A Decision-Theoretic Approach to Panel-Based, Preemptive Genotyping
| Autor: | Josh F. Peterson, Shawn P. Garbett, Ramya Marathi, Frank E. Harrell, Zilu Zhou, Joshua C. Denny, Xiaoming Wang, Yaping Shi, John A. Graves, Jonathan S. Schildcrout, Dan M. Roden, Thomas A. Lasko |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
precision medicine 030204 cardiovascular system & hematology Machine learning computer.software_genre 030226 pharmacology & pharmacy Article risk prediction 03 medical and health sciences 0302 clinical medicine Discrete event simulation Prescribed medications Genotyping discrete event simulation lcsh:R5-920 business.industry Health Policy pre-emptive genotyping Public Health Environmental and Occupational Health Precision medicine 3. Good health electronic health records Artificial intelligence business lcsh:Medicine (General) computer |
| Zdroj: | MDM Policy & Practice, Vol 4 (2019) MDM Policy & Practice |
| ISSN: | 2381-4683 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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