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“Objective: The Afirma Gene Expression Classifier (GEC) molecular marker assay was developed for the purpose of improving surgical decision-making with indeterminate Kinase Inhibitor Library fine-needle aspiration (FNA) biopsies of thyroid nodules. In this paper, we analyze the performance of the GEC over 27 months in a community hospital-based thyroid surgery practice.
Methods: We began using GEC and Thyroid Cytopathology Partners (TCP) exclusively for thyroid FNA analysis in January 2011, shortly after the Afirma GEC became commercially available. In this paper, we focus on patients with indeterminate FNA results and the outcomes of GEC analysis, with particular attention paid to the calculation of the negative predictive value
(NPV) of the Afirma test.
Results: We performed 645 FNAs in 519 patients over 27 months. Overall, 58 FNAs (9%) were read as indeterminate, with 36 of these classified as suspicious by GEC (62%), 20 characterized as GEC benign (34%), and 2 determined to be inadequate due to low mRNA content. Of the 36 suspicious GEC patients, 30 underwent thyroidectomy, and 21 of the 30 had malignant final pathology. Of the 20 benign GEC patients, 5 underwent thyroid surgery, and 2 were discovered to have malignancies. The NPV for the Afirma GEC in our practice environment was 89.6%.
Conclusion: In a practice with a high incidence of thyroid cancer in patients with indeterminate FNAs (33% for our practice),
the NPV of the Afirma GEC test may not be as robust as suggested
in the existing literature.”
“Background: Sample size planning for clinical trials is usually based on detecting a target effect size of an intervention or treatment. Explicit incorporation of costs Y-27632 purchase into such planning is considered in this article in the situation where effects of an intervention or treatment may depend on (interact with) baseline severity of the targeted symptom or disease. Because much larger sample sizes are usually learn more required to establish such an interaction effect, investigators frequently conduct studies to establish a marginal effect of the intervention for individuals with a certain level of baseline severity.
Methods: We conduct a rigorous investigation on how to determine optimum baseline symptom or disease severity inclusion criteria so that the most cost-efficient design can be used. By using a regression model with an interaction term of treatment by symptom severity, power functions were derived for various levels of baseline symptom severity. Computer algorithms and mathematical optimization were used to determine the most cost-efficient research designs assuming either single-or dual-stage screening procedures.
Results: In the scenarios we considered, impressive cost savings can be achieved by informed selection of baseline symptom severity via the inclusion criteria. Further cost-savings can be achieved if a two stage screening procedure is used and there are some known, relatively inexpensively collected, pre-screening information.