Multi-Institutional Implementation of Clinical Decision Support for APOL1, NAT2, and YEATS4 Genotyping in Antihypertensive Management

• Published in: Journal of personalized medicine Vol. 11; no. 6; p. 480
• Main Authors: Schneider, Thomas M, Eadon, Michael T, Cooper-DeHoff, Rhonda M, Cavanaugh, Kerri L, Nguyen, Khoa A, Arwood, Meghan J, Tillman, Emma M, Pratt, Victoria M, Dexter, Paul R, McCoy, Allison B, Orlando, Lori A, Scott, Stuart A, Nadkarni, Girish N, Horowitz, Carol R, Kannry, Joseph L
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.05.2021; MDPI
• Subjects: Algorithms; Antihypertensives; Automation; Blood pressure; Committees; Consortia; Electronic health records; Electronic medical records; Genotype & phenotype; Genotyping; Kidney diseases; Laboratories; Pharmacogenomics; Precision medicine; Principles; Values; United States > US; YEATS4; APOL1; NAT2; clinical decision support; pharmacogenetics
• ISSN: 2075-4426; 2075-4426
• DOI: 10.3390/jpm11060480

(1) Background: Clinical decision support (CDS) is a vitally important adjunct to the implementation of pharmacogenomic-guided prescribing in clinical practice. A novel CDS was sought for the , , and genes to guide optimal selection of antihypertensive medications among the African American population cared for at multiple participating institutions in a clinical trial. (2) Methods: The CDS committee, made up of clinical content and CDS experts, developed a framework and contributed to the creation of the CDS using the following guiding principles: 1. medical algorithm consensus; 2. actionability; 3. context-sensitive triggers; 4. workflow integration; 5. feasibility; 6. interpretability; 7. portability; and 8. discrete reporting of lab results. (3) Results: Utilizing the principle of discrete patient laboratory and vital information, a novel CDS for , , and was created for use in a multi-institutional trial based on a medical algorithm consensus. The alerts are actionable and easily interpretable, clearly displaying the purpose and recommendations with pertinent laboratory results, vitals and links to ordersets with suggested antihypertensive dosages. Alerts were either triggered immediately once a provider starts to order relevant antihypertensive agents or strategically placed in workflow-appropriate general CDS sections in the electronic health record (EHR). Detailed implementation instructions were shared across institutions to achieve maximum portability. (4) Conclusions: Using sound principles, the created genetic algorithms were applied across multiple institutions. The framework outlined in this study should apply to other disease-gene and pharmacogenomic projects employing CDS.