TY - JOUR
T1 - Preemptive genotyping for personalized medicine
T2 - Design of the right drug, right dose, right timedusing genomic data to individualize treatment protocol
AU - Bielinski, Suzette J.
AU - Olson, Janet E.
AU - Pathak, Jyotishman
AU - Weinshilboum, Richard M.
AU - Wang, Liewei
AU - Lyke, Kelly J.
AU - Ryu, Euijung
AU - Targonski, Paul V.
AU - Van Norstrand, Michael D.
AU - Hathcock, Matthew A.
AU - Takahashi, Paul Y.
AU - McCormick, Jennifer B.
AU - Johnson, Kiley J.
AU - Maschke, Karen J.
AU - Rohrer Vitek, Carolyn R.
AU - Ellingson, Marissa S.
AU - Wieben, Eric D.
AU - Farrugia, Gianrico
AU - Morrisette, Jody A.
AU - Kruckeberg, Keri J.
AU - Bruflat, Jamie K.
AU - Peterson, Lisa M.
AU - Blommel, Joseph H.
AU - Skierka, Jennifer M.
AU - Ferber, Matthew J.
AU - Black, John L.
AU - Baudhuin, Linnea M.
AU - Klee, Eric W.
AU - Ross, Jason L.
AU - Veldhuizen, Tamra L.
AU - Schultz, Cloann G.
AU - Caraballo, Pedro J.
AU - Freimuth, Robert R.
AU - Chute, Christopher G.
AU - Kullo, Iftikhar J.
N1 - Funding Information:
Grant Support: This work was supported in part by the Mayo Clinic Center for Individualized Medicine, National Institutes of Health grants U19 GM61388 (The Pharmacogenomics Research Network), R01 GM28157 , U01 HG005137 , R01 CA138461 , R01 AG034676 (The Rochester Epidemiology Project [Principal Investigators: Walter A. Rocca, MD, and Barbara P. Yawn, MD, MSc]), and U01 HG06379 and U01 HG06379 Supplement (The Electronic Medical Record and Genomics [eMERGE] Network).
PY - 2014/1
Y1 - 2014/1
N2 - Objective: To report the design and implementation of the Right Drug, Right Dose, Right TimedUsing 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.
AB - Objective: To report the design and implementation of the Right Drug, Right Dose, Right TimedUsing 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.
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U2 - 10.1016/j.mayocp.2013.10.021
DO - 10.1016/j.mayocp.2013.10.021
M3 - Article
C2 - 24388019
AN - SCOPUS:84893097016
SN - 0025-6196
VL - 89
SP - 25
EP - 33
JO - Mayo Clinic proceedings
JF - Mayo Clinic proceedings
IS - 1
ER -