Elixhauser Comorbidity Software, Version 3.7 ICD-9-CM codes were frozen in preparation for ICD-10-CM/PCS implementation and regular maintenance of the codes has been suspended. The HCUP Tools for ICD-9-CM should only be used with data for discharges before 10/1/2015. For data containing discharges after 10/1/2015, the Elixhauser Comorbidity Software for ICD-10-CM should be used.
The Elixhauser Comorbidity Software is one in a family of databases and software tools developed as part of the Healthcare Cost and Utilization Project (HCUP), a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality. HCUP databases, tools, and software inform decision making at the national, State, and community levels. Contents:
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Elixhauser Comorbidity software assigns variables that identify comorbidities in hospital discharge records using the diagnosis coding of ICD-9-CM (International Classification of Diseases, Ninth Edition, Clinical Modifications). This document describes the software that creates the comorbidity measures reported by Elixhauser et al. ("Comorbidity Measures for Use with Administrative Data." Medical Care, 1998;36:8-27).
In addition, this page includes information on the new Index, which allows users to create an index score—a single numeric value—that describes the comorbidity burden and that may be more useful for multivariate analyses, especially with relatively small sample sizes ("Identifying Increased Risk of Readmission and In-Hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index." ![]() | ||||||||||||||||||||
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In 2015, a team of HCUP researchers and statisticians used a large analysis file built from all-payer hospital administrative data from the HCUP State Inpatient Databases (SID) from 18 States in 2011 and 2012 to create two indices based on the Elixhauser Comorbidity measures designed to predict in-hospital mortality and 30-day readmission in administrative data.
Currently, the indices are only available for use with ICD-9-CM data. Once administrative data on hospital admissions coded with ICD-10-CM diagnosis codes becomes available, we can begin to re-evaluate the current indices. In longitudinal studies that include data from prior to October 1, 2015, researchers will still be reliant on ICD-9-CM based comorbidity algorithms. Select to download software. The comorbidity software consists of three SAS computer programs for PCs. Although these programs are written in SAS, they are being distributed in ASCII so that they can be readily adapted to other programming languages. The first program, Creation of Format Library for Elixhauser Comorbidity Groups, creates a SAS format library that maps diagnosis codes into comorbidity indicators. Additional formats are also created to exclude conditions that may be complications or that may be related to the principal diagnosis:
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The input data file must contain certain elements that are coded in specific ways. These elements are required for the assignment of the comorbidity flags. The flags are 0/1 indicators that note whether individual records include each comorbidity.
The input data set must have the following two variables:
Required Elements of Input File
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Creation of Format Library for Elixhauser Comorbidity Groups
The format program defines a format library that contains the diagnosis and DRG/MS-DRG screens necessary for the comorbidity analysis. The format library is referenced by the comorbidity analysis program.
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Creation of Elixhauser Comorbidity Variables
The analysis program assigns 0/1 indicators to the inpatient records for the comorbidity variables of interest. This program assumes that the input data file conforms to specific variables names, attributes, and coding conventions, as described above. There is one version of this program that works with either DRG or MS-DRG.
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Creation of Elixhauser Comorbidity Index Scores
The index program assigns two index scores to the inpatient records, one for readmissions and one for in-hospital mortality. The index program can be used to transform the current 29 HCUP comorbidities variables into comorbidity index scores for each record. The comorbidity index scores for each observation are calculated as a weighted sum of each of the binary comorbidity variables on the record. The resulting comorbidity index scores can be used in analyses in place of the 29 individual measures. This program assumes that the input data file includes 29 binary comorbidity variables with specific variables names.
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ICD-9-CM and DRG/MS-DRG coding changes through September 30, 2015, are incorporated into the Elixhauser Comorbidity Software. (Note that there were no new diagnosis codes for FY 2015.)
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The library contains formats for the ICD-9-CM codes and DRG/MS-DRG screens. Construction of these variables is summarized in Table 2. (PDF file, 120 KB)
The original table appeared in the paper by Elixhauser et al (1998). This table has been updated to reflect the ICD-9-CM and DRG/MS-DRG updates in the software. The analysis program creates these 29 Elixhauser comorbidity measures: CHF, VALVE, PULMCIRC, PERIVASC, HTN_C (using HTN, HTNCX), PARA, NEURO, CHRNLUNG, DM, DMCX, HYPOTHY, RENLFAIL, LIVER, ULCER, AIDS, LYMPH, METS, TUMOR, ARTH, COAG, OBESE, WGHTLOSS, LYTES, BLDLOSS, ANEMDEF, ALCOHOL, DRUG, PSYCH, DEPRESS. The index program creates these two Elixhauser comorbidity index variables: READMIT_SCORE and MORTAL_SCORE. | ||||||||||||||||||||
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Copies of previous versions of the Elixhauser Comorbidity Software are available for users who need to replace or access the old SAS programs.
