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Kraus MB, Bingham JS, Kekic A, Erickson C, Grilli CB, Seamans DP, Upjohn DP, Hentz JG, Clarke HD, Spangehl MJ. Does Preoperative Pharmacogenomic Testing of Patients Undergoing TKA Improve Postoperative Pain? A Randomized Trial. Clin Orthop Relat Res 2024; 482:291-300. [PMID: 37594401 PMCID: PMC10776165 DOI: 10.1097/corr.0000000000002767] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/09/2023] [Indexed: 08/19/2023]
Abstract
BACKGROUND Pharmacogenomics is an emerging and affordable tool that may improve postoperative pain control. One challenge to successful pain control is the large interindividual variability among analgesics in their efficacy and adverse drug events. Whether preoperative pharmacogenomic testing is worthwhile for patients undergoing TKA is unclear. QUESTIONS/PURPOSES (1) Are the results of preoperative pharmacogenetic testing associated with lower postoperative pain scores as measured by the Overall Benefit of Analgesic Score (OBAS)? (2) Do the results of preoperative pharmacogenomic testing lead to less total opioids given? (3) Do the results of preoperative pharmacogenomic testing lead to changes in opioid prescribing patterns? METHODS Participants of this randomized trial were enrolled from September 2018 through December 2021 if they were aged 18 to 80 years and were undergoing primary TKA under general anesthesia. Patients were excluded if they had chronic kidney disease, a history of chronic pain or narcotic use before surgery, or if they were undergoing robotic surgery. Preoperatively, patients completed pharmacogenomic testing (RightMed, OneOME) and a questionnaire and were randomly assigned to the experimental group or control group. Of 99 patients screened, 23 were excluded, one before randomization; 11 allocated patients in each group did not receive their allocated interventions for reasons such as surgery canceled, patients ultimately undergoing spinal anesthesia, and change in surgery plan. Another four patients in each group were excluded from the analysis because they were missing an OBAS report. This left 30 patients for analysis in the control group and 38 patients in the experimental group. The control and experimental groups were similar in age, gender, and race. Pharmacogenomic test results for patients in the experimental group were reviewed before surgery by a pharmacist, who recommended perioperative medications to the clinical team. A pharmacist also assessed for clinically relevant drug-gene interactions and recommended drug and dose selection according to guidelines from the Clinical Pharmacogenomics Implementation Consortium for each patient enrolled in the study. Patients were unaware of their pharmacogenomic results. Pharmacogenomic test results for patients in the control group were not reviewed before surgery; instead, standard perioperative medications were administered in adherence to our institutional care pathways. The OBAS (maximum 28 points) was the primary outcome measure, recorded 24 hours postoperatively. A two-sample t-test was used to compare the mean OBAS between groups. Secondary measures were the mean 24-hour pain score, total morphine milligram equivalent, and frequency of opioid use. Postoperatively, patients were assessed for pain with a VAS (range 0 to 10). Opioid use was recorded preoperatively, intraoperatively, in the postanesthesia care unit, and 24 hours after discharge from the postanesthesia care unit. Changes in perioperative opioid use based on pharmacogenomic testing were recorded, as were changes in prescription patterns for postoperative pain control. Preoperative characteristics were also compared between patients with and without various phenotypes ascertained from pharmacogenomic test results. RESULTS The mean OBAS did not differ between groups (mean ± SD 4.7 ± 3.7 in the control group versus 4.2 ± 2.8 in the experimental group, mean difference 0.5 [95% CI -1.1 to 2.1]; p = 0.55). Total opioids given did not differ between groups or at any single perioperative timepoint (preoperative, intraoperative, or postoperative). We found no difference in opioid prescribing pattern. After adjusting for multiple comparisons, no difference was observed between the treatment and control groups in tramadol use (41% versus 71%, proportion difference 0.29 [95% CI 0.05 to 0.53]; nominal p = 0.02; adjusted p > 0.99). CONCLUSION Routine use of pharmacogenomic testing for patients undergoing TKA did not lead to better pain control or decreased opioid consumption. Future studies might focus on at-risk populations, such as patients with chronic pain or those undergoing complex, painful surgical procedures, to test whether pharmacogenomic results might be beneficial in certain circumstances. LEVEL OF EVIDENCE Level I, therapeutic study.
