12):881–7.CrossRefPubMedPubMedCentral
33.
34.
35.
Yale New Haven Health Services Corporation/Center for Outcomes Research & Evaluation. 2016 condition-specific measures updates and specifications report hospital-level 30-day risk-standardized readmission measures. Baltimore: Centers for Medicare & Medicaid Services; 2016.
36.
Yale New Haven Health Services Corporation/Center for Outcomes Research & Evaluation. 2017 condition-specific measures updates and specifications report hospital-level 30-day risk-standardized readmission measures. Baltimore: Centers for Medicare & Medicaid Services; 2017.
37.
Stagg V. CHARLSON: Stata module to calculate Charlson index of comorbidity. Boston College Department of Economics: Statistical Software Components; 2006.
38.
Stagg V. ELIXHAUSER: Stata module to calculate Elixhauser index of comorbidity. Boston College Department of Economics: Statistical Software Components; 2015.
39.
Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol. 2004;57(12):1288–94.CrossRefPubMed
40.
41.
Bozdogan H. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika. 1987;52(3):345–70.CrossRef
42.
Vrieze SI. Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol Methods. 2012;17(2):228–43.CrossRefPubMedPubMedCentral
43.
Bernheim SM, Parzynski CS, Horwitz L, Lin Z, Araas MJ, Ross JS, et al. Accounting for Patients' socioeconomic status does not change hospital readmission rates. Health Aff (Millwood). 2016;35(8):1461–70.CrossRef
44.
Nagasako EM, Reidhead M, Waterman B, Claiborne DW. Adding socioeconomic data to hospital readmissions calculations May produce more useful results. Health Aff (Millwood). 2014;33(5):786–91.CrossRef
45.
Fuller RL, Atkinson G, Hughes JS. Indications of biased risk adjustment in the hospital readmission reduction program. J Ambul Care Manage. 2015;38(1):39–47.CrossRefPubMed
46.
Sjoding MW, Cooke CR. Readmission penalties for chronic obstructive pulmonary disease will further stress hospitals caring for vulnerable patient populations. Amer J Resp Crit Care Med. 2014;190(9):1072–4.CrossRef
47.
Prieto-Centurion V, Gussin HA, Rolle AJ, Krishnan JA. Chronic obstructive pulmonary disease readmissions at minority-serving institutions. Ann Am Thor Soc. 2013;10(6):680–4.CrossRef
48.
Caracciolo C, Parker D, Marshall E, Brown J. Excess readmission vs excess penalties: maximum readmission penalties as a function of socioeconomics and geography. J Hosp Med. 2017;12(8):610–7.CrossRefPubMed
49.
50.
Steinwald B, Dummit LA. Hospital case-mix change: sicker patients or DRG creep? Health Aff (Millwood). 1989;8(2):35–47.CrossRef
51.
Tsugawa Y, Figueroa JF, Papanicolas I, Orav EJ, Jha AK. Assessment of Strategies for Managing Expansion of Diagnosis Coding Using Risk-Adjustment Methods for Medicare. JAMA Int Med. 2019;179(9):1287-90.
52.
Metadata
Title
Comorbidity and thirty-day hospital readmission odds in chronic obstructive pulmonary disease: a comparison of the Charlson and Elixhauser comorbidity indices
Authors
Russell G. Buhr
Nicholas J. Jackson
Gerald F. Kominski
Steven M. Dubinett
Michael K. Ong
Carol M. Mangione
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2019
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-019-4549-4

Other articles of this Issue 1/2019

BMC Health Services Research 1/2019 Go to the issue

Research article

Better cardiac care: health professional’s perspectives of the barriers and enablers of health communication and education with patients of Aboriginal and Torres Strait Islander descent

Research article

Implementation and evaluation of a rural community-based pediatric hearing screening program integrating in-person and tele-diagnostic auditory brainstem response (ABR)

Research article

Policy analysis of the Iranian Health Transformation Plan in primary healthcare

Research article

Exploring adolescents and young people’s candidacy for utilising health services in a rural district, South Africa

Research article

Level of Partograph completion and healthcare workers’ perspectives on its use in Mulago National Referral and teaching hospital, Kampala, Uganda

Research article

Using concept mapping to inform the development of a transitional reintegration intervention program for formerly incarcerated people with HIV