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The following publications are examples of the many studies that have used this comorbidity algorithm:
Ahern,M. M., Hendryx,M. Avoidable hospitalizations for diabetes: comorbidity risks. Disease management: DM, 10(6):347-355 , December 2007. Austin SR, Wong YN, 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3818341/ Baldwin LM, Klabunde CN, Green P, Barlow W, Wright G. In search of the perfect comorbidity measure for use with administrative claims data: does it exist? Med Care. 2006 Aug;44(8):745-53. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3124350/ Bass E, French DD, Bradham DD, Rubenstein LZ. Risk-adjusted mortality rates of elderly veterans with hip fractures. Ann Epidemiol. 2007 Jul;17(7):514-9. Epub 2007 Apr 8. Brasel KJ, Guse CE, Layde P, Weigelt JA. Rib fractures: relationship with pneumonia and mortality. Crit Care Med. 2006 Jun;34(6):1642-6. Carney CP, Jones L, Woolson RF. Medical comorbidity in women and men with schizophrenia: a population-based controlled study. J Gen Intern Med. 2006 Nov;21(11):1133-7. Cots F, Castells X, Mercade L, Torre P, Riu M. [Article in Spanish]. [Ajuste del riesgo: mas alla de los sistemas de clasificacion de pacientes]. Gac Sanit. 2001 Sep-Oct;15(5):423-31. Dominick KL, Dudley TK, Coffman CJ, Bosworth HB. Comparison of three comorbidity measures for predicting health service use in patients with osteoarthritis. Arthritis Rheum. 2005 Oct 15;53(5):666-72. Farley JF, Harley CR, Devine JW. A comparison of comorbidity measurements to predict healthcare expenditures. Am J Manag Care. 2006 Feb;12(2):110-9. French DD, Campbell R, Spehar A, Angaran DM. Benzodiazepines and injury: a risk adjusted model. Pharmacoepidemiol Drug Saf. 2005 Jan;14(1):17-24. http://onlinelibrary.wiley.com/doi/10.1002/pds.967/abstract ![]() French DD, Campbell R, Spehar A, Rubenstein LZ, Accomando J, Cunningham F. National Veterans Health Administration hospitalizations for syncope compared to acute myocardial infarction, fracture, or pneumonia in community-dwelling elders: outpatient medication and comorbidity profiles. J Clin Pharmacol. 2006 Jun;46(6):613-9. French DD, Bass E, Bradham DD, Campbell RR, Rubenstein LZ. Rehospitalization After Hip Fracture: Predictors and Prognosis from a National Veterans Study. J Am Geriatr Soc, November 15, 2007 [Epub ahead of print] French DD, Bass E, Bradham DD, Campbell RR, Rubenstein LZ. Rehospitalization After Hip Fracture: Predictors and Prognosis from a National Veterans Study. J Am Geriatr Soc, November 15, 2007 [Epub ahead of print] French DD, Bass E, Bradham DD, Campbell RR, Rubenstein LZ. Rehospitalization After Hip Fracture: Predictors and Prognosis from a National Veterans Study. J Am Geriatr Soc, November 15, 2007 2008 Apr;56(4):705-10. Epub 2007 Nov 15. Garvin JH, Redd A, Bolton D, Graham P, Roche D, Groeneveld P, Leecaster M, Shen S, Weiner MG. Exploration of ICD-9-CM coding of chronic diseases within the Elixhauser Comorbidity Measure in patients with chronic heart failure. Perspect Health Inf Manag 2013;10:1b. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797549/ Ghali WA, Hall RE, Rosen AK, Ash AS, Moskowitz, MA. Searching for an improved clinical comorbidity index for use with ICD-9-CM administrative data. J Clin Epidemiol 1996;49(3):273–8. https://www.ncbi.nlm.nih.gov/pubmed/8676173 Glance LG, Dick AW, Osler TM, Mukamel DB. Does date stamping ICD-9-CM codes increase the value of clinical information in administrative data? Health Serv Res. 2006 Feb;41(1):231-51. Ho KM, Finn J, Knuiman M, Webb SA. Combining multiple comorbidities with Acute Physiology Score to predict hospital mortality of critically ill patients: a linked data cohort study. Anaesthesia. 2007 Nov;62(11):1095-100. Johnston JA, Wagner DP, Timmons S, Welsh D, Tsevat J, Render ML. Impact of different measures of comorbid disease on predicted mortality of intensive care unit patients. Med Care. 2002 Oct;40(10):929-40. Kurichi JE, Stineman MG, Kwong PL, Bates BE, Reker DM. Assessing and using comorbidity measures in elderly veterans with lower extremity amputations. Gerontology. 