Purpose Use of genomic sequencing is increasing at a pace that requires technological solutions to effectively meet the needs of a growing patient population. We developed GUÍA, a web-based application, to enhance the delivery of genomic results and related clinical information to patients and families. Methods GUÍA development occurred in five overlapping phases: formative research, content development, stakeholder/community member input, user interface design, and web application development. Development was informed by formative qualitative research involving parents ( N = 22) whose children underwent genomic testing. Participants enrolled in the NYCKidSeq pilot study ( N = 18) completed structured feedback interviews post–result disclosure using GUÍA. Genetic specialists, researchers, patients, and community stakeholders provided their perspectives on GUÍA’s design to ensure technical, cultural, and literacy appropriateness. Results NYCKidSeq participants responded positively to the use of GUÍA to deliver their children’s results. All participants ( N = 10) with previous experience with genetic testing felt GUÍA improved result disclosure, and 17 (94%) participants said the content was clear. Conclusion GUÍA communicates complex genomic information in an understandable and personalized manner. Initial piloting demonstrated GUÍA’s utility for families enrolled in the NYCKidSeq pilot study. Findings from the NYCKidSeq clinical trial will provide insight into GUÍA’s effectiveness in communicating results among diverse, multilingual populations.
For almost 50 years, the Icahn School of Medicine at Mount Sinai has continually invested in genetics and genomics, facilitating a healthy ecosystem that provides widespread support for the ongoing programs in translational pharmacogenomics. These programs can be broadly cataloged into discovery, education, clinical implementation and testing, which are collaboratively accomplished by multiple departments, institutes, laboratories, companies and colleagues. Focus areas have included drug response association studies and allele discovery, multiethnic pharmacogenomics, personalized genotyping and survey-based education programs, pre-emptive clinical testing implementation and novel assay development. This overview summarizes the current state of translational pharmacogenomics at Mount Sinai, including a future outlook on the forthcoming expansions in overall support, research and clinical programs, genomic technology infrastructure and the participating faculty.
Implementation of pharmacogenetic-guided warfarin dosing has been hindered by inconsistent results from reported clinical trials and a lack of available algorithms that include alleles prevalent in non-white populations. However, current evidence indicates that algorithm-guided dosing is more accurate than empirical dosing. To facilitate multiethnic algorithm-guided warfarin dosing using preemptive genetic testing, we developed a strategy that accounts for the complexity of race and leverages electronic health records for algorithm variables and deploying point-of-care dose recommendations.
Purpose: Use of genomic sequencing is increasing at a pace that requires technological solutions to effectively meet the needs of a growing patient population. We developed GUÍA, a web-based application, to enhance the delivery of genomic results and related clinical information to patients and families. Methods: GUÍA development occurred in 5 phases: formative research, content development, user interface design, stakeholder/community member input, and web application development. Development was informed by qualitative research involving parents (N=22) whose children underwent genomic testing. Participants enrolled in the NYCKidSeq pilot study (N=18) completed structured feedback interviews post-result disclosure using GUÍA. Genetic specialists, researchers, patients, and community stakeholders provided their perspectives on GUÍA's design to ensure technical, cultural, and literacy appropriateness. Results: NYCKidSeq participants responded positively to the use of GUÍA to deliver their children's results. All participants (N=10) with previous experience with genetic testing felt GUÍA improved result disclosure, and 17 (94%) participants said the content was clear. Conclusions: GUÍA communicates complex genomic information in an understandable and personalized manner. Initial piloting demonstrated GUÍA's utility for families enrolled NYCKidSeq pilot study. Findings from the NYCKidSeq clinical trial will provide insight into GUÍA's effectiveness in communicating results among diverse, multilingual populations.
Pharmacogenetic implementation programs are increasingly feasible due to the availability of clinical guidelines for implementation research. The utilization of these resources has been reported with selected drug-gene pairs; however, little is known about how prescribers respond to pharmacogenetic recommendations for statin therapy. We prospectively assessed prescriber interaction with point-of-care clinical decision support (CDS) to guide simvastatin therapy for a diverse cohort of primary care patients enrolled in a clinical pharmacogenetics program. Of the 1,639 preemptively genotyped patients, 298 (18.2%) had an intermediate function (IF) OATP1B1 phenotype and 25 (1.53%) had a poor function (PF) phenotype, predicted by a common single nucleotide variant in the SLCO1B1 gene (c.521T>C; rs4149056). Clinicians were presented with CDS when simvastatin was prescribed for patients with IF or PF through the electronic health record. Importantly, 64.2% of the CDS deployed at the point-of-care was accepted by the prescribers and resulted in prescription changes. Statin intensity was found to significantly influence prescriber adoption of the pharmacogenetic-guided CDS, whereas patient gender or race, prescriber type, or pharmacogenetic training status did not significantly influence adoption. This study demonstrates that primary care providers readily adopt pharmacogenetic information to guide statin therapy for the majority of patients with preemptive genotype data.
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