Concept: Charlson Comorbidity Index
Last Updated: 2016-01-22
Description of the Charlson Comorbidity Index
Use of the Charlson Comorbidity Index at MCHP
Source of Diagnosis Codes for the Charlson Comorbidity IndexHistorical Research Perspective on the Charlson Comorbidity Index
The diagnosis codes required for use in the Charlson Comorbidity Index are available in the hospital abstracts data and in the medical services (physician claims) data.
Hospital data contains all the relevant diagnoses during an episode of care as an inpatient. For each diagnosis recorded in the hospital data, a corresponding variable called diagnosis type is used to identify whether the diagnosis is considered a comorbidity (pre-existing condition) or a complication (a condition arising during the hospital stay). Complications are identified by a diagnosis type = "C" (complication) in the data prior to April 1, 2004 (for use with ICD-9-CM codes) or by a diagnosis type = "2" (post-admit comorbidity) in the data beginning on April 1, 2004 (for use with ICD-10 codes). At MCHP, complications can be included or excluded in the Charlson Comorbidity Index algorithm.
The Medical Services (Physician Claims) data contain only one diagnosis code per record, relevant to the reason for the visit to the physician. Although the Charlson Comorbidity Index is originally designed to work with hospital data only, at MCHP we have developed a method that can include 3-digit diagnosis codes from the Medical Services data, if warranted by the research, to expand the scope of comorbidity found in the population. See the section titled MCHP Charlson Comorbidity Index SAS Code for 3-Digit Codes in the Medical Services (Physician) Data for more information on using 3-digit codes from the Medical Services data.
Lists of Charlson Comorbidity Index Categories and the Associated ICD Codes
- For a current list of the 17 categories in the Charlson Comorbidity Index used in MCHP research, along with the ICD-9-CM and ICD-10-CA diagnosis codes from both Hospital Abstracts (hosp) and Medical Services (med) data, and the associated weights for each category, please see the relevant table from Lix et al. (2016).
- For a list of the categories and ICD codes used in Deyo et al. (1992) and Quan et al. (2005), please see ICD-9-CM and ICD-10 Coding Algorithms for Charlson Comorbidities.MCHP Charlson Comorbidity Index SAS Code
At MCHP there are different SAS programs available that can be used to generate Charlson Comorbidity Index scores. The process involves producing category indicators at the record level from hospital abstracts data and calculating a score for each episode of care, or if desired, reviewing multiple records from hospital abstracts and physician visits data for the same individual over time, and then generating an overall Index score. The SAS code includes:
- two SAS macros that identify the Charlson Comorbidity categories and the total number of categories for each individual record (hospital episode). One macro is based on ICD-9-CM diagnoses codes and the other is based on ICD-10 diagnoses codes;
- SAS code that identifies the appropriate Charlson Comorbidity category for each record from the medical services (physician visits) data based on 3-digit ICD-9-CM diagnosis codes; and
- SAS code that calculates a longitudinal Index score based on all of the episodes of hospital care and physician visits for an individual and the applicable weights for each category of comorbidity.
MCHP Charlson Comorbidity Index SAS Macros for Hospital Data - Individual Episode of CareThere are two different SAS macros available for working with hospital data: one for use with ICD-9-CM diagnosis codes and one for use with ICD-10 diagnosis codes.
To run the MCHP Charlson Comorbidity Index SAS macro, a file containing individual hospital records with diagnosis codes and the corresponding diagnosis type is required. Although the Charlson Comorbidity Index was originally designed for use with comorbidities only, there may be times when the Index should consider all diagnoses. For example, if the Index is being used in a longitudinal study, all diagnoses could be included in the Index calculation. However, if the study period only covers a short period of time, including complications may over-estimate the burden of disease.
The MCHP Charlson Comorbidity Index SAS macros have a parameter option to include all types of diagnoses (type=off) or to limit the diagnoses to exclude complications (type=on). A research decision should be made on whether to include all diagnoses or exclude complications in the Index calculation.
