Background With the ever growing arsenal of oral chemotherapy agents now available, cancer treatment is being increasingly managed in the outpatient setting. However, oral chemotherapy use is often associated with several potential obstacles and complications. In order to provide optimal patient safety and oral chemotherapy monitoring, our institution implemented an oral chemotherapy program managed by clinical pharmacists electronically through Epic Beacon. Objective To describe implementation of a novel pharmacist-managed oral chemotherapy program and evaluate pharmacist interventions before and after implementation of an oral chemotherapy program. Methods This was a single-center retrospective chart review of documented pharmacy interventions for oral chemotherapy prescriptions during three months prior to as well as three months following Epic Beacon implementation. Time periods for data inclusion were October-December 2013 (pre-Beacon) and October-December 2014 (post-Beacon). Patients included in the study had one or more oral chemotherapy orders during the pre-Beacon period, the post-Beacon period, or both pre- and post-Beacon. Our analysis did not include oral chemotherapy orders that were placed outside of a treatment plan in the post-Beacon period. Results A total of 240 patients with 450 total oral chemotherapy orders were assessed over the duration of the study. Beacon implementation allowed a greater number of oral chemotherapy orders to be reviewed, with 134 oral chemotherapy orders reviewed in the study period prior to Beacon implementation and 316 orders reviewed in the post-Beacon period. Additionally, there were 660% more pharmacist interventions (89 interventions pre-Beacon versus 681 interventions post-Beacon), with an increased focus on coordination of care, chemotherapy calendar coordination, and assistance with treatment plans. Furthermore, implementation of Epic Beacon allowed identification of over 500% more chemotherapy order errors (41 total errors identified pre-Beacon versus 250 total errors identified post-Beacon). Pharmacists were also able to identify more significant, serious, or potentially lethal errors following implementation. The time associated with oral chemotherapy review and intervention also increased accordingly with number of orders reviewed. Conclusion Implementation of an electronic workflow for oral chemotherapy dramatically increased pharmacist review of orders, resulting in improved documentation of interventions and errors, decreased need for clarification of orders, as well as increased volume of prescriptions at our on-site pharmacy. This study demonstrates a comprehensive approach to maximize safety when oral chemotherapy is utilized as a component of the treatment regimen.

Initiation of insulin pump therapy at diagnosis improved glycemic control, was well tolerated, and contributed to improved patient satisfaction with treatment. This study also suggests that earlier use of pump therapy might help to preserve residual β-cell function, although a larger clinical trial would be required to confirm this.

It is unknown whether clinical characteristics can successfully predict which multiple myeloma (MM) patients would be poor mobilizers with growth factor (GF) alone so they can be assigned to mobilization with chemotherapy + GF or GF + plerixafor. MM patients (N = 477) who underwent autologous mobilization with GF were retrospectively reviewed and assigned into training and validation cohorts. In multiple regression analysis, age, platelet count at time of mobilization, type of GF utilized, and extent of exposure to lenalidomide independently correlated with peripheral blood (PB)-CD34+ and were integrated in a predicting score (PS) for poor mobilizers, defined as PB-CD34+ < 20/mm(3) 4 days after initiation of GF. There was no correlation between institution, gender, time between diagnosis, and mobilization or plasma cells in the bone marrow at time of mobilization and PBCD34+. The PS cut-off found in the training cohort to have 90% sensitivity for prediction of poor mobilizers performed with 89.7% sensitivity but only 34.8% specificity in the validation cohort. Conversely, the PS cut-off developed to have 90% specificity performed with 86.9% specificity but only 37% sensitivity. We conclude that clinical characteristics identifiable before initiation of mobilization should not be used to stratify MM patients for different mobilization strategies.

To evaluate the contemporary prevalence of diabetic peripheral neuropathy (DPN) in participants with type 1 diabetes in the T1D Exchange Clinic Registry throughout the U.S. RESEARCH DESIGN AND METHODSDPN was assessed with the Michigan Neuropathy Screening Instrument Questionnaire (MNSIQ) in adults with ‡5 years of type 1 diabetes duration. A score of ‡4 defined DPN. Associations of demographic, clinical, and laboratory factors with DPN were assessed. RESULTSAmong 5,936 T1D Exchange participants (mean 6 SD age 39 6 18 years, median type 1 diabetes duration 18 years [interquartile range 11, 31], 55% female, 88% non-Hispanic white, mean glycated hemoglobin [HbA 1c ] 8.1 6 1.6% [65.3 6 17.5 mmol/mol]), DPN prevalence was 11%. Compared with those without DPN, DPN participants were older, had higher HbA 1c , had longer duration of diabetes, were more likely to be female, and were less likely to have a college education and private insurance (all P < 0.001). DPN participants also were more likely to have cardiovascular disease (CVD) (P < 0.001), worse CVD risk factors of smoking (P 5 0.008), hypertriglyceridemia (P 5 0.002), higher BMI (P 5 0.009), retinopathy (P 5 0.004), reduced estimated glomerular filtration rate (P 5 0.02), and Charcot neuroarthropathy (P 5 0.002). There were no differences in insulin pump or continuous glucose monitor use, although DPN participants were more likely to have had severe hypoglycemia (P 5 0.04) and/or diabetic ketoacidosis (P < 0.001) in the past 3 months. CONCLUSIONSThe prevalence of DPN in this national cohort with type 1 diabetes is lower than in prior published reports but is reflective of current clinical care practices. These data also highlight that nonglycemic risk factors, such as CVD risk factors, severe hypoglycemia, diabetic ketoacidosis, and lower socioeconomic status, may also play a role in DPN development.Diabetic neuropathy is a prevalent complication in patients with diabetes and a major cause of morbidity and mortality (1). Among the various forms of diabetic neuropathy, distal symmetric polyneuropathy (DPN) and diabetic autonomic neuropathies are by far the most studied (1).