Preemptive Genotyping for Personalized Medicine: Design of the Right Drug, Right Dose, Right Time—Using Genomic Data to Individualize Treatment Protocol
Abstract 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 pharmacogenom...
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| creator | Bielinski, Suzette J., PhD, MEd Olson, Janet E., PhD Pathak, Jyotishman, PhD Weinshilboum, Richard M., MD Wang, Liewei, MD, PhD Lyke, Kelly J Ryu, Euijung, PhD Targonski, Paul V., MD, PhD Van Norstrand, Michael D., MD, PhD Hathcock, Matthew A., MS Takahashi, Paul Y., MD McCormick, Jennifer B., PhD, MPP Johnson, Kiley J., MS, CGC Maschke, Karen J., PhD Rohrer Vitek, Carolyn R., MS Ellingson, Marissa S., MS, CGC Wieben, Eric D., PhD Farrugia, Gianrico, MD Morrisette, Jody A., MBA Kruckeberg, Keri J., BA Bruflat, Jamie K., MS Peterson, Lisa M., BS Blommel, Joseph H., MS Skierka, Jennifer M., BS Ferber, Matthew J., PhD Black, John L., MD Baudhuin, Linnea M., PhD Klee, Eric W., PhD Ross, Jason L., MA Veldhuizen, Tamra L., BS Schultz, Cloann G., MS Caraballo, Pedro J., MD Freimuth, Robert R., PhD Chute, Christopher G., MD, DrPH Kullo, Iftikhar J., MD |
| description | Abstract 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. |
| doi_str_mv | 10.1016/j.mayocp.2013.10.021 |
| format | Article |
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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.</description><identifier>ISSN: 0025-6196</identifier><identifier>EISSN: 1942-5546</identifier><identifier>DOI: 10.1016/j.mayocp.2013.10.021</identifier><identifier>PMID: 24388019</identifier><identifier>CODEN: MACPAJ</identifier><language>eng</language><publisher>England: Elsevier Inc</publisher><subject>Atherosclerosis - drug therapy ; Cohort Studies ; Decision Making ; Diabetes Mellitus - drug therapy ; Dyslipidemias - drug therapy ; Electronic Health Records ; Female ; Genetic research ; Genetic Testing - standards ; Genetic variation ; Genotype ; Genotyping Techniques ; Hematopoiesis - drug effects ; Humans ; Hydroxymethylglutaryl-CoA Reductase Inhibitors - therapeutic use ; Hypertension - drug therapy ; Identification and classification ; Internal Medicine ; Male ; Methods ; Middle Aged ; Pharmacogenetics ; Pharmacogenetics - methods ; Pharmacogenetics - standards ; Pilot Projects ; Practice Guidelines as Topic ; Precision medicine ; Precision Medicine - methods ; Precision Medicine - standards ; Predictive Value of Tests ; United States</subject><ispartof>Mayo Clinic proceedings, 2014, Vol.89 (1), p.25-33</ispartof><rights>Mayo Foundation for Medical Education and Research</rights><rights>2014 Mayo Foundation for Medical Education and Research</rights><rights>Copyright © 2014 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.</rights><rights>COPYRIGHT 2014 Elsevier, Inc.</rights><rights>Copyright Mayo Foundation for Medical Education and Research Jan 2014</rights><rights>2013 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved. 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c663t-b0e891aa45d97819fff4df37a0a2c637922831691c1989956a77607511c1492c3</citedby><cites>FETCH-LOGICAL-c663t-b0e891aa45d97819fff4df37a0a2c637922831691c1989956a77607511c1492c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,4010,27900,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24388019$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bielinski, Suzette J., PhD, MEd</creatorcontrib><creatorcontrib>Olson, Janet E., PhD</creatorcontrib><creatorcontrib>Pathak, Jyotishman, PhD</creatorcontrib><creatorcontrib>Weinshilboum, Richard M., MD</creatorcontrib><creatorcontrib>Wang, Liewei, MD, PhD</creatorcontrib><creatorcontrib>Lyke, Kelly J</creatorcontrib><creatorcontrib>Ryu, Euijung, PhD</creatorcontrib><creatorcontrib>Targonski, Paul V., MD, PhD</creatorcontrib><creatorcontrib>Van Norstrand, Michael D., MD, PhD</creatorcontrib><creatorcontrib>Hathcock, Matthew A., MS</creatorcontrib><creatorcontrib>Takahashi, Paul Y., MD</creatorcontrib><creatorcontrib>McCormick, Jennifer B., PhD, MPP</creatorcontrib><creatorcontrib>Johnson, Kiley J., MS, CGC</creatorcontrib><creatorcontrib>Maschke, Karen J., PhD</creatorcontrib><creatorcontrib>Rohrer Vitek, Carolyn R., MS</creatorcontrib><creatorcontrib>Ellingson, Marissa S., MS, CGC</creatorcontrib><creatorcontrib>Wieben, Eric D., PhD</creatorcontrib><creatorcontrib>Farrugia, Gianrico, MD</creatorcontrib><creatorcontrib>Morrisette, Jody A., MBA</creatorcontrib><creatorcontrib>Kruckeberg, Keri J., BA</creatorcontrib><creatorcontrib>Bruflat, Jamie K., MS</creatorcontrib><creatorcontrib>Peterson, Lisa M., BS</creatorcontrib><creatorcontrib>Blommel, Joseph H., MS</creatorcontrib><creatorcontrib>Skierka, Jennifer M., BS</creatorcontrib><creatorcontrib>Ferber, Matthew J., PhD</creatorcontrib><creatorcontrib>Black, John L., MD</creatorcontrib><creatorcontrib>Baudhuin, Linnea M., PhD</creatorcontrib><creatorcontrib>Klee, Eric W., PhD</creatorcontrib><creatorcontrib>Ross, Jason L., MA</creatorcontrib><creatorcontrib>Veldhuizen, Tamra L., BS</creatorcontrib><creatorcontrib>Schultz, Cloann G., MS</creatorcontrib><creatorcontrib>Caraballo, Pedro J., MD</creatorcontrib><creatorcontrib>Freimuth, Robert R., PhD</creatorcontrib><creatorcontrib>Chute, Christopher G., MD, DrPH</creatorcontrib><creatorcontrib>Kullo, Iftikhar J., MD</creatorcontrib><title>Preemptive Genotyping for Personalized Medicine: Design of the Right Drug, Right Dose, Right Time—Using Genomic Data to Individualize Treatment Protocol</title><title>Mayo Clinic proceedings</title><addtitle>Mayo Clin Proc</addtitle><description>Abstract 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.