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Methods Inf Med 2000; 39(01): 7-11
DOI: 10.1055/s-0038-1634260
Original Article
Schattauer GmbH

Utility of the Charlson Comorbidity Index Computed from Routinely Collected Hospital Discharge Diagnosis Codes

R. L. O’Connell
1   Centre for Clinical Epidemiology and Biostatistics, The University of Newcastle, Australia
,
L. L.-Y. Lim
1   Centre for Clinical Epidemiology and Biostatistics, The University of Newcastle, Australia
› Author Affiliations
Further Information

Publication History

Publication Date:
07 February 2018 (online)

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Abstract:

This study aims to determine whether the Charlson comorbidity index computed from ICD-9-CM discharge diagnosis codes adds additional information to a model containing adjustment for more informed patient details (e.g., disease severity and history), besides solely age and sex, when predicting long-term survival. We conducted a retrospective cohort study of patients admitted to hospital for suspected acute myocardial infarction. Index scores were calculated by applying the D’Hoore et al. algorithm (1993). The index significantly improved the model fit (likelihood ratio test: p <0.001). The D’Hoore-adapted Charlson index is a useful comorbidity risk adjustment tool when applied to AMI and angina patients.

Keywords:

Comorbidity - Risk Adjustment - Acute Myocardial Infarction - Mortality
 

  • REFERENCES

  • 1 Sibthorpe B, Kliewer E, Smith L. Record linkage in Australian epidemiological research: health benefits, privacy safeguards and future potential. Aust J Public Health 1995; 19: 250-6.
    PubMedGoogle Scholar
  • 2 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: 373-83.
    CrossrefPubMedGoogle Scholar
  • 3 Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992; 45: 613-9.
    CrossrefPubMedGoogle Scholar
  • 4 D’Hoore W, Sicotte C, Tilquin C. Risk adjustment in outcome assessment: the Charlson comorbidity index. Method Inform Med 1993; 32: 382-7.
    PubMedGoogle Scholar
  • 5 D’Hoore W, Bouckaert A, Tilquin C. Practical Considerations on the Use of the Charlson Comorbidity Index with Administrative Databases. J Clin Epidemiol 1996; 49: 1429-33.
    CrossrefPubMedGoogle Scholar
  • 6 Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas AM, Pajak A. Myocardial infarction and coronary deaths in the World Health Organization MONICA Project: Registration procedures, event rates and case-fatality rates in 38 populations from 21 countries in 4 continents. Circulation 1994; 90: 583-612.
    CrossrefPubMedGoogle Scholar
  • 7 Dobson AJ, Alexander HM, Al-Roomi K. et al. Coronary events in the Hunter Region of New South Wales, Australia: 1984-1986. Acta Med Scand (Suppl) 1988; 728: 84-9.
    PubMedGoogle Scholar
  • 8 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: 1075-9.
    CrossrefPubMedGoogle Scholar
  • 9 StataCorp.. 1997. Stata Statistical Software: Release 5.0. College Station, TX: Stata Corporation.;
    Google Scholar
  • 10 SAS Institute INC, SAS/STAT®. Software: Changes and Enhancements through Release.
    PubMedGoogle Scholar
  • 11 Selker HP, Griffith JL, D’Agostino RB. A time-insensitive predictive instrument for acute myocardial infarction mortality in multicenter study. Med Care 1991; 29: 1196-211.
    CrossrefPubMedGoogle Scholar
  • 12 ISIS-2 (Second International Study of Infarct Survival) Collaborative Group.. Randomised trial of intravenous Streptokinase, oral aspirin, both or neither among 17187 cases of suspected acute myocardial infarction. Lancet 1988; 2: 349-60.
    PubMedGoogle Scholar
  • 13 ISIS-4 (Fourth International Study of Infarct Survival) Collaborative Group.. A randomized factorial trial assessing early oral captopril, oral mononitrate and intravenous magnesium sulphate in 58,050 patients with suspected acute myocardial infarction. Lancet 1995; 345: 669-85.
    CrossrefPubMedGoogle Scholar
  • 14 Romano PS, Roos LL, Jollis JG. Further evidence concerning the use of a clinical comorbidity index with ICD-9-CM administrative data. J Clin Epidemiol 1993; 46: 1085-90.
    CrossrefPubMedGoogle Scholar
  • 15 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: 273-8.
    CrossrefPubMedGoogle Scholar
  • 16 Normand S-LT, Morris CN, Fung KS, McNeil BJ, Epstein AM. Development and validation of a claims based index for adjusting for risk of mortality: the case of acute myocardial infarction. J Clin Epidemiol 1995; 48: 229-43.
    CrossrefPubMedGoogle Scholar
  • 17 Elixhauser A, Steiner C, Harris R, Coffey RM. Comorbidity Measures for Use with Administrative Data. Med Care 1998; 36: 8-27.
    CrossrefPubMedGoogle Scholar
  • 18 Iezzoni LI, Foley SM, Daley J, Hughes J, Fisher ES, Heeren T. Comorbidities, Complications, and Coding Bias: Does the Number of Diagnosis Codes Matter in Predicting In-Hospital Mortality?. JAMA 1992; 267: 2197-203.
    CrossrefPubMedGoogle Scholar