Journal of Clinical Epidemiology
Volume 57, Issue 12 , Pages 1288-1294 , December 2004

New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality

  • Vijaya Sundararajan

      Affiliations

    • Victorian Department of Human Services, Level 18, 120 Spencer Street, Melbourne, 3000 Victoria, Australia
    • Corresponding Author InformationCorresponding author. Tel.: +61 03 9637 4997; fax: +61 03 9637 4763.
  • ,
  • Toni Henderson

      Affiliations

    • Victorian Department of Human Services, Level 18, 120 Spencer Street, Melbourne, 3000 Victoria, Australia
  • ,
  • Catherine Perry

      Affiliations

    • Victorian Department of Human Services, Level 18, 120 Spencer Street, Melbourne, 3000 Victoria, Australia
  • ,
  • Amanda Muggivan

      Affiliations

    • Victorian Department of Human Services, Level 18, 120 Spencer Street, Melbourne, 3000 Victoria, Australia
  • ,
  • Hude Quan

      Affiliations

    • Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
  • ,
  • William A. Ghali

      Affiliations

    • Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
    • Department of Medicine, University of Calgary, Calgary, Alberta, Canada

,Accepted 8 March 2004.

References 

  1. 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;40:373–383
  2. Gabriel SE, Crowson CS, O'Fallon WM. A comparison of two comorbidity instruments in arthritis. J Clin Epidemiol. 1999;52:1137–1142
  3. Zhang JX, Iwashyna TJ, Christakis NA. The performance of different lookback periods and sources of information for Charlson comorbidity adjustment in Medicare claims. Med Care. 1999;37:1128–1139
  4. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613–619
  5. Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1993;46:1075–1079discussion 1081–90
  6. D'Hoore W, Sicotte C, Tilquin C. Risk adjustment in outcome assessment: the Charlson comorbidity index. Methods Inf Med. 1993;32:382–387
  7. World Health Organization . International statistical classification of disease and related health problems, Tenth Revision (ICD-10). Geneva: World Health Organization; 1992;
  8. Commission on Professional and Hospital Activities . Annotated ICD-9-CM international classification of diseases, 9th revision, clinical modification. Ann Arbor, MI: Edwards Brothers; 1986;
  9. Anonymous . Implementation of the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10). Epidemiol Bull. 1997;18(1):1–4
  10. de Lusignan S, Minmagh C, Kennedy J, Zeimet M, Bommezijn H, Bryant J. A survey to identify the clinical coding and classification systems currently in use across Europe. Medinfo. 2001;10:86–89
  11. Brouch K. Where in the world is ICD-10?. J AHIMA. 2000;71(8):52–57
  12. Innes KC, Peasley K, Roberts R. Ten down under: implementing ICD-10 in Australia. J AHIMA. 2000;71(1):52–56
  13. Pickett D, Berglund D, Blum A, Wing L. A quick review of ICD-10-CM. J AHIMA. 1999;70(9):99–100
  14. Canadian Institute of Health Information . Final report: the Canadian enhancement of ICD-10 (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision). Ottawa: Canadian Institute of Health Information; 2001;[Available at http://secure.cihi.ca/cihiweb/en/downloads/codingclass_icd10enhan_e.pdf]
  15. Australian Bureau of Statistics . 2001 Census of Population and Housing. Canberra: Australian Bureau of Statistics; 2001;
  16. Division AH. The Victorian Admitted Episodes Dataset: an overview April 2000. Melbourne: Acute Health Division, Victorian Government Department of Human Services; 2000;
  17. 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;49:273–278
  18. Cleves MA, Sanchez N, Draheim M. Evaluation of two competing methods for calculating Charlson's comorbidity index when analyzing short-term mortality using administrative data. J Clin Epidemiol. 1997;50:903–908
  19. National Centre for Classification in Health. The International Statistical Classification of Diseases and Related Health Problems, 10th revision, Australian modification. ICD-10-AM Australian Coding Standards. 1st ed. 1 July 1998. Sydney: Faculty of Health Sciences, University of Sydney; 1998.
  20. National Centre for Classification in Health . The International Statistical Classification of Diseases and Related Health Problems, 10th revision, Australian modification (ICD-10-AM). Volume 5. ICD-10-AM Australian coding standards. 2nd ed.. Sydney: Faculty of Health Sciences, University of Sydney; 2000;1 July 2000
  21. National Centre for Classification in Health . The International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification (ICD-10-AM). Volume 1. ICD-10-AM Tabular list of diseases. 3rd ed.. Sydney: Faculty of Health Sciences, University of Sydney; 2002;1 July 2002
  22. Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed.. New York: John Wiley; 2000;
  23. SAS Institute . SAS user's guide. Version 8.2 ed.. Cary, NC: SAS Institute; 1999;
  24. Andresen EM, Bowley N, Rothenberg BM, Panzer R, Katz P. Test-retest performance of a mailed version of the Medical Outcomes Study 36-Item Short-Form Health Survey among older adults. Med Care. 1996;34:1165–1170
  25. Angus DC, Musthafa AA, Clermont G, Griffin MF, Linde-Zwirble WT, Dremsizov TT, et al. Quality-adjusted survival in the first year after the acute respiratory distress syndrome. Am J Respir Crit Care Med. 2001;163:1389–1394
  26. Desai MM, Bogardus ST, Williams CS, Vitagliano G, Inouye SK. Development and validation of a risk-adjustment index for older patients: the high-risk diagnoses for the elderly scale. J Am Geriatr Soc. 2002;50:474–481
  27. Dodds TA, Martin DP, Stolov WC, Deyo RA. A validation of the functional independence measurement and its performance among rehabilitation inpatients. Arch Phys Med Rehabil. 1993;74:531–536
  28. Grasso ME, Weller WE, Shaffer TJ, Diette GB, Anderson GF. Capitation, managed care, and chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 1998;158:133–138
  29. Melfi C, Holleman E, Arthur D, Katz B. Selecting a patient characteristics index for the prediction of medical outcomes using administrative claims data. J Clin Epidemiol. 1995;48:917–926
  30. Parker JP, McCombs JS, Graddy EA. Can pharmacy data improve prediction of hospital outcomes? Comparisons with a diagnosis-based comorbidity measure. Med Care. 2003;41:407–419
  31. Romano PS, Roos LL, Luft HS, Jollis JG, Doliszny K. Ischemic Heart Disease Patient Outcomes Research Team. A comparison of administrative versus clinical data: coronary artery bypass surgery as an example. J Clin Epidemiol. 1994;47:249–260
  32. Roos LL, Walld RK, Romano PS, Roberecki S. Short-term mortality after repair of hip fracture: Do Manitoba elderly do worse?. Med Care. 1996;34:310–326

PII: S0895-4356(04)00164-7

doi: 10.1016/j.jclinepi.2004.03.012

Journal of Clinical Epidemiology
Volume 57, Issue 12 , Pages 1288-1294 , December 2004