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pubmed:abstractText |
A survey-based COPD severity scoring algorithm could prove useful for targeted disease management and risk-adjustment. For this purpose, we sought to prospectively validate a COPD severity score that had previously been cross-sectionally validated. Using a population-based sample of 267 adults with self-reported physician-diagnosed COPD, we examined the extent to which this COPD severity score predicts future respiratory hospitalizations, emergency department (ED) visits, and outpatient visits. Structured telephone interviews, conducted at baseline and on an annual basis in two subsequent years, determined COPD severity scores and health-care utilization. A basic predictive model for respiratory-specific health-care utilization was developed using sociodemographics, tobacco history, and medical comorbidity data in multivariate logistic regression analysis. The added predictive value of the COPD severity score over and above this basic model was then evaluated by testing the change in model concordance indices. Our analysis demonstrated that the COPD severity score did, in fact, increase the concordance-index of models predicting future respiratory hospitalizations (increase from 80% to 87%; P = 0.03), ED visits (64% to 82%, P = 0.003), and outpatient visits (59% to 77%, P < 0.0001). In a separate analysis, both a greater baseline severity score and worsening of the severity score over time were prospectively associated with each outcome studied (P < 0.05 for each). In conclusion, the COPD severity score adds predictive value in estimating future respiratory-specific health-care utilization and is longitudinally responsive to evolving changes in COPD status over time. This severity score may be used to adjust for disease severity or to identify high-risk populations.
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pubmed:affiliation |
Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of California, San Francisco, California 94143-0111, USA. omachi@ucsf.edu
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pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, Non-P.H.S.,
Research Support, N.I.H., Extramural
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