The two MCHP SAS macros are available below:
NOTE: The MCHP SAS macro code is based on information in Quan's "Enhanced Charlson Diagnosis-Type SAS code" programs, but modified to be more generalized for use with other data sources and to run more efficiently at MCHP.To run the MCHP Charlson Comorbidity Index SAS code with the Medical Services (Physician Claims) data, a file containing individual physician visit records with the diagnosis code is required.
CAUTION: the Medical Services data should not be used alone to create the Charlson Comorbidity Index, as many 3-digit ICD-9-CM codes lack the specificity required in the algorithm. The Medical Services data should only be used in conjunction with the Hospital Abstracts data to generate an Index score. In general, using Medical Services data alone to calculate the Charlson Comorbidity Index is not recommended and goes beyond the original intent of the Index.
The use of 3-digit codes requires a modification in the Charlson Comorbidity Index algorithm. These modifications include:
- combining the two diabetes conditions, "Diabetes With Complication" and "Diabetes Without Complications", into one category because it is not possible to differentiate between the two categories using 3-digit codes. Anyone with ICD code "250" is assigned to the less severe "Diabetes Without Complications" category.
- deciding to include or exclude certain codes in specific categories due to the specificity issue. For a complete list of the 3-digit ICD-9-CM codes used for each category and the choices that were made for inclusion/exclusion of 3-digit codes in our research, please see Using 3-Digit ICD-9-CM Codes with the Charlson Comorbidity Index.
See the Notes, Cautions and Limitations section below for more information on specificity and using 3-digit ICD codes in the algorithm.
The MCHP SAS code for working with 3-digit codes is available below:
NOTE: The 3-digit SAS code example is based on work at MCHP for generating the Index based on 3-digit ICD-9-CM diagnosis codes found in the Medical Services (Physician Claims) data.
MCHP Charlson Comorbidity Index SAS Code - Calculating a Longitudinal Index ScoreThis code will calculate a longitudinal Index score for an individual based on multiple Index records over time. It takes into account all of the comorbidity categories indicated for all episodes of hospital care and physician visits, and the algorithm flags a comorbidity category as present only once during the calculation and does not increase the overall Index score when the same category occurs more than once. This code will also attach the appropriate weight to each category when it is calculating the Index score.
The MCHP SAS code for calculating the Index score is available below:
MCHP Research Using the Charlson Comorbidity Index
The following is a list of published MCHP research that have used the Charlson Comorbidity Index, and a brief description of how the Index is used in that research.
1. Roos et al. (1997)In the publication Complications, comorbidities, and mortality: improving classification and prediction by Roos et al. (1997), they investigated the possible confounding nature of complications (conditions arising after the beginning of hospital treatment) and found the existence of a variable called "Diagnosis Type" in the data could be used to separate complications from comorbidities.
NOTE: Information relevant to this early work by MCHP on the Charlson Comorbidity Index is available from Charlson Comorbidity Index - Archived Concept Information OR from the LINKS section below -- see MCHP - Archived Charlson Concept Information.
2. Garland et al. (2012)In the MCHP Deliverable The Epidemiology and Outcomes of Critical Illness in Manitoba by Garland et al. (2012), they used the Charlson Comorbidity Index, as adapted by Deyo and Quan, as one of three systems for assessing chronic comorbid health conditions. For more information on the use of the Charlson Comorbidity Index and the results from this research, please read the following sections:
3. Lix et al. (2016)In the MCHP Deliverable Cancer Data Linkage in Manitoba: Expanding the Infrastructure for Research by Lix et al. (2016), additional SAS code has been developed by MCHP to incorporate the 3-digit ICD-9-CM diagnosis codes available in the Medical Services (Physician Claims) data into the Charlson Comorbidity Index algorithm. Links will be provided to the research methodology and results when this becomes available.
For a list of the 17 categories in the Charlson Comorbidity Index used in this MCHP research, along with the ICD-9-CM and ICD-10-CA diagnosis codes from both Hospital Abstracts (hosp) and Medical Services (med) data, and the associated weights for each category, please see the relevant table in this research deliverable.