2007;53(5):255-9. Epub 2007 Apr 13. https://www.karger.com/Article/Pdf/101703 ![]() Li B, Evans D, Faris P, Dean S, Quan H. Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases. BMC Health Services Research, 8:12, 2008. Livingston EH, Rege RV. Technical complications are rising as common duct exploration is becoming rare. J Am Coll Surg. 2005 Sep; 201(3):426-33. http://linkinghub.elsevier.com/retrieve/pii/S1072-7515(05)00531-4 Livingston EH. Development of bariatric surgery-specific risk assessment tool. Surg Obes Relat Dis. 2007 Jan-Feb;3(1):14-20; discussion 20. Epub 2006 Dec 27. Mitchell, Jean M. Effects Of Physician-Owned Limited-Service Hospitals: Evidence From Arizona. (Link removed because it no longer worked. Replacement not found.) 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 Jul; 55(7):698-705. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005 Nov; 43(11):1073-1077. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=16224307&query_hl=7 Robinson,D.,Jr, Eisenberg,D., Nietert,P. J., Doyle,M., Bala,M., Paramore,C., Fraeman,K., Renahan,K. Systemic sclerosis prevalence and comorbidities in the US, 2001-2002. Curr Med Res Opin, 24(4):1157-66, April 2008. Schneeweiss S, Maclure M. Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol 2000;29(5):891–8. https://www.ncbi.nlm.nih.gov/pubmed/11034974 Sharabiani MT, Aylin P, Bottle A. Systematic review of comorbidity indices for administrative data. Med Care 2012;50(12):1109–18. https://www.ncbi.nlm.nih.gov/pubmed/22929993 Southern DA, Quan H, Ghali WA. Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care. 2004 Apr;42(4):355-60. Stukenborg GJ, Wagner DP, Connors AF Jr. Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations. Med Care. 2001 Jul;39(7):727-39. Tang,J., Wan,J. Y., Bailey,J. E. Performance of comorbidity measures to predict stroke and death in a community-dwelling, hypertensive Medicaid population. Stroke, 39(7):1938-44, July 2008, Epub 2008 Apr 24. http://stroke.ahajournals.org/content/strokeaha/39/7/1938.full.pdf ![]() Thombs BD, Singh VA, Halonen J, Diallo A, Milner SM. The effects of preexisting medical comorbidities on mortality and length of hospital stay in acute burn injury: evidence from a national sample of 31,338 adult patients. Ann Surg. 2007 Apr;245(4):629-34. Thompson NR, Fan Y, Dalton JE, Jehi L, Rosenbaum BP, Vadera S, Griffith SD. A new Elixhauser-based comorbidity summary measure to predict in-hospital mortality. Med Care 2015;53(4):374–9. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4812819/ van Walraven C, Austin PC, Jenings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Medical Care. 2009 (47):626-633. http://www.ncbi.nlm.nih.gov/pubmed/19433995 Weinhandl,E. D., Snyder,J. J., Israni,A. K., Kasiske,B. L. Effect of comorbidity adjustment on CMS criteria for kidney transplant center performance. American journal of transplantation, 9(3):506-16, March 2009. Werner RM, Asch DA, Polsky D. Racial Profiling: The Unintended Consequences of Coronary Artery Bypass Graft Report Cards. Circulation, 111:1257-1263, 2005; Xiao H, Tan F, Goovaerts P, Ali A, Adunlin G, Huang Y, Gwede C. Construction of a comorbidity index for prostate cancer patients linking state cancer registry with inpatient and outpatient data. J Registry Manage. 2013 Winter; 40(4):159-64. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4337841/ Yan Y, Birman-Deych E, Radford MJ, et al. Comorbidity indices to predict mortality from Medicare data: results from the National Registry of Atrial Fibrillation. Med Care. 2005 Nov; 43(11):1073-1077. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=16224299&query_hl=5 Zhu,H., Hill,M. D. Stroke: the Elixhauser Index for comorbidity adjustment of in-hospital case fatality. Neurology, 22;71(4):283-7, July 2008. | ||||||||||||||||||||
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Internet Citation: HCUP Elixhauser Comorbidity Software. Healthcare Cost and Utilization Project (HCUP). June 2017. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp. |
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