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Affiliation(s)
- Molly B. Kraus
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Phoenix, AZ, USA
| | | | | | - Colby Erickson
- Arizona College of Osteopathic Medicine, Midwestern University, Glendale, AZ, USA
| | | | - David P. Seamans
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - David P. Upjohn
- Center for Regenerative Biotherapeutics, Mayo Clinic, Phoenix, AZ, USA
| | - Joseph G. Hentz
- Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, AZ, USA
| | - Henry D. Clarke
- Department of Orthopedic Surgery, Mayo Clinic, Phoenix, AZ, USA
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Kodida R, Reble E, Clausen M, Shickh S, Mighton C, Sam J, Forster N, Panchal S, Aronson M, Semotiuk K, Graham T, Silberman Y, Randall Armel S, McCuaig JM, Cohn I, Morel CF, Elser C, Eisen A, Carroll JC, Glogowski E, Schrader KA, Di Gioacchino V, Lerner-Ellis J, Kim RH, Bombard Y. A model for the return and referral of all clinically significant secondary findings of genomic sequencing. J Med Genet 2023; 60:733-739. [PMID: 37217257 DOI: 10.1136/jmg-2022-109091] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/19/2023] [Indexed: 05/24/2023]
Abstract
Secondary findings (SFs) identified through genomic sequencing (GS) can offer a wide range of health benefits to patients. Resource and capacity constraints pose a challenge to their clinical management; therefore, clinical workflows are needed to optimise the health benefits of SFs. In this paper, we describe a model we created for the return and referral of all clinically significant SFs, beyond medically actionable results, from GS. As part of a randomised controlled trial evaluating the outcomes and costs of disclosing all clinically significant SFs from GS, we consulted genetics and primary care experts to determine a feasible workflow to manage SFs. Consensus was sought to determine appropriate clinical recommendations for each category of SF and which clinician specialist would provide follow-up care. We developed a communication and referral plan for each category of SFs. This involved referrals to specialised clinics, such as an Adult Genetics clinic, for highly penetrant medically actionable findings. Common and non-urgent SFs, such as pharmacogenomics and carrier status results for non-family planning participants, were directed back to the family physician (FP). SF results and recommendations were communicated directly to participants to respect autonomy and to their FPs to support follow-up of SFs. We describe a model for the return and referral of all clinically significant SFs to facilitate the utility of GS and promote the health benefits of SFs. This may serve as a model for others returning GS results transitioning participants from research to clinical settings.
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Affiliation(s)
- Rita Kodida
- Genomics Health Services & Policy Research Program, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Emma Reble
- Genomics Health Services & Policy Research Program, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Marc Clausen
- Genomics Health Services & Policy Research Program, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Salma Shickh
- Genomics Health Services & Policy Research Program, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management & Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Chloe Mighton
- Genomics Health Services & Policy Research Program, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management & Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Jordan Sam
- Genomics Health Services & Policy Research Program, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Nicole Forster
- Fred A. Litwin Family Centre in Genetic Medicine, University Health Network, Toronto, Ontario, Canada
| | - Seema Panchal
- The Marvelle Koffler Breast Centre, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Melyssa Aronson
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada
| | - Kara Semotiuk
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada
| | - Tracy Graham
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Yael Silberman
- Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Susan Randall Armel
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Division of Medical Oncology & Hematology, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada
| | - Jeanna M McCuaig
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Division of Medical Oncology & Hematology, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada
| | - Iris Cohn
- Division of Clinical Pharmacology & Toxicology, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Chantal F Morel
- Fred A. Litwin Family Centre in Genetic Medicine, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Christine Elser
- The Marvelle Koffler Breast Centre, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Andrea Eisen
- Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - June C Carroll
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
- Granovsky Gluskin Family Medicine Centre, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada
| | | | - Kasmintan A Schrader
- British Columbia Cancer Agency, Vancouver, British Columbia, Canada
- Department of Medical Genetics, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Vanessa Di Gioacchino
- The Marvelle Koffler Breast Centre, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada
- Pathology & Laboratory Medicine, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada
| | - Jordan Lerner-Ellis
- Pathology & Laboratory Medicine, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada
| | - Raymond H Kim
- Division of Medical Oncology & Hematology, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Yvonne Bombard
- Genomics Health Services & Policy Research Program, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management & Evaluation, University of Toronto, Toronto, Ontario, Canada
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Ramsey LB, Prows CA, Chidambaran V, Sadhasivam S, Quinn CT, Teusink-Cross A, Tang Girdwood S, Dawson DB, Vinks AA, Glauser TA. Implementation of CYP2D6-guided opioid therapy at Cincinnati Children's Hospital Medical Center. Am J Health Syst Pharm 2023; 80:852-859. [PMID: 36715063 PMCID: PMC11004919 DOI: 10.1093/ajhp/zxad025] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Indexed: 01/31/2023] Open
Abstract
PURPOSE We describe the implementation of CYP2D6-focused pharmacogenetic testing to guide opioid prescribing in a quaternary care, nonprofit pediatric academic medical center. SUMMARY Children are often prescribed oral opioids after surgeries, for cancer pain, and occasionally for chronic pain. In 2004, Cincinnati Children's Hospital Medical Center implemented pharmacogenetic testing for CYP2D6 metabolism phenotype to inform codeine prescribing. The test and reports were updated to align with changes over time in the testing platform, the interpretation of genotype to phenotype, the electronic health record, and Food and Drug Administration (FDA) guidance. The use of the test increased when a research project required testing and decreased as prescribing of oxycodone increased due to FDA warnings about codeine. Education about the opioid-focused pharmacogenetic test was provided to prescribers (eg, the pain and sickle cell teams) as well as patients and families. Education and electronic health record capability increased provider compliance with genotype-guided postsurgical prescribing of oxycodone, although there was a perceived lack of utility for oxycodone prescribing. CONCLUSION The implementation of pharmacogenetic testing to inform opioid prescribing for children has evolved with accumulating evidence and guidelines, requiring changes in reporting of results and recommendations.
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Affiliation(s)
- Laura B Ramsey
- Department of Pediatrics, Division of Clinical Pharmacology and Division of Research in Patient Services, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Cynthia A Prows
- Division of Human Genetics and Division of Patient Services, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Vidya Chidambaran
- Department of Anesthesiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Senthilkumar Sadhasivam
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Charles T Quinn
- Department of Pediatrics and Division of Hematology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Ashley Teusink-Cross
- Division of Pharmacy, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Sonya Tang Girdwood
- Department of Pediatrics, Division of Clinical Pharmacology; and Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - D Brian Dawson
- Department of Pediatrics and Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Alexander A Vinks
- Department of Pediatrics, Division of Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Tracy A Glauser
- Department of Pediatrics, Division of Neurology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- College of Medicine, University of Cincinnati, Cincinnati, OH, USA
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Williams GR, Tsongalis GJ, Lewis LD, Barney RE, Cook LJ, Geno KA, Nerenz RD. Potential Impact of Pharmacogenomic Single Nucleotide Variants in a Rural Caucasian Population. J Appl Lab Med 2023; 8:251-263. [PMID: 36611001 PMCID: PMC10539040 DOI: 10.1093/jalm/jfac091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/15/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND In the US adverse drug reactions (ADRs) are estimated to cause 100 000 fatalities and cost over $136 billion annually. A patient's genes play a significant role in their response to a drug. Pharmacogenomics aims to optimize drug choice and dose for individual patients by characterizing patients' pharmacologically relevant genes to identify variants of known impact. METHODS DNA was extracted from randomly selected remnant whole blood samples from Caucasian patients with previously performed complete blood counts. Samples were genotyped by mass spectrometry using a customized pharmacogenomics panel. A third-party result interpretation service used genotypic results to predict likely individual responses to frequently prescribed drugs. RESULTS Complete genotypic and phenotypic calls for all tested Cytochrome P450 isoenzymes and other genes were obtained from 152 DNA samples. Of these 152 unique genomic DNA samples, 140 had genetic variants suggesting dose adjustment for at least one drug. Cardiovascular and psychiatry drugs had the highest number of recommendations, which included United States Food and Drug Administration warnings for highly prescribed drugs metabolized by CYP2C19, CYP2C9, CYP2D6, HLA-A, and VKORC1. CONCLUSIONS Risk for each drug:gene pairing primarily depends upon the degree of predicted enzyme impairment or activation, width of the therapeutic window, and whether parent compound or metabolite is pharmacologically active. The resulting metabolic variations range from risk of toxicity to therapeutic failure. Pharmacogenomic profiling likely reduces ADR potential by allowing up front drug/dose selection to fit a patient's unique drug-response profile.
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Affiliation(s)
- Grace R. Williams
- Department of Pathology, Virginia Commonwealth University Health System, Richmond, VA, USA
| | - Gregory J. Tsongalis
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Lionel D. Lewis
- Department of Medicine, The Geisel School of Medicine at Dartmouth and Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Rachael E. Barney
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Leanne J. Cook
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - K. Aaron Geno
- Department of Pathology, Texas Tech University Health Sciences Center El Paso, El Paso, TX, USA
| | - Robert D. Nerenz
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
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Bai H, Zhang X, Bush WS. Pharmacogenomic and Statistical Analysis. Methods Mol Biol 2023; 2629:305-330. [PMID: 36929083 DOI: 10.1007/978-1-0716-2986-4_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Genetic variants can alter response to drugs and other therapeutic interventions. The study of this phenomenon, called pharmacogenomics, is similar in many ways to other types of genetic studies but has distinct methodological and statistical considerations. Genetic variants involved in the processing of exogenous compounds exhibit great diversity and complexity, and the phenotypes studied in pharmacogenomics are also more complex than typical genetic studies. In this chapter, we review basic concepts in pharmacogenomic study designs, data generation techniques, statistical analysis approaches, and commonly used methods and briefly discuss the ultimate translation of findings to clinical care.
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Affiliation(s)
- Haimeng Bai
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
- Department of Nutrition, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Xueyi Zhang
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - William S Bush
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.
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Cicali EJ, Lemke L, Al Alshaykh H, Nguyen K, Cavallari LH, Wiisanen K. How to Implement a Pharmacogenetics Service at your Institution. JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY 2022; 5:1161-1175. [PMID: 36589694 PMCID: PMC9799247 DOI: 10.1002/jac5.1699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 07/29/2022] [Indexed: 01/05/2023]
Abstract
The vast majority of patients possess one or more pharmacogenetic variants that can influence optimal medication use. When pharmacogenetic data are used to guide drug choice and dosing, evidence points to improved disease outcomes, fewer adverse effects, and lower healthcare spending. Although its science is well established, clinical use of pharmacogenetic data to guide drug therapy is still in its infancy. Pharmacogenetics essentially involves the intersection of an individual's genetic data with their medications, which makes pharmacists uniquely qualified to provide clinical support and education in this field. In fact, most pharmacogenetics implementations, to date, have been led by pharmacists as leaders or members of a multidisciplinary team or as individual practitioners. A successful large-scale pharmacogenetics implementation requires coordination and synergy among administrators, clinicians, informatics teams, laboratories, and patients. Because clinical implementation of pharmacogenetics is in its early stages, there is an urgent need for guidance and dissemination of shared experiences to provide a framework for clinicians. Many early adopters of pharmacogenetics have explored various strategies among diverse practice settings. This article relies on the experiences of early adopters to provide guidance for critical steps along the pathway to implementation, including strategies to engage stakeholders; evaluate pharmacogenetic evidence; coordinate laboratory testing, results interpretation and their integration into the electronic health record; identify reimbursement avenues; educate providers and patients; and maintain a successful program. Learning from early adopters' published experiences and strategies can allow clinicians leading a new pharmacogenetics implementation to avoid pitfalls and adapt and apply lessons learned by others to their own practice.
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Affiliation(s)
- Emily J Cicali
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Fl, USA
| | - Lauren Lemke
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Fl, USA
| | - Hana Al Alshaykh
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Fl, USA
| | - Khoa Nguyen
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
| | - Larisa H Cavallari
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Fl, USA
| | - Kristin Wiisanen
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Fl, USA
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Koleva-Kolarova R, Buchanan J, Vellekoop H, Huygens S, Versteegh M, Mölken MRV, Szilberhorn L, Zelei T, Nagy B, Wordsworth S, Tsiachristas A. Financing and Reimbursement Models for Personalised Medicine: A Systematic Review to Identify Current Models and Future Options. APPLIED HEALTH ECONOMICS AND HEALTH POLICY 2022; 20:501-524. [PMID: 35368231 PMCID: PMC9206925 DOI: 10.1007/s40258-021-00714-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/28/2021] [Indexed: 05/31/2023]
Abstract
BACKGROUND The number of healthcare interventions described as 'personalised medicine' (PM) is increasing rapidly. As healthcare systems struggle to decide whether to fund PM innovations, it is unclear what models for financing and reimbursement are appropriate to apply in this context. OBJECTIVE To review financing and reimbursement models for PM, summarise their key characteristics, and describe whether they can influence the development and uptake of PM. METHODS A literature review was conducted in Medline, Embase, Web of Science, and Econlit to identify studies published in English between 2009 and 2021, and reviews published before 2009. Grey literature was identified through Google Scholar, Google and subject-specific webpages. Articles that described financing and reimbursement of PM, and financing of non-PM were included. Data were extracted and synthesised narratively to report on the models, as well as facilitators, incentives, barriers and disincentives that could influence PM development and uptake. RESULTS One hundred and fifty-three papers were included. Research and development of PM was financed through both public and private sources and reimbursed largely through traditional models such as single fees, Diagnosis-Related Groups, and bundled payments. Financial-based reimbursement, including rebates and price-volume agreements, was mainly applied to targeted therapies. Performance-based reimbursement was identified mainly for gene and targeted therapies, and some companion diagnostics. Gene therapy manufacturers offered outcome-based rebates for treatment failure for interventions including Luxturna®, Kymriah®, Yescarta®, Zynteglo®, Zolgensma® and Strimvelis®, and coverage with evidence development for Kymriah® and Yescarta®. Targeted testing with OncotypeDX® was granted value-based reimbursement through initial coverage with evidence development. The main barriers and disincentives to PM financing and reimbursement were the lack of strong links between stakeholders and the lack of demonstrable benefit and value of PM. CONCLUSIONS Public-private financing agreements and performance-based reimbursement models could help facilitate the development and uptake of PM interventions with proven clinical benefit.
