The Relative Ability of Comorbidity Ascertainment Methodologies to Predict In-Hospital Mortality Among Hospitalized Community-acquired Pneumonia Patients.

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 1596 patients admitted to the University of Chicago for community-acquired pneumonia from 1998 to 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 (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 have 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.