Concept: Charlson Index

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Concept Description

Last Updated: 2009-10-22

Introduction

The Charlson Index contains 19 categories of comorbidity, which are primarily defined using ICD-9-CM diagnoses codes (a few procedure codes are also employed). Each category has an associated weight, taken from the original Charlson paper ¹ , which is based on the adjusted risk of one-year mortality. The overall comorbidity score reflects the cumulative increased likelihood of one-year mortality; the higher the score, the more severe the burden of comorbidity.

Use the Charlson Index with caution - the ICD-9-CM codes used in the program can refer either to complication or comorbidity diagnoses. Unless these can be reliably differentiated it is possible that the burden of comorbid disease might be overestimated. The best way to avoid the confusion of complications with comorbidities is to remove the complications. For hospital datasets this can be done using the diagnosis type variable.

When this is not possible the use of this macro is not precluded: the effect of complications on the comorbidity estimates provided by this macro is limited since individual diagnoses are not used directly in calculating the comorbidity score. Rather, these determine attribution to comorbidity categories which are scored as binary variables. This binary attribution within a comorbidity category limits the effect of complications since both the comorbidity and complication codes often fall in the same disease category.

Recent work by Quan et al. (2005) has modified the Charlson Index (ICD-9-CM codes) and updated the coding to ICD-10. These updates are presented towards the bottom of this concept.

How the Index Works

The Index may be calculated either for a single record (separation) or over a defined period of time prior to an index event. Every diagnosis and procedure code is analyzed to see if it falls within one of the 16 comorbid conditions. If one of these is found, a flag (1) for that condition is set. These flags are weighted appropriately and summed to generate values:

PCHRLSON - Charlson variable computed from admissions prior to and including the event.

XCHRLSON - Charlson variable computed from the event admission only.

PCOMP - Component variables for the Charlson variable observed on admissions prior to the event. (Note: there are 61 component variables)

XCOMP - Component variables for the Charlson variable which were observed on the event admission. (Note: There are 61 component variables)

AGGREGATES - Combinations of PCOMP and XCOMP variables which are used for final determination of the Charlson score.

SAS CODE:

A SAS macro is available from MCHP that will compute the Index. This SAS code corresponds to the original Charlson work.


• This program was based on 1993 ICD-9-CM codes for various conditions. If you are using the index on other years you should check to confirm there have been no major changes in ICD codes.


The following table (from Schneeweis et al, 2003 ) provides the weights used in this recent paper along with the original weights.

Table 4: Conditions According to the Romano Adaptation of the Charlson Index for Use with Claims Data ( Romano et al, 1993a, 1993b ) with original Charlson Index Weights ( Charlson et al. 1987 ) and Weights Derived from New Jersey Medicare Data (N=235, 881) for the Same Conditions.

The following codes are included in the aforementioned SAS program

SAS CODE: developed from Quan et al. (2005).

Note: This code has not been validated by MCHP.



• ICD-9-CM Enhanced Charlson SAS code
  
  
• ICD-9-CM Enhanced Charlson Diagnosis-Type SAS code
  
  
• MCHP SAS macro code for Charlson ICD-9-CM Index
  
  Note: The MCHP SAS macro code is based on information in Quan's "ICD-9-CM Enhanced Charlson Diagnosis-Type SAS code" program, but modified to be more generalized for use with other data sources and to run more efficiently.

ICD-9-CM to ICD-10 Conversion

Several studies have translated the Charlson index from the usage of ICD-9-CM codes to ICD-10 codes for the study of complications and comorbidities. Quan et al. "conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms." Halfon et al. carried out the translation to ICD-10 from the Deyo adaptation of the Charlson comorbidity index. The following link is a table comparing Halfon's and Quan's translations of the Charlson Index using ICD-10 codes. Excel ] Note: The table also includes Quan's translation of the Elixhauser index. <in progress>

SAS CODE: developed from Quan et al. (2005).

Note: This code has not been validated by MCHP.



• ICD-10-CA Charlson SAS code
  
  
• ICD-10-CA Enhanced Charlson Diagnosis-Type SAS code
  
  
• MCHP SAS macro code for Charlson ICD-10-CA Index
  
  Note: The MCHP SAS macro code is based on information in Quan's "ICD-10-CA Enhanced Charlson Diagnosis-Type SAS code" program, but modified to be more generalized for use with other data sources and to run more efficiently.

SAS code and formats 

• Charlson ICD-10-CA Index SAS Macro Code
• Charlson ICD-9-CM Index SAS Macro Code
• ICD-10-CA Charlson SAS Code
• ICD-10-CA Enhanced Charlson Diagnosis-Type SAS Code
• ICD-9-CM Enhanced Charlson Diagnosis-Type SAS Code
• ICD-9-CM Enhanced Charlson SAS Code

Related concepts 

• Complications and Comorbidities

Related terms 

• Charlson Comorbidity Index
• Elixhauser Comorbidity Index
• ICD-10
• ICD-10-CA
• ICD-9-CM
• International Classification of Diseases, 10th Revision (ICD-10)
• International Classification of Diseases, 10th Revision, with Canadian Enhancements (ICD-10-CA)
• International Classification of Diseases, 9th Revision, with Clinical Modifications (ICD-9-CM)

Links 

• ICD-9-CM and ICD-10 Coding Algorithms for Charlson Comorbidities

References 

• 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):373-383. [Abstract] (View)
• Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. Journal of Clinical Epidemiology 1992;45(6):613-619. [Abstract] (View)
• Halfon P, Eggli Y, van Melle G, Chevalier J, Wasserfallen JB, Burnand B. Measuring potentially avoidable hospital readmissions. J Clin Epidemiol 2002;55(6):573-587. [Abstract] (View)
• 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 2008;14(8):12. [Abstract] (View)
• Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005;43(11):1130-1139. [Abstract] (View)
• Quan H, Parsons GA, Ghali WA. Validity of information on comorbidity derived rom ICD-9-CCM administrative data. Med Care 2002;40(8):675-685. [Abstract] (View)
• 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(10):1075-1079. [Abstract] (View)
• Romano PS, Roos LL, Jollis JG. Response: Further evidence concerning the use of a clinical comorbidity index with ICD-9-CM administrative data. J Clin Epidemiol 1993;46(10):1085-1090. [Abstract] (View)
• Roos LL, Stranc L, James RC, Li J. Complications, comorbidities, and mortality: improving classification and prediction. Health Serv Res 1997;32(2):229-238. [Abstract] (View)
• Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res 2003;38(4):1103-1120. [Abstract] (View)
• Sundararajan V, Quan H, Halfon P, Fushimi K, Luthi JC, Burnand B, Ghali WA, International Methodology Consortium for Coded Health Information (IMECCHI). Cross-national comparative performance of three versions of the ICD-10 Charlson Index. Medical Care 2007;45(12):1210-1215. [Abstract] (View)

Keywords 

• comorbidity
• complications
• Disease
• Health Measures
• mortality