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. 2020 Dec 1;3(12):e2029411.
doi: 10.1001/jamanetworkopen.2020.29411.

Prescribing Prevalence of Medications With Potential Genotype-Guided Dosing in Pediatric Patients

Laura B Ramsey  1   2 , Henry H Ong  3 , Jonathan S Schildcrout  4 , Yaping Shi  4 , Leigh Anne Tang  5 , J Kevin Hicks  6 , Nihal El Rouby  7   8 , Larisa H Cavallari  7 , Sony Tuteja  9 , Christina L Aquilante  10 , Amber L Beitelshees  11 , Daniel L Lemkin  12 , Kathryn V Blake  13 , Helen Williams  14 , James J Cimino  15 , Brittney H Davis  16 , Nita A Limdi  16 , Philip E Empey  17 , Christopher M Horvat  18 , David P Kao  19 , Gloria P Lipori  20 , Marc B Rosenman  21   22 , Todd C Skaar  23 , Evgenia Teal  24 , Almut G Winterstein  25 , Aniwaa Owusu Obeng  26 , Daria Salyakina  27 , Apeksha Gupta  27 , Joshua Gruber  27 , Jennifer McCafferty-Fernandez  27 , Jeffrey R Bishop  28   29 , Zach Rivers  30 , Ashley Benner  31 , Bani Tamraz  32 , Janel Long-Boyle  32 , Josh F Peterson  33 , Sara L Van Driest  33   34 ; IGNITE Pharmacogenetics Working Group
Affiliations

Prescribing Prevalence of Medications With Potential Genotype-Guided Dosing in Pediatric Patients

Laura B Ramsey et al. JAMA Netw Open. .

Abstract

Importance: Genotype-guided prescribing in pediatrics could prevent adverse drug reactions and improve therapeutic response. Clinical pharmacogenetic implementation guidelines are available for many medications commonly prescribed to children. Frequencies of medication prescription and actionable genotypes (genotypes where a prescribing change may be indicated) inform the potential value of pharmacogenetic implementation.

Objective: To assess potential opportunities for genotype-guided prescribing in pediatric populations among multiple health systems by examining the prevalence of prescriptions for each drug with the highest level of evidence (Clinical Pharmacogenetics Implementation Consortium level A) and estimating the prevalence of potential actionable prescribing decisions.

Design, setting, and participants: This serial cross-sectional study of prescribing prevalences in 16 health systems included electronic health records data from pediatric inpatient and outpatient encounters from January 1, 2011, to December 31, 2017. The health systems included academic medical centers with free-standing children's hospitals and community hospitals that were part of an adult health care system. Participants included approximately 2.9 million patients younger than 21 years observed per year. Data were analyzed from June 5, 2018, to April 14, 2020.

Exposures: Prescription of 38 level A medications based on electronic health records.

Main outcomes and measures: Annual prevalence of level A medication prescribing and estimated actionable exposures, calculated by combining estimated site-year prevalences across sites with each site weighted equally.

Results: Data from approximately 2.9 million pediatric patients (median age, 8 [interquartile range, 2-16] years; 50.7% female, 62.3% White) were analyzed for a typical calendar year. The annual prescribing prevalence of at least 1 level A drug ranged from 7987 to 10 629 per 100 000 patients with increasing trends from 2011 to 2014. The most prescribed level A drug was the antiemetic ondansetron (annual prevalence of exposure, 8107 [95% CI, 8077-8137] per 100 000 children). Among commonly prescribed opioids, annual prevalence per 100 000 patients was 295 (95% CI, 273-317) for tramadol, 571 (95% CI, 557-586) for codeine, and 2116 (95% CI, 2097-2135) for oxycodone. The antidepressants citalopram, escitalopram, and amitriptyline were also commonly prescribed (annual prevalence, approximately 250 per 100 000 patients for each). Estimated prevalences of actionable exposures were highest for oxycodone and ondansetron (>300 per 100 000 patients annually). CYP2D6 and CYP2C19 substrates were more frequently prescribed than medications influenced by other genes.

