Background–
Despite widespread use of comorbidities for population health
descriptions and risk adjustment, the ideal method for ascertaining
comorbidities is not known. We sought to compare the relative value of
several methodologies by which comorbidities may be ascertained.
Methods–
This is an observational study of 1,596 patients admitted to the
University of Chicago for community-acquired pneumonia from
1998–2012. We collected data via chart abstraction, administrative
data, and patient report, then performed logistic regression analyses,
specifying comorbidities as independent variables and in-hospital mortality
as the dependent variable. Finally, we compared area under the curve
(AUC) statistics to determine the relative ability of each method
of comorbidity ascertainment to predict in-hospital mortality.
Results–
Chart review (area under curve [AUC] 0.72) and administrative data
(Charlson AUC 0.83, Elixhauser AUC 0.84) predicted in-hospital mortality
with greater fidelity than patient report (AUC 0.61). However, multivariate
logistic regression analyses demonstrated that individual comorbidity
derivation via chart review had the strongest relationship with in-hospital
mortality. This is consistent with prior literature suggesting that
administrative data has inherent, paradoxical biases with important
implications for risk adjustment based solely on administrative data.
Conclusions–
Although comorbidities derived through administrative data did
produce an AUC greater than chart review, our analyses suggest a coding bias
in several comorbidities with a paradoxically protective effect. Therefore,
chart review, while labor and resource intensive, may be the ideal method
for ascertainment of clinically-relevant comorbidities.
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