{"doi":"10.1002/cpt.1838","title":"Pharmacogenomic‐Based Decision Support to Predict Adherence to Medications","abstract":"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.","journal":"Clinical Pharmacology & Therapeutics","year":2020,"id":75946,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":22,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9652,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":396010,"name":"Brittany A. Borden","orcid":"0009-0004-2439-1525","position":1,"is_corresponding":false},{"id":396011,"name":"Keith Danahey","orcid":"0000-0002-2418-855X","position":2,"is_corresponding":false},{"id":396012,"name":"Kiang-Teck J Yeo","orcid":"0000-0002-4708-8420","position":3,"is_corresponding":false},{"id":396013,"name":"Xander M.R. van Wijk","orcid":"0000-0001-8524-1281","position":4,"is_corresponding":false},{"id":356678,"name":"Mark J. Ratain","orcid":"0000-0002-0938-4392","position":5,"is_corresponding":false},{"id":51075,"name":"Peter H. O’Donnell","orcid":"0000-0003-2650-0049","position":6,"is_corresponding":false},{"id":396727,"name":"Carlton Christian","orcid":null,"position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-18T21:46:55.856612Z","pmid":"32236960","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}