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OBJECTIVE: To understand patient characteristics related to acceptability of returning individual research results via various modalities, focusing on electronic visits (e-visits). PATIENTS AND METHODS: Twelve hundred participants from the Mayo Clinic Biobank were selected using a stratified random sampling approach based on sex, age, and education level. Mailed surveys ascertained return of results preferences for 2 disease vignettes (cystic fibrosis and hereditary breast cancer) and a pharmacogenomics vignette. The study was conducted from October 1, 2013, through March 31, 2014. RESULTS: In all, 685 patients (57%) responded, and 60% reported liking e-visits, although the option of receiving results in an office visit was liked most frequently. Multivariable logistic models showed that the odds of liking the use of e-visits for returning results for cystic fibrosis and hereditary breast cancer were higher among those with higher education and better genetic knowledge and among those not living in proximity to the Mayo Clinic (Rochester, Minnesota). Level of genetic knowledge was not considerably associated with accepting e-visits, whereas education level remained important. For all vignettes, those who are divorced were less likely to accept e-visits. CONCLUSION: Researchers are faced with a difficult challenge of returning results with a method that is both acceptable to recipients and logistically feasible. This study implies that the use of e-visits may be a viable option for return of results to stratify the chasm between in-person genetic counseling and online portal receipt of results.
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OBJECTIVE: To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR). PATIENTS AND METHODS: We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR. RESULTS: The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance. CONCLUSION: This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.