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Tuesday, 19 October 2004

This presentation is part of: Poster Session - Clinical Strategies; Judgment and Decison Making

CAN INTERVENTIONS FOR ASTHMA BE MODELED THROUGH AN OBJECTIVE MEASURE OF LUNG FUNCTION?

Sally S. Araki, PhD1, Anne L. Fuhlbrigge, MD, MS2, A. David Paltiel, PhD3, Katherine Croom2, Scott T. Weiss, MD, MS2, and Karen M. Kuntz, ScD4. (1) Stanford University, Center for Health Policy / Center for Primary Care and Outcomes Research, Stanford, CA, (2) Brigham and Women's Hospital, Harvard Medical School, The Channing Laboratory, Boston, MA, (3) Yale School of Medicine, Department of Epidemiology and Public Health, New Haven, CT, (4) Harvard School of Public Health, Harvard Center for Risk Analysis, Boston, MA

Purpose: To determine the degree to which modeling asthma interventions as a function of forced expiratory volume in 1 second percent predicted (FEV1) -- an objective measure of lung function -- predicts long-term outcomes of childhood asthma.

Methods: We developed a first-order Monte Carlo simulation model of childhood asthma using data from the Childhood Asthma Management Program (CAMP), a multicenter clinical trial designed to examine the long-term effects of inhaled corticosteroids (ICS) in asthmatic children. The primary outcomes consisted of acute events: hospitalizations, emergency room (ER) visits, and the need for rescue therapy. Monthly risks of each acute event were based on logistic regression equations estimated from the placebo arm of CAMP with covariates of age, FEV1, prior hospitalizations, and prior night awakenings. We then modeled the effect of ICS as a percent improvement in FEV1 (8% for mild, 12% for moderate, 15% for severe impairments in lung capacity) based on prior estimates derived from short-term clinical trials in the literature. Finally, we compared simulated outcomes for ICS with observed outcomes (i.e., from the ICS treatment arm in CAMP).

Results: Simulated versus observed mean number of acute events were similar in the placebo (natural history) group. Predicted [observed] means over 48 months were: hospitalizations 0.25 [0.25]; ER visits 1.05 [1.05]; and need for rescue therapy 4.82 [4.87]. In the treatment group, simulated versus observed number of events closely matched for hospitalizations (0.15 vs. 0.15), but not for ER visits (0.88 vs. 0.57) or need for rescue therapy (3.88 vs. 2.79). While the trial demonstrated treatment effects of 39% reduction in hospitalizations, 46% reduction in ER visits, and 43% reduction in the need for rescue therapy at 48 months; the model simulated reductions of 40% in hospitalizations, 16% in ER visits, and 19% in the need for rescue therapy. By modeling the effect of intervention on FEV1, we were able to closely predict hospitalizations but underestimated treatment effects on ER visits and the need for rescue therapy.

Conclusions: Our findings suggest that intervention effects may be modeled through FEV1 to predict hospitalizations in childhood asthma over time for planning and evaluation purposes. However, intervention effects on FEV1 alone do not predict ER visits or the need for rescue therapy; additional factors must be considered.


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