For more information on using 3-digit codes from the Medical Services data, see the section titled MCHP Charlson Comorbidity Index SAS Code for 3-Digit Codes in the Medical Services (Physician) Data in this concept.
In the original publication "A new method of classifying prognostic comorbidity in longitudinal studies: development and validation" by Charlson et al. (1987), they identified 19 categories of comorbidity and weights were developed for each category based on the adjusted relative risk of one-year mortality. All of the individual weights are summed to produce a single comorbidity score for each patient.
2. Deyo et al. (1992)
In the publication "Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases" by Deyo et al. (1992), they assigned corresponding ICD-9-CM diagnosis and procedure codes for each category of the Charlson Comorbidity Index. Two of the original categories, "Leukemia" and "Lymphomas", were combined in the "Any Malignancy" category, thus resulting in a total of 17 different categories.
3. Romano et al. (1993)
In the publication "Further evidence concerning the use of a clinical comorbidity index with ICD-9-CM administrative data" by Romano et al. (1993), they compared the list of ICD codes developed by Deyo with a different set of codes, referred to as the Dartmouth-Manitoba codes, developed for use with the Charlson Comorbidity Index. The study illustrated that the assignment of ICD-9-CM codes to the Charlson Comorbidity Index is not straightforward and depends on the type of data and its availability, and concluded that in some cases the differences will have little or a significant impact on the measure of comorbidity.
4. Halfon et al. (2002)
In the publication "Measuring potentially avoidable hospital readmissions" by Halfon et al. (2003), they translated the ICD-9-CM diagnosis codes from the Deyo adaptation of the Charlson Comorbidity Index into ICD-10-codes. Appendix C in the article presents the ICD-10 codes and assigned weights that were developed by Halfon.
5. Schneeweiss et al. (2003)
In the publication "Improved comorbidity adjustment for predicting mortality in Medicare populations" by Schneeweiss et al. (2003), they investigated methods for adjusting comorbidity measures in order to improve the prediction of mortality in Medicare populations. This research used Romano's adapted Charlson Comorbidity Index and came up with adjusted weights for using the index - see Table 4: Conditions According to the Romano Adaptation of the Charlson Comorbidity Index for Use with Claims Data with original Charlson Index Weights and Weights Derived from New Jersey Medicare Data in the full text publication available from the PubMed Central web site at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1360935/pdf/hesr_165.pdf - accessed October 3, 2014.
In the publication "Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data" by Quan et al. (2005), they "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." They also followed Deyo's coding algorithm, which led to modifications and enhanced coding algorithms for the index. For a listing of the ICD-9-CM and ICD-10 codes used for each category in Quan et al. (2005), please see ICD-9-CM and ICD-10 Coding Algorithms for Charlson Comorbidities.7. Sundararajan et al. (2007)
Comparison of Halfon and Quan's ICD-10 CodingThe following link is a table comparing Halfon's and Quan's translations of the Charlson Comorbidity Index using ICD-10 codes: Halfon-Quan Comparison of Charlson ICD-10 Codes. Note: The table also includes Quan's translation of the Elixhauser index.The Charlson Comorbidity Index SAS® code developed for this project is available below with Quan's permission:
- Quan's - ICD-9-CM Enhanced Charlson SAS Code
- Quan's - ICD-9-CM Enhanced Charlson Diagnosis-Type SAS Code
- Quan's - ICD-10 Enhanced Charlson SAS Code
- Quan's - ICD-10 Enhanced Charlson Diagnosis-Type SAS Code
NOTE: Quan's SAS code examples have not been validated at MCHP.
In the publication "Cross-national comparative performance of three versions of the ICD-10 Charlson Index" by Sundararajan et al. (2007), they compared the performance of three different versions of the Charlson Comorbidity Index using ICD-10 coding and found that all three versions performed satisfactorily.8. Li et al. (2008)
In the publication "Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases" by Li et al. (2008), they assessed the performance of the Charlson Comorbidity Index and the Elixhauser Comorbidity Index using ICD-10 coding systems and found that the "change in coding algorithms did not influence the performance of either ... [index] ... in the prediction of outcome".
Notes, Cautions and Limitations
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