Conclusions and relevance: These findings suggest that opportunities for pharmacogenetic implementation among pediatric patients in the US are abundant. As expected, the greatest opportunity exists with implementing CYP2D6 and CYP2C19 pharmacogenetic guidance for commonly prescribed antiemetics, analgesics, and antidepressants.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Ramsey reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study and grants and personal fees from BTG International, Ltd, outside the submitted work. Dr Hicks reported receiving grants from OneOme, LLC, the and American Society of Health-System Pharmacists and personal fees from 23andMe, Novartis International AG, and Quest Diagnostics outside the submitted work. Dr Cavallari reported receiving grants from the National Human Genome Research Institute (NHGRI), NIH, during the conduct of the study. Dr Aquilante reported receiving grants from the NIH during the conduct of the study. Dr Beitelshees reported receiving grants from the NIH during the conduct of the study. Dr Blake reported receiving grants from the University of Florida, Gainesville, during the conduct of the study. Dr Empey reported receiving grants from the Leon Lowenstein Foundation during the conduct of the study. Dr Horvat reported receiving grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, during the conduct of the study. Dr Rosenman reported receiving grants and nonfinancial support from the NHGRI, NIH, during the conduct of the study. Dr Skaar reported receiving grants from the NIH during the conduct of the study, personal fees from Indiana University Health Pharmacogenetics, consulting and personal fees from Tabula Rasa HealthCare, Inc, and travel and honorarium for speaking at the Precision Medicine World Conference outside the submitted work. Dr Winterstein reported receiving grants from Merck & Co, the NIH, the US Food and Drug Administration, the Patient-Centered Outcomes Research Institute, the Agency for Healthcare Research and Quality, and the State of Florida outside the submitted work. Dr McCafferty-Fernandez reported receiving grants from Sanford Health; Sanford Health and Nicklaus Children’s Hospital have a collaborative agreement to develop and implement personalized medicine in pediatrics during the conduct of the study. Dr Bishop reported receiving grants from Vanderbilt University during the conduct of the study, consulting for OptumRx outside the submitted work, and being a member of the Clinical Pharmacogenetics Implementation Consortium. Dr Rivers reported receiving grants from the National Center for Advancing Translational Sciences, NIH, during the conduct of the study. Dr Tamraz reported receiving personal fees from Codex Genetics outside the submitted work. Dr Peterson reported receiving grants from the NHGRI, NIH, during the conduct of the study and personal fees from Color Genomics outside the submitted work. Dr Van Driest reported receiving grants from the NIH during the conduct of the study. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Annual Prevalence of Exposure to at Least 1 Clinical Pharmacogenetics Implementation Consortium (CPIC) Level A Medication by Site and to 1 or More CPIC Level A Medications
A, Each circle represents the observed prevalence of exposure for a given site on a log scale. Circles are absent for years when data were not available. The size of the circle is proportional to the number of patients who experienced at least 1 encounter in that year. The dotted lines represent the prevalence of exposure estimated from the model fit. The mean prevalence of exposure across all sites is shown by the solid black line. The 95% CIs for the mean is filled in gray but may be too narrow to observe. B, On a linear scale, the mean annual prevalence of exposure is stratified by the number of CPIC level A medications prescribed. The prevalence of exposure was estimated from the model. The whiskers indicate 95% CIs. M indicates million.
Figure 2.
Figure 2.. Annual Prevalence of Exposure to Clinical Pharmacogenetics Implementation Consortium (CPIC) Level A Medications, Stratified by Drug Class and Individual Analgesics
A, The annual prevalence of exposure for each drug or drug class was estimated from the model and is plotted on a log axis. If a drug class only had a single included drug, that drug was listed instead of the drug class. For example, ondansetron is listed instead of antiemetic medications. B, Annual prevalence of exposure for analgesics is plotted on a linear scale. The estimated prevalence of exposure for all analgesics was taken from the drug class model in part A, whereas those for oxycodone, codeine, and tramadol were taken from the individual drug models. The whiskers indicate 95% CIs. Non-CPIC level A analgesics were not included. SSRI indicates selective serotonin reuptake inhibitor; TCA, tricyclic antidepressant.
Figure 3.
Figure 3.. Annual Prevalence of Exposure to Clinical Pharmacogenetics Implementation Consortium (CPIC) Level A Medications Stratified by Gene
A, Annual prevalence of exposure to at least 1 CPIC level A medication plotted on a log scale, stratified by the associated gene. B, Annual prevalence of exposure of at least 2 CPIC level A medications. The rate of exposure was estimated from the model and is displayed on a log scale.
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