Common comorbidity scales were similar in their ability to predict health care costs and mortality.
OBJECTIVE
To compare the ability of commonly used measures of medical comorbidity (ambulatory care groups [ACGs], Charlson comorbidity index, chronic disease score, number of prescribed medications, and number of chronic diseases) to predict mortality and health care costs over 1 year.


METHODS
A prospective cohort study of community-dwelling older adults (n=3,496) attending a large primary care practice.


RESULTS
For predicting health care charges, the number of medications had the highest predictive validity (R(2)=13.6%) after adjusting for demographics. ACGs (R(2)=16.4%) and the number of medications (15.0%) had the highest predictive validity for predicting ambulatory visits. ACGs and the Charlson comorbidity index (area under the receiver operator characteristic [ROC] curve=0.695-0.767) performed better than medication-based measures (area under the ROC curve=0.662-0.679) for predicting mortality. There is relatively little difference, however, in the predictive validity across these scales.


CONCLUSIONS
In an outpatient setting, a simple count of medications may be the most efficient comorbidity measure for predicting utilization and health-care charges over the ensuing year. In contrast, diagnosis-based measures have greater predictive validity for 1-year mortality. Current comorbidity measures, however, have only poor to moderate predictive validity for costs or mortality over 1 year.
Perkins
Anthony J
AJ
Indiana University Center for Aging Research, 1050 Wishard Boulevard, RG6, Indianapolis, IN 46202, USA. tperkins@regenstrief.org
Kroenke
Kurt
K
Unützer
Jürgen
J
Katon
Wayne
W
Williams
John W
JW
Hope
Carol
C
Callahan
Christopher M
CM
D001741:Black or African American; D000368:Aged; D000553:Ambulatory Care / Q000191:economics; D002423:Cause of Death; D002908:Chronic Disease; D015897:Comorbidity; D005260:Female; D017048:Health Care Costs; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D015233:Models, Statistical; D019338:Polypharmacy; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies