Research Article

CYP2C19 genotype, physician prescribing pattern, and risk for long QT on serotonin selective reuptake inhibitors

*Author for correspondence: Tel.: +1 701 231 6554;

E-mail Address: natasha.petry@ndsu.edu

Department of Pharmacy Practice, North Dakota State University, Fargo, ND 58108, USA

Department of Internal Medicine, Sanford Health Fargo, ND 58122, USA

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Roxana Lupu

Department of Internal Medicine, Sanford Health Sioux Falls, SD 57117, USA

Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57105, USA

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Ahmed Gohar

Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57105, USA

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Eric A Larson

Department of Internal Medicine, Sanford Health Sioux Falls, SD 57117, USA

Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57105, USA

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Carmen Peterson

Department of Internal Medicine, Sanford Health Sioux Falls, SD 57117, USA

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Vanessa Williams

Department of Internal Medicine, Sanford Health Sioux Falls, SD 57117, USA

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Jing Zhao

Department of Internal Medicine, Sanford Health Sioux Falls, SD 57117, USA

Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57105, USA

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Russell A Wilke

Department of Internal Medicine, Sanford Health Sioux Falls, SD 57117, USA

Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57105, USA

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Lindsay J Hines

Department of Neuropsychology, Sanford Health, Fargo, ND 58122, USA

Department of Psychology, University of North Dakota, Grand Forks, ND 58202, USA

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Published Online:15 Apr 2019https://doi.org/10.2217/pgs-2018-0156

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Abstract

Aim: To examine the impact of CYP2C19 genotype on selective serotonin reuptake inhibitor (SSRI) prescribing patterns. Patients & methods: Observational cohort containing 507 unique individuals receiving an SSRI prescription with CYP2C19 genotype already in their electronic medical record. Genotype was distributed as follows: n = 360 (71%) had no loss of function alleles, 136 (26.8%) had one loss of function allele and 11 (2.2%) had two loss of function alleles. Results & conclusion: For poor metabolizers exposed to sertraline, citalopram or escitalopram, providers changed prescribing patterns in response to alerts in the electronic medical record by either changing the drug, changing the dose or monitoring serial EKGs longitudinally. For intermediate metabolizers exposed to sertraline, citalopram or escitalopram, no alert was needed (mean QTc = 440.338 ms [SD = 31.1273] for CYP2C19*1/*1, mean QTc = 440.371 ms [SD = 29.2706] for CYP2C19*1/*2; p = 0.995).

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Papers of special note have been highlighted as: • of interest; •• of considerable interest

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Vol. 20, No. 5

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History

Received 17 September 2018

Accepted 14 January 2019

Published online 15 April 2019

Published in print April 2019

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Keywords

Financial & competing interests disclosure

This work was funded by the NIH (U01HG007253) and through a generous gift from T Denny Sanford that created Imagenetics (merging Internal Medicine and Genetics). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Acknowledgements

The authors would like to thank E Dalseide (University of South Dakota) for assistance with the construction of Figures 1 and 2.

Ethical disclosure

In first section of methods, the authors state that they have obtained appropriate institutional review board approval for all human experimental investigations. No informed consent used as study was retrospective chart review.

Author's contributions

N Petry substantially contributed to the conception or design of the work, interpretation of data, drafting and revising the work, final approval and agreement to be accountable for work. R Lupu substantially contributed to the conception or design of the work, acquisition and analysis/interpretation of data, drafting and revising the work, final approval and agreement to be accountable for work. A Gohar substantially contributed to the acquisition and analysis of data for the work, drafting and revising the work, final approval and agreement to be accountable for work. EA Larson substantially contributed to the conception or design of the work, interpretation of data, drafting and revising the work, final approval and agreement to be accountable for work. C Peterson substantially contributed to the conception and acquisition of data for the work, drafting and revising the work, final approval and agreement to be accountable for work. V Williams substantially contributed to the conception and acquisition of data for the work, drafting and revising the work, final approval and agreement to be accountable for work. J Zhao substantially contributed to the conception or design of the work, acquisition and analysis/interpretation of data, drafting and revising the work, final approval and agreement to be accountable for work. RA Wilke substantially contributed to the conception or design of the work, interpretation of data, drafting and revising the work, final approval and agreement to be accountable for work. LJ Hines substantially contributed to the conception or design of the work, interpretation of data, drafting and revising the work, final approval and agreement to be accountable for work.

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