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Affiliation(s)
| | - James Buchanan
- Health Economics Research Centre, University of Oxford, Oxford, UK
| | - Heleen Vellekoop
- Institute for Medical Technology Assessment, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands
| | - Simone Huygens
- Institute for Medical Technology Assessment, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands
| | - Matthijs Versteegh
- Institute for Medical Technology Assessment, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands
| | - Maureen Rutten-van Mölken
- Institute for Medical Technology Assessment, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - László Szilberhorn
- Syreon Research Institute, Budapest, Hungary
- Faculty of Social Sciences, Eötvös Loránd University, Budapest, Hungary
| | - Tamás Zelei
- Syreon Research Institute, Budapest, Hungary
| | - Balázs Nagy
- Syreon Research Institute, Budapest, Hungary
| | - Sarah Wordsworth
- Health Economics Research Centre, University of Oxford, Oxford, UK
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, Oxford, UK
| | - Apostolos Tsiachristas
- Health Economics Research Centre, University of Oxford, Oxford, UK
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, Oxford, UK
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Bousman CA, Oomen A, Jessel CD, Tampi RR, Forester BP, Eyre HA, Lavretsky H, Müller DJ. Perspectives on the Clinical Use of Pharmacogenetic Testing in Late-Life Mental Healthcare: A Survey of the American Association of Geriatric Psychiatry Membership. Am J Geriatr Psychiatry 2022; 30:560-571. [PMID: 34740522 DOI: 10.1016/j.jagp.2021.09.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE To assess perspectives on pharmacogenetic (PGx) testing among members of the American Association of Geriatric Psychiatry (AAGP). DESIGN Cross-sectional survey. PARTICIPANTS Members of the AAGP. MEASUREMENTS Anonymous web-based survey consisting of 41 items covering experiences, indications, barriers, facilitators and ethical, legal and social implications for PGx testing. RESULTS A total of 124 surveys were completed (response rate = 13%). Most respondents (60%) had used PGx testing but an equal proportion (58%) was uncertain about the clinical usefulness of PGx testing in late-life mental health. Despite self-reported confidence in the ability to order and interpret PGx testing, 60% of respondents felt there was not enough clinical evidence for them to use PGx testing in their practice. This was compounded by uncertainties related to their ethical obligation and legal liability when interpreting and using (or not using) PGx testing results. Respondents strongly affirmed that clinical and legal guidelines for PGx testing in older adults are needed and would be helpful. CONCLUSION The findings suggest additional PGx research and physician education in late-life mental healthcare settings is required to reconcile uncertainties related to the clinical efficacy and ethico-legal aspects of PGx testing as well as address current knowledge barriers to testing uptake. These efforts would be further facilitated by the development of clinical practice guidelines to ensure equitable access to testing and standardized implementation of PGx-informed prescribing in older adults.
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Affiliation(s)
- Chad A Bousman
- Department of Medical Genetics, University of Calgary (CAB), Calgary, AB, Canada; Department of Psychiatry, University of Calgary (CAB), Calgary, AB, Canada; Department of Physiology & Pharmacology, University of Calgary (CAB), Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary (CAB, AO), Calgary, AB, Canada; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary (CAB, CDJ), Calgary, AB, Canada.
