A Point-of-Care Interactive Decision Tool Reveals Variance Between Clinicians and Experts in Selecting Among GLP-1 RAs in T2D

Abstract Background: T2D management is shifting toward treating patients with therapies that align with their level of CV and end-organ risk. To this end, evidence-based guidelines now recommend glucagon-like peptide-1 receptor agonists (GLP-1 RAs) for both glycemic and extraglycemic benefits. The great speed with which these recommendations change create immediate gaps in knowledge and competence, especially as they relate to managing patients with comorbid CV and/or renal disease. To help clinicians understand GLP-1 RA therapies and their novel characteristics in practice, we developed a decision support tool where choice of treatment among GLP-1 RAs is guided by a panel of experts. Methods: We developed a decision support tool with guidance from 5 experts who provided therapy recommendations for 48 unique patient case scenarios based on patient variables including CVD, CKD, retinopathy, A1C level, and need for weight loss. Clinician learners are prompted to specify a patient scenario using these variables before selecting an intended therapy. After all questions are completed for a patient scenario, the tool displays what the panel of experts recommend and then asks the learner if this information changed their intended choice. Results: From February through October 2020, 983 learners entered 1433 unique patient case scenarios. Of these, 365 were anonymous and 623 were authenticated, of which 70% (n = 437) were from the US; 50% (n = 310) were MDs; 22% (n = 135) were nurses, NPs, or PAs; and 19% (n = 121) were PharmDs. The intended therapy of learners differed from the experts in 34% (n = 489) of cases and were limited to 3 categories: cases in which learners chose to use exenatide (17%), cases in which they chose to use a GLP-1 RA in conjunction with insulin (12%), or cases in which they were unsure (71%). Of note, of the 93 cases in which learners chose exenatide, 68% (n = 63) were cases with CVD and/or CKD, where exenatide was not recommended by experts. Similarly, of the 89 cases in which learners chose insulin with a GLP-1 RA, 57% (n = 51) were cases with A1C < 9%, where insulin was not recommended by experts. Of cases in which learners’ intended therapy differed from the experts’ (and they indicated the impact of the tool), 52% indicated that they planned to change their treatment plan. Conclusion: This tool highlights continuing gaps in clinicians’ ability to select among GLP-1 RAs for T2D. Using a decision support tool can positively influence practice behaviors: Learners can see if their intended treatment choice is congruent with a panel of experts and change plans as appropriate.