Format

Send to

Choose Destination
See comment in PubMed Commons below
J Clin Epidemiol. 2004 Oct;57(10):1040-8.

Common comorbidity scales were similar in their ability to predict health care costs and mortality.

Author information

1
Indiana University Center for Aging Research, 1050 Wishard Boulevard, RG6, Indianapolis, IN 46202, USA. tperkins@regenstrief.org

Abstract

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.

STUDY DESIGN AND SETTING:

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.

CONCLUSION:

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.

PMID:
15528055
DOI:
10.1016/j.jclinepi.2004.03.002
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science
    Loading ...
    Support Center