The Charlson Index Is Insufficient to Control for Comorbidities in a National Trauma Registry

The Charlson Comorbidity Index (CCI) is frequently used to control for confounding by comorbidities in observational studies, but its performance as such has not been studied. We evaluated the performance of CCI and an alternative summary method, logistic principal component analysis (LPCA), to adju...

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Bibliographic Details
Published in:The Journal of surgical research 2019-04, Vol.236, p.319-325
Main Authors: Renson, Audrey, Bjurlin, Marc A.
Format: Article
Language:English
Subjects:
Adult
Aged
Bias
Biostatistics
Causal inference
Comorbidity
Confounding Factors, Epidemiologic
Data Interpretation, Statistical
Female
Hospitalization - statistics & numerical data
Humans
Male
Middle Aged
Observational studies
Observational Studies as Topic
Odds Ratio
Principal Component Analysis
Registries - statistics & numerical data
Retrospective Studies
Risk Factors
Trauma
Trauma Centers - statistics & numerical data
Trauma Severity Indices
Wounds and Injuries - diagnosis
Wounds and Injuries - epidemiology
Wounds and Injuries - therapy
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Summary:The Charlson Comorbidity Index (CCI) is frequently used to control for confounding by comorbidities in observational studies, but its performance as such has not been studied. We evaluated the performance of CCI and an alternative summary method, logistic principal component analysis (LPCA), to adjust for comorbidities, using as an example the association between insurance and mortality. Using all admissions in the National Trauma Data Bank 2010-2015, we extracted mortality, payment method, and 36 International Classification of Disease, Ninth Revision–derived comorbidities. We estimated odds ratios (ORs) for the association between uninsured status and mortality before and after adjusting for CCI, LPCA, and separate covariates. We also calculated standardized mean differences (SMDs) of comorbidity variables before and after weighting the sample using inverse probability of treatment weights for CCI, LPCA, and separate covariates. In 4,936,880 admissions, most (68.3%) had at least one comorbidity. Considerable imbalance was observed in the unweighted sample (mean SMD = 0.086, OR = 1.17), which was almost entirely eliminated by inverse probability of treatment weights on separate covariates (mean SMD = 0.012, OR = 1.36). The CCI performed similarly to the unweighted sample (mean SMD = 0.080, OR = 1.25), whereas two LPCA axes were better able to control for confounding (mean SMD = 0.04, OR = 1.31). Using covariate adjustment, the CCI accounted for 56.1% of observed confounding, whereas two LPCA axes accounted for 91.3%. The use of the CCI to adjust for confounding may result in residual confounding, and alternative strategies should be considered. LPCA may be a viable alternative to adjusting for each comorbidity when samples are small or positivity assumptions are violated.
ISSN:0022-4804
1095-8673
DOI:10.1016/j.jss.2018.07.072