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MEASURING THE EFFECT OF CLINICAL GUIDELINES ON PATIENT OUTCOMES

Published online by Cambridge University Press:  25 May 2001

Deborah A. Marshall
Affiliation:
University of North Carolina at Chapel Hill
Kit N. Simpson
Affiliation:
University of North Carolina at Chapel Hill
Edward C. Norton
Affiliation:
University of North Carolina at Chapel Hill
Andrea K. Biddle
Affiliation:
University of North Carolina at Chapel Hill
Mike Youle
Affiliation:
Chelsea and Westminister Hospital, London

Abstract

Objectives: To identify and examine the methodologic issues related to evaluating the effectiveness of treatment adherence to clinical guidelines. The example of antiretroviral therapy guidelines for human immunodeficiency virus (HIV) disease is used to illustrate the points.

Methods: Regression analysis was applied to observational HIV clinic data for patients with CD4+ cell counts less than 500 per μL and greater than 50 per μL at baseline (n = 704),using Cox proportional hazards time-varying covariates models controlling for baseline risk. The results are compared with simpler models (Cox model [without time-varying covariates] and logistic regression). In addition, the effect of including a measure of exposure to antiretroviral guidelines in the model is explored.

Results: This study has three implications for modeling clinical guideline effectiveness. To capture events that are time-sensitive, a duration model should be used, and covariates that are time-varying should be modeled as time-varying. Thirdly, incorporating a threshold measure of exposure to reflect the minimum period of time for guideline adherence required for a measurable effect on patient outcome should be considered.

Conclusions: The methods proposed in this paper are important to consider if guidelines are to evolve from being a tool for summarizing and transferring the results of research from the literature to clinicians into a practical tool that influences clinical practice patterns. However, the methodology tested in this study needs to be validated using additional data on similar patients and using data on patients with other diseases.

Type
Research Article
Copyright
© 2000 Cambridge University Press

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