4.6 Article

Pharmacogenomic-Based Decision Support to Predict Adherence to Medications

Journal

CLINICAL PHARMACOLOGY & THERAPEUTICS
Volume 108, Issue 2, Pages 368-376

Publisher

WILEY
DOI: 10.1002/cpt.1838

Keywords

-

Funding

  1. National Institutes of Health (NIH) [K23 GM 100288-01A1]
  2. University of Chicago Comprehensive Cancer Center
  3. University of Chicago Bucksbaum Institute for Clinical Excellence Pilot Award
  4. Central Society for Clinical and Translational Research-Early Career Development Award
  5. Benjamin McAllister Scholar Program
  6. NIH [5T35DK062719-28]
  7. William F. O'Connor Foundation

Ask authors/readers for more resources

Poor adherence is associated with worse disease outcomes. Pharmacogenomics provides a possible intervention to address adherence. We hypothesized that pharmacogenomic-informed care could increase adherence. Patients in a prospective case-control study underwent preemptive pharmacogenomic genotyping with results available for provider use at the point of care; controls (not genotyped) were treated by the same providers. Over 6,000 e-prescriptions for 39 medications with actionable pharmacogenomic information were analyzed. Composite adherence, measured by modified proportion of days covered (mPDC), was compared between cases/controls and genomically concordant vs. genomically higher-risk medications. Overall, 536 patients were included. No difference in mean mPDC was observed due to availability of pharmacogenomic guidance. However, case patients prescribed high-risk pharmacogenomic medications were more than twice as likely to have low mPDC for these medications compared with genomically concordant prescriptions (odds ratio = 2.4 (1.03-5.74), P < 0.05). This study is the first to show that composite pharmacogenomic information predicts adherence.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available