| | - Anita Oomen
- Alberta Children's Hospital Research Institute, University of Calgary (CAB, AO), Calgary, AB, Canada
| | - Chaten D Jessel
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary (CAB, CDJ), Calgary, AB, Canada
| | - Rajesh R Tampi
- Department of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University (RRT), Cleveland, OH; Department of Psychiatry, Yale School of Medicine (RRT), New Haven, CT
| | - Brent P Forester
- Division of Geriatric Psychiatry, McLean Hospital (BPF), Belmont, MA; Harvard Medical School (BPF), Boston, MA
| | - Harris A Eyre
- Neuroscience-inspired Policy Initiative, Organisation for Economic Co-Operation and Development (OECD) and PRODEO Institute (HAE), Paris, France; Institute for Mental Health and Physical Health and Clinical Translation (IMPACT), Deakin University (HAE), Geelong, Victoria, Australia; Global Brain Health Institute, University of California, San Francisco and Trinity College Dublin (HAE), Dublin, Ireland; Department of Psychiatry, Baylor College of Medicine (HAE), Houston, TX
| | - Helen Lavretsky
- Department of Psychiatry, Semel Institute for Neuroscience, University of California Los Angeles (HL), Los Angeles, CA
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto (DJM), Toronto, Ontario, Canada; Centre for Addiction and Mental Health, University of Toronto (DJM), Toronto, Ontario, Canada
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Silva P, Dahlke DV, Smith ML, Charles W, Gomez J, Ory MG, Ramos KS. An Idealized Clinicogenomic Registry to Engage Underrepresented Populations Using Innovative Technology. J Pers Med 2022; 12:jpm12050713. [PMID: 35629136 PMCID: PMC9144063 DOI: 10.3390/jpm12050713] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/18/2022] [Accepted: 04/26/2022] [Indexed: 11/26/2022] Open
Abstract
Current best practices in tumor registries provide a glimpse into a limited time frame over the natural history of disease, usually a narrow window around diagnosis and biopsy. This creates challenges meeting public health and healthcare reimbursement policies that increasingly require robust documentation of long-term clinical trajectories, quality of life, and health economics outcomes. These challenges are amplified for underrepresented minority (URM) and other disadvantaged populations, who tend to view the institution of clinical research with skepticism. Participation gaps leave such populations underrepresented in clinical research and, importantly, in policy decisions about treatment choices and reimbursement, thus further augmenting health, social, and economic disparities. Cloud computing, mobile computing, digital ledgers, tokenization, and artificial intelligence technologies are powerful tools that promise to enhance longitudinal patient engagement across the natural history of disease. These tools also promise to enhance engagement by giving participants agency over their data and addressing a major impediment to research participation. This will only occur if these tools are available for use with all patients. Distributed ledger technologies (specifically blockchain) converge these tools and offer a significant element of trust that can be used to engage URM populations more substantively in clinical research. This is a crucial step toward linking composite cohorts for training and optimization of the artificial intelligence tools for enhancing public health in the future. The parameters of an idealized clinical genomic registry are presented.
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Affiliation(s)
- Patrick Silva
- Health Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA; (J.G.); (K.S.R.)
- Correspondence: ; Tel.: +1-979-436-9055
| | - Deborah Vollmer Dahlke
- School of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA; (D.V.D.); (M.L.S.); (M.G.O.)
| | - Matthew Lee Smith
- School of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA; (D.V.D.); (M.L.S.); (M.G.O.)
| | - Wendy Charles
- BurstIQ, 9635 Maroon Circle, #310, Englewood, CO 80112, USA;
| | - Jorge Gomez
- Health Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA; (J.G.); (K.S.R.)
| | - Marcia G. Ory
- School of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA; (D.V.D.); (M.L.S.); (M.G.O.)
| | - Kenneth S. Ramos
- Health Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA; (J.G.); (K.S.R.)
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10
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Formea CM, Schultz AJ, Empey PE. Pharmacists Closing Health Disparity Gaps through Pharmacogenomics. JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY 2022. [DOI: 10.1002/jac5.1629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Christine M. Formea
- Intermountain Healthcare, Department of Pharmacy Services Salt Lake City Utah
- Intermountain Precision Genomics, Intermountain Healthcare St. George Utah
| | - April J. Schultz
- Sanford Imagenetics, Sanford Health Sioux Falls South Dakota
- Sanford USD School of Medicine University of South Dakota Sioux Falls South Dakota
| | - Philip E. Empey
- School of Pharmacy University of Pittsburgh Pittsburgh Pennsylvania
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11
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The Value of Pharmacogenetics to Reduce Drug-Related Toxicity in Cancer Patients. Mol Diagn Ther 2022; 26:137-151. [PMID: 35113367 PMCID: PMC8975257 DOI: 10.1007/s40291-021-00575-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/19/2021] [Indexed: 10/19/2022]
Abstract
Many anticancer drugs cause adverse drug reactions (ADRs) that negatively impact safety and reduce quality of life. The typical narrow therapeutic range and exposure-response relationships described for anticancer drugs make precision dosing critical to ensure safe and effective drug exposure. Germline mutations in pharmacogenes contribute to inter-patient variability in pharmacokinetics and pharmacodynamics of anticancer drugs. Patients carrying reduced-activity or loss-of-function alleles are at increased risk for ADRs. Pretreatment genotyping offers a proactive approach to identify these high-risk patients, administer an individualized dose, and minimize the risk of ADRs. In the field of oncology, the most well-studied gene-drug pairs for which pharmacogenetic dosing recommendations have been published to improve safety are DPYD-fluoropyrimidines, TPMT/NUDT15-thiopurines, and UGT1A1-irinotecan. Despite the presence of these guidelines, the scientific evidence showing the benefits of pharmacogenetic testing (e.g., improved safety and cost-effectiveness) and the development of efficient multi-gene genotyping panels, routine pretreatment testing for these gene-drug pairs has not been implemented widely in the clinic. Important considerations required for widespread clinical implementation include pharmacogenetic education of physicians, availability or allocation of institutional resources to build an efficient clinical infrastructure, international standardization of guidelines, uniform adoption of guidelines by regulatory agencies leading to genotyping requirements in drug labels, and development of cohesive reimbursement policies for pretreatment genotyping. Without clinical implementation, the potential of pharmacogenetics to improve patient safety remains unfulfilled.
