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Katy E Trinkley,1– 4 Michael G Kahn,5 Larry A Allen,2,4 Heather Haugen,6 Miranda E Kroehl,7 Chen-Tan Lin,2,3 Daniel C Malone,8 Daniel D Matlock2,4,9 1Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, USA; 2Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA; 3Clinical Informatics, University of Colorado Health, Aurora, CO, USA; 4Adult and Child Consortium for Outcomes Research and Delivery Science, Aurora, CO, USA; 5Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA; 6University of Colorado, Colorado Clinical and Translational Sciences Institute (CCTSI), Aurora, CO, USA; 7Charter Communications Corporation, Greenwood Village, CO, USA; 8Department of Pharmacotherapy, University of Utah Skaggs College of Pharmacy, Salt Lake City, UT, USA; 9VA Eastern Colorado Geriatric Research Education and Clinical Center, Aurora, CO, USACorrespondence: Katy E TrinkleyUniversity of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, 12850 E Montview Blvd., Mail Stop C238, Aurora, CO 80045, USATel +1-303-724-6563Fax +1-303-724-0979Email katy.trinkley@cuanschutz.eduIntroduction: Consideration of patient preferences for guideline-directed medical therapies (GDMT) for heart failure with reduced ejection fraction (HFrEF) may help improve major gaps in prescribing and adherence. This study aimed to identify the range and relative priority of factors influencing patients’ decisions to take HFrEF medications.Materials and Methods: This was a convergent mixed methods study of patients with HFrEF. Focus groups were conducted to identify a list of factors followed by individuals rating and ranking the influence of each factor on their decision to take a medication. Using thematic analysis, we summarized preferences into categories.Results: Two focus groups with 13 participants reported 22 factors. Of the factors, “keeping you alive” was most commonly ranked in the top three (seven participants), followed by “communication and understanding” (six participants). Factors were summarized into six categories (listed in order of patient-reported influence): 1) demonstrated improvements in quality of life and longevity, 2) decreased risk of hospitalization, 3) opportunity for shared decision-making and trust in provider, 4) absence of adverse events, 5) affordability, and 6) convenience of taking and absence of interference with daily life.Conclusion: Patients prioritize treatment benefits and being informed more than risks, cost and inconvenience of taking HFrEF medications.Keywords: heart failure, treatment preferences, medication preferences, patient preferences

Katy E Trinkley,1 S Michelle Nikels,2 Robert L Page II,1 Melanie S Joy11Skaggs School of Pharmacy and Pharmaceutical Sciences, 2School of Medicine, University of Colorado, Aurora, CO, USA Objective: The purpose of this paper is to serve as a review for primary care providers on the bedside methods for estimating glomerular filtration rate (GFR) for dosing and chronic kidney disease (CKD) staging and to discuss how automated health information technologies (HIT) can enhance clinical documentation of staging and reduce medication errors in patients with CKD.Methods: A nonsystematic search of PubMed (through March 2013) was conducted to determine the optimal approach to estimate GFR for dosing and CKD staging and to identify examples of how automated HITs can improve health outcomes in patients with CKD. Papers known to the authors were included, as were scientific statements. Articles were chosen based on the judgment of the authors.Results: Drug-dosing decisions should be based on the method used in the published studies and package labeling that have been determined to be safe, which is most often the Cockcroft–Gault formula unadjusted for body weight. Although Modification of Diet in Renal Disease is more commonly used in practice for staging, the CKD–Epidemiology Collaboration (CKD–EPI) equation is the most accurate formula for estimating the CKD staging, especially at higher GFR values. Automated HITs offer a solution to the complexity of determining which equation to use for a given clinical scenario. HITs can educate providers on which formula to use and how to apply the formula in a given clinical situation, ultimately improving appropriate medication and medical management in CKD patients.Conclusion: Appropriate estimation of GFR is key to optimal health outcomes. HITs assist clinicians in both choosing the most appropriate GFR estimation formula and in applying the results of the GFR estimation in practice. Key limitations of the recommendations in this paper are the available evidence. Further studies are needed to better understand the best method for estimating GFR.Keywords: laboratory automation, glomerular filtration rate, medications, dose adjustment