RESUMO
Background: As preferences for oral anticoagulation shift from warfarin to direct oral anticoagulants (DOACs), a new care management model is needed. A population approach leveraging a DOAC Dashboard was implemented to track all patients on a DOAC followed by a physician at an academic medical center. The DOAC Dashboard is a real-time report within the electronic health record (EHR) that identifies patients who require evaluation for DOAC dose/therapy adjustment due to changing renal function, age, weight, indication, and/or significant drug-drug interaction (DDI). Objective: This study aims to describe the initial phase of DOAC Dashboard implementation, to evaluate the effectiveness of interventions, and to assess a multidisciplinary approach to management. Method: Retrospective descriptive study of the DOAC Dashboard from August 22, 2019, to January 20, 2022. Primary outcomes include total number of alerts addressed and interventions needed. Secondary outcome is the proportion of interventions implemented by the prescribing clinician. Result: A total of 10 912 patients were identified by the DOAC Dashboard at baseline. A total of 5038 alerts were identified, with 668 critical alerts, 3337 possible critical alerts, and 1033 other alerts. Pharmacists addressed 1796 alerts during the study period (762 critical alerts and 1034 possible critical). Critical alerts included 62 significant DDI, 379 inappropriate dosing, and 321 others. Of the critical alerts, intervention was needed in 291 cases (38%), with 255 (88%) of proposed interventions implemented. Critical alerts and possible critical alerts not requiring intervention were resolved by data entry. Conclusion: The DOAC Dashboard provides an efficient method of identifying patients on DOACs that require dose adjustments or therapeutic modifications.
RESUMO
Infections are an important cause of morbidity and mortality in newly diagnosed multiple myeloma (NDMM), but the real-world risk using modern induction regimens such as bortezomib, lenalidomide, and dexamethasone (RVd) is not well described. We performed a retrospective single-center cohort study to identify infections and risk factors in patients treated with first-line RVd from January 2014 to January 2020 and collected demographic and clinical data. Of 144 patients treated with RVd for NDMM, 21 patients (14.5%) experienced a bacterial infection during induction, of which 8 (5.5%) were grade 3 infections despite a low rate of antibiotic prophylaxis use (12%). Grade 3 neutropenia occurred in 11% of patients, 2% had febrile neutropenia and there were no deaths from infection. On multivariable analysis, age, smoking history, diabetes, antibiotic use in the 60 days preceding the start of RVd, and high-risk cytogenetics were associated with higher risk of bacterial infection.
Assuntos
Infecções Bacterianas , Mieloma Múltiplo , Humanos , Lactente , Mieloma Múltiplo/diagnóstico , Mieloma Múltiplo/tratamento farmacológico , Mieloma Múltiplo/epidemiologia , Lenalidomida/efeitos adversos , Bortezomib/efeitos adversos , Incidência , Estudos Retrospectivos , Estudos de Coortes , Protocolos de Quimioterapia Combinada Antineoplásica/efeitos adversos , Infecções Bacterianas/diagnóstico , Infecções Bacterianas/epidemiologia , Infecções Bacterianas/etiologia , Fatores de Risco , Dexametasona/efeitos adversosRESUMO
OBJECTIVE: Approximately 30% of individuals admit that their medication nonadherence is due to forgetfulness, and 20%-30% of new prescriptions are never picked up. The primary objective of this study is to determine the impact of a text messaging reminder service on time to prescription pickup in an independent community pharmacy setting. METHODS: We conducted a retrospective evaluation using a pre-post design involving 42 individuals who voluntarily enrolled in a text messaging reminder service. The periods 3 months before and 3 months after the initiation of the service were compared. Text messages were sent to individuals when their prescriptions were ready to be picked up. Time to prescription pickup was the primary dependent variable, and the number of medications, distance from the pharmacy, age, and sex were examined. In addition, we sent a text message inquiring about satisfaction with the service. RESULTS: In an analysis of 487 prescription pickup instances, 212 occurred before and 275 after the service for the 42 enrolled individuals. The average time to prescription pickup significantly improved by 12.3 hours (P = 0.001) after implementing the service. After adjusting for age, sex, distance to the pharmacy, number of medications per instance, and number of text messages per instance, the time to prescription pickup improved significantly by 15.8 hours (P = 0.03) after implementation. Thirty-three individuals (79%) reported satisfaction with the text messaging service, whereas 9 patients (21%) did not respond to the survey. Individuals living between 2 and 5 miles from the pharmacy and those 50-64 years old were less likely to respond to the satisfaction survey compared with the other distance and age categories. CONCLUSION: A text message reminder service significantly improved the time to prescription pickup by almost 16 hours in an independent pharmacy. Overall, individuals were satisfied with the service.