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Published on: December 7, 2018
The Charlson Index Is Insufficient to Control for Comorbidities in a National Trauma Registry
Audrey Renson1, Marc A Bjurlin2
1Department of Clinical Research, New York University Langone Hospital-Brooklyn, Brooklyn, New York; Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, New York.
Insights
The Charlson Comorbidity Index (CCI) may leave residual confounding in observational studies. Logistic principal component analysis (LPCA) better adjusted for comorbidities, offering a viable alternative for controlling confounding.
Area of Science:
- Health Services Research
- Epidemiology
- Biostatistics
Background:
- The Charlson Comorbidity Index (CCI) is widely used to adjust for confounding by comorbidities in observational studies.
- The performance of the CCI in controlling for confounding has not been previously evaluated.
- An alternative method, logistic principal component analysis (LPCA), was also assessed for its ability to adjust for comorbidities.
Purpose of the Study:
- To evaluate the performance of the CCI and LPCA in adjusting for comorbidities.
- To compare the effectiveness of CCI and LPCA in controlling for confounding in observational research.
- To examine the association between insurance status and mortality, using comorbidity adjustment methods.
Main Methods:
- Utilized National Trauma Data Bank admissions (2010-2015) including mortality, payment method, and 36 ICD-9 comorbidities.
- Estimated odds ratios (ORs) for uninsured status and mortality before and after adjusting for CCI, LPCA, and separate covariates.
- Calculated standardized mean differences (SMDs) of comorbidity variables before and after inverse probability of treatment weighting (IPTW) for CCI, LPCA, and separate covariates.
Main Results:
- In nearly 5 million admissions, 68.3% had at least one comorbidity.
- The CCI performed similarly to unweighted samples (mean SMD = 0.080, OR = 1.25), indicating limited control of confounding.
- Two LPCA axes demonstrated superior confounding control (mean SMD = 0.04, OR = 1.31), accounting for 91.3% of observed confounding compared to CCI's 56.1%.
Conclusions:
- Adjusting for confounding using the CCI may lead to residual confounding, necessitating consideration of alternative strategies.
- Logistic principal component analysis (LPCA) presents a promising alternative for adjusting for comorbidities, particularly in small samples or when positivity assumptions are violated.
- The findings suggest LPCA offers more robust control for confounding than the CCI in observational studies.
Background:
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.
Materials And Methods:
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.
Results:
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%.
Conclusions:
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.
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