</description><subject>Atherosclerosis - drug therapy</subject><subject>Cohort Studies</subject><subject>Decision Making</subject><subject>Diabetes Mellitus - drug therapy</subject><subject>Dyslipidemias - drug therapy</subject><subject>Electronic Health Records</subject><subject>Female</subject><subject>Genetic research</subject><subject>Genetic Testing - standards</subject><subject>Genetic variation</subject><subject>Genotype</subject><subject>Genotyping Techniques</subject><subject>Hematopoiesis - drug effects</subject><subject>Humans</subject><subject>Hydroxymethylglutaryl-CoA Reductase Inhibitors - therapeutic use</subject><subject>Hypertension - drug therapy</subject><subject>Identification and classification</subject><subject>Internal Medicine</subject><subject>Male</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Pharmacogenetics</subject><subject>Pharmacogenetics - methods</subject><subject>Pharmacogenetics - standards</subject><subject>Pilot Projects</subject><subject>Practice Guidelines as Topic</subject><subject>Precision medicine</subject><subject>Precision Medicine - methods</subject><subject>Precision Medicine - standards</subject><subject>Predictive Value of Tests</subject><subject>United 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(Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Mayo Clinic proceedings</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bielinski, Suzette J., PhD, MEd</au><au>Olson, Janet E., PhD</au><au>Pathak, Jyotishman, PhD</au><au>Weinshilboum, Richard M., MD</au><au>Wang, Liewei, MD, PhD</au><au>Lyke, Kelly J</au><au>Ryu, Euijung, PhD</au><au>Targonski, Paul V., MD, PhD</au><au>Van Norstrand, Michael D., MD, PhD</au><au>Hathcock, Matthew A., MS</au><au>Takahashi, Paul Y., MD</au><au>McCormick, Jennifer B., PhD, MPP</au><au>Johnson, Kiley J., MS, CGC</au><au>Maschke, Karen J., PhD</au><au>Rohrer Vitek, Carolyn R., MS</au><au>Ellingson, Marissa S., MS, CGC</au><au>Wieben, Eric D., PhD</au><au>Farrugia, Gianrico, MD</au><au>Morrisette, Jody A., MBA</au><au>Kruckeberg, Keri J., BA</au><au>Bruflat, Jamie K., MS</au><au>Peterson, Lisa M., BS</au><au>Blommel, Joseph H., MS</au><au>Skierka, Jennifer M., BS</au><au>Ferber, Matthew J., PhD</au><au>Black, John L., MD</au><au>Baudhuin, Linnea M., PhD</au><au>Klee, Eric W., PhD</au><au>Ross, Jason L., MA</au><au>Veldhuizen, Tamra L., BS</au><au>Schultz, Cloann G., MS</au><au>Caraballo, Pedro J., MD</au><au>Freimuth, Robert R., PhD</au><au>Chute, Christopher G., MD, DrPH</au><au>Kullo, Iftikhar J., MD</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Preemptive Genotyping for Personalized Medicine: Design of the Right Drug, Right Dose, Right Time—Using Genomic Data to Individualize Treatment Protocol</atitle><jtitle>Mayo Clinic proceedings</jtitle><addtitle>Mayo Clin Proc</addtitle><date>2014</date><risdate>2014</risdate><volume>89</volume><issue>1</issue><spage>25</spage><epage>33</epage><pages>25-33</pages><issn>0025-6196</issn><eissn>1942-5546</eissn><coden>MACPAJ</coden><abstract>Abstract 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.</abstract><cop>England</cop><pub>Elsevier Inc</pub><pmid>24388019</pmid><doi>10.1016/j.mayocp.2013.10.021</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0025-6196 |
| ispartof | Mayo Clinic proceedings, 2014, Vol.89 (1), p.25-33 |
| issn | 0025-6196 1942-5546 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_3932754 |
| source | MEDLINE; Alma/SFX Local Collection |
| subjects | Atherosclerosis - drug therapy Cohort Studies Decision Making Diabetes Mellitus - drug therapy Dyslipidemias - drug therapy Electronic Health Records Female Genetic research Genetic Testing - standards Genetic variation Genotype Genotyping Techniques Hematopoiesis - drug effects Humans Hydroxymethylglutaryl-CoA Reductase Inhibitors - therapeutic use Hypertension - drug therapy Identification and classification Internal Medicine Male Methods Middle Aged Pharmacogenetics Pharmacogenetics - methods Pharmacogenetics - standards Pilot Projects Practice Guidelines as Topic Precision medicine Precision Medicine - methods Precision Medicine - standards Predictive Value of Tests United States |
| title | Preemptive Genotyping for Personalized Medicine: Design of the Right Drug, Right Dose, Right Time—Using Genomic Data to Individualize Treatment Protocol |
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