Impact of Drug Interactions on Major Bleed Rates in Patients Taking Direct Oral Anticoagulants.

Background: While direct oral anticoagulants (DOACs) have less stringent monitoring, favorable pharmacokinetics, and fewer drug interactions compared to warfarin, there is still a potential for drug-drug interactions. There is limited evidence showing total impact of DOAC drug interactions on major bleeding events. Methods: This was an IRB-approved retrospective, case control, single center study. Patients were included if they had received a DOAC from January 2012 to September 2019 and were identified for major bleeding events and matched to a control group. The primary objective was to compare the presence of major drug interactions between patients on DOACs who did and did not have a major bleeding event. The secondary objectives were to compare the impact of specific interacting drug classes and their additive effects on major bleeding events. Results: There were 122 patients included in the study. While the number of patients on at least one interacting medication within duration of DOAC use was numerically higher in the bleed group (85% vs 72%), this was not significant (P = .077). There were significantly more patients on at least three interacting medications within the last 3 months of the study period in the bleed group (n = 9 vs 1), with significantly higher use of aspirin (n = 38 vs 17) and rate control agents (n = 24 vs 11). Conclusion: There may be a cumulative effect on risk of bleeding if patients are on three or more interacting medications concomitantly with a DOAC. This risk of bleeding may be higher with aspirin and rate control agents.

Impact of SMART Goals on Diabetes Management in a Pharmacist-Led Telehealth Clinic.

Background: Although there is evidence demonstrating successful implementation of SMART (specific, measurable, achievable, relevant, time-bound) goals in clinical settings, their impact on improving diabetes control has not been well-established. Objective: The primary objective was to determine the association between setting SMART goals and change in A1c among a Veteran population. Methods: This was an IRB-approved retrospective, case-control study. Patients with Type 2 diabetes mellitus (DM) managed virtually by a Clinical Pharmacy Specialist at a VA Community-Based Outpatient Clinic were eligible for inclusion. The electronic medical record was used to identify patients that set a SMART goal for DM management during the study timeframe. These patients were matched to a similar cohort of patients that did not set a SMART goal. Results: There were 100 patients included in the study. Goal A1c was achieved in 30% of patients in the SMART goal group compared with 24% of patients in the control group. There was a 1.2% reduction in A1c from baseline to 3 months in the SMART goal group vs .85% in the control group (P = .287). The mean number of medication changes per patient was 1.7 in the SMART goal group vs 2.1 in the control group (P = .174). Patients in the SMART goal group set an average of 1.5 SMART goals during the study period. Conclusion: Overall, patients that set SMART goals had clinically meaningful A1c lowering. Setting SMART goals for DM management in agreeable patients during diabetes telehealth visits may lead to fewer medication changes and improved diabetes control.

Implementation of Postoperative Standard Opioid Prescribing Schedules Reduces Opioid Prescriptions Without Change in Patient-reported Pain Outcomes.

OBJECTIVE: To implement Standard Opioid Prescribing Schedules (SOPS) based on opioid use following urologic surgeries and to evaluate how evidence-based prescribing schedules affect opioid use and patient reported outcomes. METHODS: Patients who underwent urologic surgeries within 6 procedure subtypes at UNC Health during the 2 study time periods ("pre-SOPS": 7/2017-1/2018, "post-SOPS": 7/2018-1/2019) were invited to complete a survey analyzing postoperative opioid usage, storage and disposal, and patient reported outcomes (including pain interference using a validated questionnaire). A pharmacy database provided medication prescribing data and patient demographics. During the pre-SOPS time period, baseline outcomes were measured. Following the pre-SOPS period, usage amounts were analyzed and Standard Opioid Prescribing Schedules were developed to guide prescriptions during the post-SOPS period. Descriptive summary statistics and appropriate t test or r2 were calculated. RESULTS: A total of 438 patients within 6 procedure types completed the survey (pre-SOPS: 282 patients, post-SOPS: 156 patients). Pre-SOPS, patients were prescribed significantly more 5-mg oxycodone tablets than used (20.9 vs 7.8, P <.001). Post-SOPS, compared to pre-SOPS amounts, patients were prescribed significantly fewer tablets (12.7 vs 20.9, P <.001) and used fewer tablets (5.3 vs 7.8, P = .003). No difference was observed in pain interference (average t-score (standard deviation): 54.33 (10.9) pre-SOPS vs 55.89 (9.1) post-SOPS, P = .125) or patient satisfaction (95% pre-SOPS vs 94% post-SOPS). CONCLUSION: Adherence to data-driven postoperative opioid prescribing schedules reduce opioid prescriptions and use without compromising pain interference or patient satisfaction. These results have important implications for urologists' ability to decrease opioid prescriptions and fight the opioid epidemic.