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12
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Gene-drug pairings for antidepressants and antipsychotics: level of evidence and clinical application. Mol Psychiatry 2022; 27:593-605. [PMID: 34754108 DOI: 10.1038/s41380-021-01340-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/23/2021] [Accepted: 10/01/2021] [Indexed: 11/09/2022]
Abstract
Substantial inter-individual discrepancies exist in both therapeutic effectiveness and adverse effects of antidepressant and antipsychotic medications, which can, in part, be explained by genetic variation. Here, we searched the Pharmacogenomics Knowledge Base for gene-antidepressant and gene-antipsychotic pairs with the highest level of evidence. We then extracted and compared the associated prescribing recommendations for these pairs developed by the Clinical Pharmacogenomics Implementation Consortium, the Dutch Pharmacogenetics Working Group or approved product labels in the US, Canada, Europe, and Asia. Finally, we highlight key economical, educational, regulatory, and ethical issues that, if not appropriately considered, can hinder the implementation of these recommendations in clinical practice. Our review indicates that evidence-based guidelines are available to assist with the implementation of pharmacogenetic-guided antidepressant and antipsychotic prescribing, although the maximum impact of these guidelines on patient care will not be realized until key barriers are minimized or eliminated.
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13
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Tafazoli A, Guchelaar HJ, Miltyk W, Kretowski AJ, Swen JJ. Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice. Front Pharmacol 2021; 12:693453. [PMID: 34512329 PMCID: PMC8424415 DOI: 10.3389/fphar.2021.693453] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Pharmacogenomics (PGx) studies the use of genetic data to optimize drug therapy. Numerous clinical centers have commenced implementing pharmacogenetic tests in clinical routines. Next-generation sequencing (NGS) technologies are emerging as a more comprehensive and time- and cost-effective approach in PGx. This review presents the main considerations for applying NGS in guiding drug treatment in clinical practice. It discusses both the advantages and the challenges of implementing NGS-based tests in PGx. Moreover, the limitations of each NGS platform are revealed, and the solutions for setting up and management of these technologies in clinical practice are addressed.
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Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Network of Personalized Therapeutics, Leiden, Netherlands
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Adam J. Kretowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Jesse J. Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Network of Personalized Therapeutics, Leiden, Netherlands
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Luczak T, Brown SJ, Armbruster D, Hundertmark M, Brown J, Stenehjem D. Strategies and settings of clinical pharmacogenetic implementation: a scoping review of pharmacogenetics programs. Pharmacogenomics 2021; 22:345-364. [PMID: 33829852 DOI: 10.2217/pgs-2020-0181] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Pharmacogenetic (PGx) literature has shown beneficial outcomes in safety, efficacy and cost when evidence-based gene-drug decision making is incorporated into clinical practice. PGx programs with successfully implemented clinical services have been published in a variety of settings including academic health centers and community practice. The primary objective was to systematically scope the literature to characterize the current trends, extent, range and nature of clinical PGx programs. Forty articles representing 19 clinical PGx programs were included in analysis. Most programs are in urban, academic institutions. Education, governance and workflow were commonly described while billing/reimbursement and consent were not. This review provides an overview of current PGx models that can be used as a reference for institutions beginning the implementation process.
