Veterans Affairs Pharmacogenomic Testing for Veterans (PHASER) clinical program

Dong, Olivia; Bates, Jill; Chanfreau‐Coffinier, Catherine; Naglich, Michael; Kelley, Michael J.; Meyer, Laurence; Icardi, Michael; Vassy, Jason L.; Sriram, Peruvemba; Heise, C. William; Rivas, Salvador; Ribeiro, Maria Ângela Gonçalves de Oliveira; Jacobitz, Russell; Rozelle, Susan; Chapman, Jennifer; Voora, Deepak

doi:10.2217/pgs-2020-0173

Cited by 23 publications

(19 citation statements)

References 11 publications

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“…The NHS England was able to reorganize its existing capacity in 2018, creating a Genomic Medicine Service through its Genomic Laboratory Hubs, each hosted by an acute NHS trust and designated a geographic region for coverage [ 12 ]. Similarly, the US Department of Veteran’s Affairs has leveraged its existing capacity to deliver genetic testing through its oncology program and dedicated service centres across the US toward non-oncologic indications for testing [ 13 ].…”

Section: Resultsmentioning

confidence: 99%

Effective and Efficient Delivery of Genome-Based Testing-What Conditions Are Necessary for Health System Readiness?

Husereau

Steuten

Muthu³

et al. 2022

Healthcare

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Health systems internationally must prepare for a future of genetic/genomic testing to inform healthcare decision-making while creating research opportunities. High functioning testing services will require additional considerations and health system conditions beyond traditional diagnostic testing. Based on a literature review of good practices, key informant interviews, and expert discussion, this article attempts to synthesize what conditions are necessary, and what good practice may look like. It is intended to aid policymakers and others designing future systems of genome-based care and care prevention. These conditions include creating communities of practice and healthcare system networks; resource planning; across-region informatics; having a clear entry/exit point for innovation; evaluative function(s); concentrated or coordinated service models; mechanisms for awareness and care navigation; integrating innovation and healthcare delivery functions; and revisiting approaches to financing, education and training, regulation, and data privacy and security. The list of conditions we propose was developed with an emphasis on describing conditions that would be applicable to any healthcare system, regardless of capacity, organizational structure, financing, population characteristics, standardization of care processes, or underlying culture.

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Section: Resultsmentioning

confidence: 99%

Effective and Efficient Delivery of Genome-Based Testing-What Conditions Are Necessary for Health System Readiness?

Husereau

Steuten

Muthu³

et al. 2022

Healthcare

View full text Add to dashboard Cite

show abstract

“…The VA serves more than 13 million veterans with more than 1600 points-of-care nationwide. Routine patient care does not typically incorporate factoring genetic data to make decision strategies (Dong et al, 2021 ). Several institutional initiatives are looking to incorporate genetic data (Zhang, 2016 ), but they are generally restricted to specific sites.…”

Section: Discussionmentioning

confidence: 99%

Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care

et al. 2022

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IntroductionA growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data.MethodThis study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction.ResultsThe final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration.DiscussionOver 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset.

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“…SLCO1B1 genotype information is increasingly being utilized to guide the selection and dosing of several statins, especially simvastatin 15–17 . Genetic variation in another transporter, ABCG2 , and the gene encoding the drug metabolizing enzyme CYP2C9 have also been shown to be relevant for statin prescribing in addition to SLCO1B1 genotype 18 .…”

Section: Figurementioning

confidence: 99%

PharmVar GeneFocus: SLCO1B1

Ramsey

Gong

Lee

et al. 2022

Clin Pharma and Therapeutics

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The Pharmacogene Variation Consortium (PharmVar) is now providing star (*) allele nomenclature for the highly polymorphic human SLCO1B1 gene encoding the organic anion transporting polypeptide 1B1 (OATP1B1) drug transporter. Genetic variation within the SLCO1B1 gene locus impacts drug transport, which can lead to altered pharmacokinetic profiles of several commonly prescribed drugs. Variable OATP1B1 function is of particular importance regarding hepatic uptake of statins and the risk of statin‐associated musculoskeletal symptoms. To introduce this important drug transporter gene into the PharmVar database and serve as a unified reference of haplotype variation moving forward, an international group of gene experts has performed an extensive review of all published SLCO1B1 star alleles. Previously published star alleles were self‐assigned by authors and only loosely followed the star nomenclature system that was first developed for cytochrome P450 genes. This nomenclature system has been standardized by PharmVar and is now applied to other important pharmacogenes such as SLCO1B1. In addition, data from the 1000 Genomes Project and investigator‐submitted data were utilized to confirm existing haplotypes, fill knowledge gaps, and/or define novel star alleles. The PharmVar‐developed SLCO1B1 nomenclature has been incorporated by the Clinical Pharmacogenetics Implementation Consortium (CPIC) 2022 guideline on statin‐associated musculoskeletal symptoms.

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Veterans Affairs Pharmacogenomic Testing for Veterans (PHASER) clinical program

Cited by 23 publications

References 11 publications

Effective and Efficient Delivery of Genome-Based Testing-What Conditions Are Necessary for Health System Readiness?

Effective and Efficient Delivery of Genome-Based Testing-What Conditions Are Necessary for Health System Readiness?

Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care

PharmVar GeneFocus: SLCO1B1

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