Accurate assessment of kidney function is an important component of determining appropriate drug dosing regimens. Nearly all manufacturer-recommended dosage adjustments are based on creatinine clearance ranges derived from clinical pharmacokinetic studies performed during the drug development process. The Cockcroft-Gault (CG) equation provides an estimate of creatinine clearance and is the equation most commonly used to determine drug dosages in patients with impaired kidney function. The Modification of Diet in Renal Disease (MDRD) study equation has also been proposed for this purpose. Published studies report that drug dosages determined by the two equations do not agree in 10-40% of cases. However, interpretation and comparison of these studies are complicated by the variable creatinine methods used for calculating CG and MDRD estimates, the patient populations studied, and a lack of outcomes data demonstrating the clinical significance of dosing discrepancies. Moreover, the impact of reporting standardized serum creatinine values on the accuracy of the CG equation and corresponding drug dosing regimens have been questioned. Currently, no prospective pharmacokinetic studies have been conducted with use of the MDRD equation to generate dosing recommendations, and limited data are available to support its use in some patient populations representing demographic extremes. Collectively, these issues have resulted in considerable confusion among clinicians and have fueled a healthy debate on whether or not to use the MDRD equation to determine drug dosages. Each of these issues is reviewed, and a proposed algorithm for using creatinine-based kidney function assessments in drug dosing is provided. Knowledge of the advantages, limitations, and clinical role of each equation will facilitate their safe and effective use in drug dosing.
Objective
To determine levels of environmental chemotherapy contamination in a new cancer hospital that has exclusively used a closed-system drug transfer device (PhaSeal) for preparing and administering all compatible antineoplastics.
Methods
After 6 months of operation, surface samples were collected from pharmacy and nursing areas to determine levels of contamination with cyclophosphamide and ifosfamide. In addition, urine samples were collected from pharmacists, pharmacy technicians, and nurses to determine employee exposure to these agents. All samples were analyzed using liquid chromatography/tandem mass spectrometry.
Results
Twenty-one percent (7/34) of surface samples collected tested positive for cyclophosphamide contamination. Twelve percent (4/34) of surface samples tested positive for ifosfamide. To place this into perspective, historical data collected at our outpatient oncology infusion clinic 6 months after converting to PhaSeal from conventional methods of antineoplastic preparation showed 33% (7/21) and 71% (15/21) of samples tested positive for cyclophosphamide and ifosfamide, respectively. The level of ifosfamide contamination found in samples that tested positive at our new hospital also appeared to be lower than in positive samples at the outpatient infusion clinic. In the current study, the urine of one participant (1/11), a pharmacy technician, tested positive for low levels of cyclophosphamide and ifosfamide. To compare, 71% and 0% of participants tested at the outpatient infusion clinic had positive urine samples prior to and 6 months after implementation of PhaSeal, respectively. Conclusions: Compared with historical levels of contamination in our outpatient oncology infusion clinic, levels of chemotherapy contamination appeared lower. However, some contamination was still present in our new cancer hospital where PhaSeal had been used exclusively.
The Cockcroft-Gault, MDRD, and CKD-EPI equations provide reasonable estimates of kidney function; however, clinicians must understand the limitations when using these estimates for drug regimen design.
Objective. To model the relationship of common pharmacy education assessment data including student demographics, pre-pharmacy performance, core didactic performance, and external testing measures to identify predictors of student readiness for advanced pharmacy practice experiences (APPEs). Methods. The associations between 23 predictive covariates from 226 graduating students from 2015-2018 (5786 observations) and APPE readiness as measured by midpoint core APPE scores were modeled. Multiple linear and Poisson regression models with backward selection were used. A selection criterion of p ..10 was used for covariate elimination from the model. Three models were evaluated: average of all midpoint core APPE rotation scores; average of midpoint acute care pharmacy practice and ambulatory care APPE rotation scores; and number of midpoint core clerkship failing scores. Results. The average age of the population at admission was 25.464.5 years, 47% were female, and 75.2% had prior degrees. Across the three prediction models, knowledge-retention covariates were the strongest predictors. Total score on the Pharmacy Curriculum Outcomes Assessment was a modest yet consistent predictor across the models. All other significant predictors were unique to the various models. Conclusion. This four-year, population-based modeling study of the relationship of common pharmacy education assessment data to APPE midpoint scores shows a modest correlation with knowledge-based measures. There is a need for greater innovation in this area of research.
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