Comparison of risk adjustment measures based on self-report, administrative data, and pharmacy records to predict clinical outcomes

Comparing clinical outcomes in observational studies often requires adjustment for comorbid disease. The objective of this study was to compare the performance of risk adjustment measures derived from different data sources to predict the clinical outcomes of mortality and hospitalization. We compared the predictive ability of self-reported comorbidity measures to those derived from administrative diagnosis codes and pharmacy data to predict all-cause mortality and hospitalizations in a large sample of veterans receiving care in the Veterans Affairs outpatient clinic setting. In logistic regression models to predict mortality adjusting for age and gender, the Seattle Index of Comorbidity, SF-36, Charlson Index, Diagnosis Cost Groups, and RxRisk had similar discriminatory power ranging between 0.73 and 0.74. The Adjusted Clinical Groups and Chronic Illness and Disability Payment System were less accurate in prediction mortality. Although all measures performed less well in predicting hospitalizations, administrative measures performed better than self-reported measures. We conclude that self-reported morbidity measures had similar performance to administrative and pharmacy measures to predict mortality in a larger outpatient sample, but under-performed these measures in predicting hospitalization. While models using self-report measures can typically only be run on subsamples of patients for which models using administrative and pharmacy measures can be run, models combining self-reported morbidity and other measures performed better than models with a single measure. © Springer Science+Business Media, LLC 2006.