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Affiliation(s)
- Tiana Luczak
- Department of Pharmacy Practice & Pharmaceutical Sciences, University of Minnesota, College of Pharmacy, Duluth, MN 55812, USA.,Essentia Health, Duluth, MN 55805, USA
| | - Sarah Jane Brown
- Health Sciences Libraries, University of Minnesota, MN 55455, USA
| | - Danielle Armbruster
- Department of Pharmacy Practice & Pharmaceutical Sciences, University of Minnesota, College of Pharmacy, Duluth, MN 55812, USA
| | - Megan Hundertmark
- Department of Pharmacy Practice & Pharmaceutical Sciences, University of Minnesota, College of Pharmacy, Duluth, MN 55812, USA
| | - Jacob Brown
- Department of Pharmacy Practice & Pharmaceutical Sciences, University of Minnesota, College of Pharmacy, Duluth, MN 55812, USA
| | - David Stenehjem
- Department of Pharmacy Practice & Pharmaceutical Sciences, University of Minnesota, College of Pharmacy, Duluth, MN 55812, USA
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Shah SN, Gammal RS, Amato MG, Alobaidly M, Reyes DD, Hasan S, Seger DL, Krier JB, Bates DW. Clinical Utility of Pharmacogenomic Data Collected by a Health-System Biobank to Predict and Prevent Adverse Drug Events. Drug Saf 2021; 44:601-607. [PMID: 33620701 DOI: 10.1007/s40264-021-01050-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2021] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Medication-related harm represents a significant issue for patient safety and quality of care. One strategy to avoid preventable adverse drug events is to utilize patient-specific factors such as pharmacogenomics (PGx) to individualize therapy. OBJECTIVE We measured the number of patients enrolled in a health-system biobank with actionable PGx results who received relevant medications and assessed the incidence of adverse drug events (ADEs) that might have been prevented had the PGx results been used to inform prescribing. METHODS Patients with actionable PGx results in the following four genes with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines were identified: HLA-A*31:01, HLA-B*15:02, TPMT, and VKORC1. The patients who received interacting medications (carbamazepine, oxcarbazepine, thiopurines, or warfarin) were identified, and electronic health records were reviewed to determine the incidence of potentially preventable ADEs. RESULTS Of 36,424 patients with PGx results, 2327 (6.4%) were HLA-A*31:01 positive; 3543 (9.7%) were HLA-B*15:02 positive; 2893 (7.9%) were TPMT intermediate metabolizers; and 4249 (11.7%) were homozygous for the VKORC1 c.1639 G>A variant. Among patients positive for one of the HLA variants who received carbamazepine or oxcarbazepine (n = 92), four (4.3%) experienced a rash that warranted drug discontinuation. Among the TPMT intermediate metabolizers who received a thiopurine (n = 56), 11 (19.6%) experienced severe myelosuppression that warranted drug discontinuation. Among patients homozygous for the VKORC1 c.1639 G>A variant who received warfarin (n = 379), 85 (22.4%) experienced active bleeding and/or international normalized ratio (INR) > 5 that warranted drug discontinuation or dose reduction. CONCLUSION Patients with actionable PGx results from a health-system biobank who received relevant medications experienced predictable ADEs. These ADEs may have been prevented if the patients' PGx results were available in the electronic health record with clinical decision support prior to prescribing.
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Affiliation(s)
- Sonam N Shah
- Department of Internal Medicine, Brigham and Women's Hospital, 41 Avenue of Louis Pasteur, Office 103, Boston, MA, 02115, USA. .,Department of Pharmacy Practice, MCPHS University School of Pharmacy, Boston, MA, USA.
| | - Roseann S Gammal
- Department of Pharmacy Practice, MCPHS University School of Pharmacy, Boston, MA, USA.,Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Mary G Amato
- Department of Internal Medicine, Brigham and Women's Hospital, 41 Avenue of Louis Pasteur, Office 103, Boston, MA, 02115, USA.,Department of Pharmacy Practice, MCPHS University School of Pharmacy, Boston, MA, USA
| | - Maryam Alobaidly
- Department of Pharmacy Practice, MCPHS University School of Pharmacy, Boston, MA, USA
| | - Dariel Delos Reyes
- Department of Pharmacy Practice, MCPHS University School of Pharmacy, Boston, MA, USA
| | - Sarah Hasan
- Department of Pharmacy Practice, MCPHS University School of Pharmacy, Boston, MA, USA
| | - Diane L Seger
- Clinical Quality Analysis, Partners Healthcare, Somerville, MA, USA
| | - Joel B Krier
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - David W Bates
- Department of Internal Medicine, Brigham and Women's Hospital, 41 Avenue of Louis Pasteur, Office 103, Boston, MA, 02115, USA.,Clinical Quality Analysis, Partners Healthcare, Somerville, MA, USA.,Harvard Medical School, Boston, MA, USA
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