Purpose: To evaluate whether OIT or the standard strict avoidance was associated with higher gains in quality-adjusted life-years (QALYs).
Method: We performed a decision analysis incorporating a Markov transition model to compare the expected gain in quality-adjusted life years of CMP OIT with strict CMP avoidance over a lifetime. The base case was assumed to be an 8-year-old child of either gender with CMA. Rates of transition to the partial or full desensitization and complete tolerance states, rates of outgrowing CMA and death due to CMA, probabilities, disutilities and duration of mild and severe reactions, and health state utilities were determined from the literature. Extensive one and two-way sensitivity analyses were performed. Ranges of variables were determined or estimated based on published studies.
Result: In the baseline analysis, OIT showed a 3.5-year greater quality-adjusted life expectancy, although absolute unadjusted life expectancy was similar in both arms. When all parameters were varied over plausible ranges, sensitivity analysis indicated that the model was sensitive to the CMA, full desensitization and complete tolerance health state utilities, but not to the rates of transition between states. Probabilities of severe reactions had to be over 10 times the literature-based estimates at the maximum plausible duration and disutility to affect the model outcome. Limitations of this model include the small number of OIT clinical trials on which to base the parameter estimates and their short duration of follow-up, the difficulty of determining reaction rates and death rates specifically due to CMA, the paucity of utility measures for younger children with CMA, and the exclusion of factors such as family history or other atopic conditions that may affect a child’s response to OIT.
Conclusion: This decision analysis suggests that, over a lifetime, OIT offers a benefit by increasing quality-adjusted life, when treatment is commenced in an 8-year-old child with CMA. Longer follow-up times and additional clinical trials with larger study sample sizes will help to address the limitations of this decision analysis.
Candidate for the Lee B. Lusted Student Prize Competition
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