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Cited by 301

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Preemptive Genotyping for Personalized Medicine: Design of the Right Drug, Right Dose, Right Time—Using Genomic Data to Individualize Treatment Protocol

Open access
Bielinski, Suzette J., Olson, Janet E., Pathak, Jyotishman, Weinshilboum, Richard M., Wang, Liewei, Lyke, Kelly J., Ryu, Euijung, Targonski, Paul V., Van Norstrand, Michael D., Hathcock, Matthew A., Takahashi, Paul Y., McCormick, Jennifer B., Johnson, Kiley J., Maschke, Karen J., Rohrer Vitek, Carolyn R., Ellingson, Marissa S., Wieben, Eric D., Farrugia, Gianrico, Morrisette, Jody A., Kruckeberg, Keri J., Bruflat, Jamie K., Peterson, Lisa M., Blommel, Joseph H., Skierka, Jennifer M., Ferber, Matthew J., Black, John L., Baudhuin, Linnea M., Klee, Eric W., Ross, Jason L., Veldhuizen, Tamra L., Schultz, Cloann G., Caraballo, Pedro J., Freimuth, Robert R., Chute, Christopher G., Kullo, Iftikhar J.·2014·Mayo Clinic Proceedings
DOI 10.1016/j.mayocp.2013.10.021
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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 in the electronic medical record (EMR). Patients and Methods We used a multivariable 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 Mayo Clinic Biobank participants with a recruitment goal of 1000 patients. Cox proportional hazards model was utilized using the 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 ICD-9 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, 50% provided blood samples, 13% refused, 28% did not respond, and 9% consented but did not provide a blood sample within the recruitment window (October 4, 2012 – March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS is integrated in the EMR and flags potential patient-specific drug-gene interactions and provides therapeutic guidance. Conclusion These interventions will improve understanding and implementation of genomic data in clinical practice.

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Metadata

Authors
Bielinski, S. J., Olson, J. E., Pathak, J., Weinshilboum, R. M., Wang, L., Lyke, K. J., Ryu, E., Targonski, P. V., Van Norstrand, M. D., Hathcock, M. A., Takahashi, P. Y., McCormick, J. B., Johnson, K. J., Maschke, K. J., Rohrer Vitek, C. R., Ellingson, M. S., Wieben, E. D., Farrugia, G., Morrisette, J. A., Kruckeberg, K. J., Bruflat, J. K., Peterson, L. M., Blommel, J. H., Skierka, J. M., Ferber, M. J., Black, J. L., Baudhuin, L. M., Klee, E. W., Ross, J. L., Veldhuizen, T. L., Schultz, C. G., Caraballo, P. J., Freimuth, R. R., Chute, C. G., & Kullo, I. J.
Venue
Mayo Clinic Proceedings
Publisher
Elsevier BV
Published
2014-01
Volume
89
Issue
1
Pages
25-33
ISSN
0025-6196 (ISSN-L)
DOI
10.1016/j.mayocp.2013.10.021
OpenAlex
W2091631034
Cited by
301
References
11
FWCI
36.45
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Preemptive Genotyping for Personalized Medicine: Design of the Right Drug, Right Dose, Right Time—Using Genomic Data to Individualize Treatment Protocol. https://app.cochat.ai/papers/10.1016/j.mayocp.2013.10.021
The paper
Bielinski, S. J., Olson, J. E., Pathak, J., Weinshilboum, R. M., Wang, L., Lyke, K. J., Ryu, E., Targonski, P. V., Van Norstrand, M. D., Hathcock, M. A., Takahashi, P. Y., McCormick, J. B., Johnson, K. J., Maschke, K. J., Rohrer Vitek, C. R., Ellingson, M. S., Wieben, E. D., Farrugia, G., Morrisette, J. A., . . . Kullo, I. J. (2014). Preemptive Genotyping for Personalized Medicine: Design of the Right Drug, Right Dose, Right Time—Using Genomic Data to Individualize Treatment Protocol. Mayo Clinic Proceedings. https://doi.org/10.1016/j.mayocp.2013.10.021

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.