A Decision-Theoretic Approach to Panel-Based, Preemptive Genotyping. Issue 2 (August 2019)

• Record Type: Journal Article
• Authors: Shi, Yaping; Graves, John A.; Garbett, Shawn P.; Zhou, Zilu; Marathi, Ramya; Wang, Xiaoming; Harrell, Frank E.; Lasko, Thomas A.; Denny, Joshua C.; Roden, Dan M.; Peterson, Josh F.; Schildcrout, Jonathan S.
• Is Part Of: MDM policy & practice. Volume 4:Issue 2(2019)
• Publication Date: 2019-08
• Subjects: precision medicine -- risk prediction -- pre-emptive genotyping -- electronic health records -- discrete event simulation; Medicine -- Decision making -- Periodicals; Medicine -- Decision making; Decision Making; Clinical Medicine; Health Policy; Periodicals; Periodicals; Electronic journals; 616.075
• DOI: 10.1177/2381468319864337
• Languages: English
• ISSNs: 2381-4683

Abstract

We discuss a decision-theoretic approach to building a panel-based, preemptive genotyping program. The method is based on findings that a large percentage of patients are prescribed medications that are known to have pharmacogenetic associations, and over time, a substantial proportion are prescribed additional such medication. Preemptive genotyping facilitates genotype-guided therapy at the time medications are prescribed; panel-based testing allows providers to reuse previously collected genetic data when a new indication arises. Because it is cost-prohibitive to conduct panel-based genotyping on all patients, we describe a three-step approach to identify patients with the highest anticipated benefit. First, we construct prediction models to estimate the risk of being prescribed one of the target medications using readily available clinical data. Second, we use literature-based estimates of adverse event rates, variant allele frequencies, secular death rates, and costs to construct a discrete event simulation that estimates the expected benefit of having an individual's genetic data in the electronic health record after an indication has occurred. Finally, we combine medication prescription risk with expected benefit of genotyping once a medication is indicated to calculate the expected benefit of preemptive genotyping. For each patient-clinic visit, we calculate this expected benefit across a range of medications and select patients with the highest expected benefit overall. We build a proof of concept implementation using a cohort of patients from a single academic medical center observed from July 2010 through December 2012. We then apply the results of our modeling strategy to show the extent to which we can improve clinical and economic outcomes in a cohort observed from January 2013 through December 2015.