A closed-book, multiple-choice examination following this article tests your under standing of the following objectives:1. Examine the effect of albumin on the diuretic effect of furosemide. 2. Correlate findings of various studies related to the sequential administration of albumin and furosemide. 3. Identify study limitations and opportunities for future research related to administration of furosemide and albumin.To read this article and take the test online, visit www.ajcconline.org and click "CNE Articles in This Issue." No fee for AACN members.

Background Early posttraumatic seizure (PTS) is a significant cause of unfavorable outcomes in traumatic brain injury (TBI). This study was aimed to investigate the incidence and determine a predictive model for early PTS. Materials and Methods A prospective cohort study of 484 TBI patients was conducted. All patients were evaluated for seizure activities within 7 days after the injury. Risk factors for early PTS were identified using univariate analysis. The candidate risk factors with p < 0.1 were selected into multivariable logistic regression analysis to identify predictors of early PTS. The fitting model and the power of discrimination with the area under the receiver operating characteristic (AUROC) curve were demonstrated. The nomogram for prediction of early PTS was developed for individuals. Results There were 27 patients (5.6%) with early PTS in this study. The final model illustrated chronic alcohol use (odds ratio [OR]: 4.06, 95% confidence interval [CI]: 1.64–10.07), epidural hematoma (OR: 3.98, 95% CI: 1.70–9.33), and Glasgow Coma Scale score 3–8 (OR: 3.78, 95% CI: 1.53–9.35) as predictors of early PTS. The AUROC curve was 0.77 (95% CI: 0.66–0.87). Conclusions The significant predictors for early PTS were chronic alcohol use, epidural hematoma, and severe TBI. Our nomogram was considered as a reliable source for prediction.

In Thailand during the early 1990s, there was a need for an increased number of pharmacists and expansion of their knowledge and skills to address the need of the nation. Leaders of the Thai pharmacy education community at the time crafted a long‐term plan aiming to expand the pharmacy educator workforce at a national scale through the financial support of the Royal Thai Government. This led to the establishment of the United States‐Thai Consortium for the Development of Pharmacy Education in Thailand in 1994. The aim of the Consortium was to advance pharmacy education in Thailand through the support of leading U.S. pharmacy schools using both short‐term and long‐term trainings. Twenty plus years later, pharmacy education and practice in Thailand have changed dramatically. The number of faculties (schools) of pharmacy in Thailand has increased from 10 in 1993 to 19 in 2013. The ratio of pharmacists to population has decreased from 1:10532 in 1994 to 1:2261 in 2016. The professional pharmacy curriculum has changed from a 5‐year bachelor to a 6‐year Doctor of Pharmacy (Pharm.D.) degree. The role of Thai pharmacists has been endorsed by national health service initiatives and practice guidelines. Currently, 7 universities offer residency/fellowship programs. The 8 Thai founding institutions of the Consortium are now publishing over 500 papers in high‐quality international journals annually. In summary, pharmacy education, practice, and research in Thailand have improved dramatically through the U.S.‐Thai Pharmacy Consortium. This bi‐national model of knowledge and skill transfer may serve as an example for how a large‐scale international partnership can facilitate a rapid and positive transformation of pharmacy in a developing country. Local adjustment and adaptation are required to reflect national identity and to suit the local context.

Purpose: Serum digoxin concentration (SDC) monitoring may be unavailable in some healthcare settings. Predicted SDC comes into play in the efficacy and toxicity monitoring of digoxin. Renal function is the important parameter for predicting SDC. This study was conducted to compare measured and predicted SDC when using creatinine clearance (CrCl) from Cockcroft-Gault (CG) equation and estimated glomerular filtration rate (eGFR) calculated from CKD-Epidemiology Collaboration (CKD-EPI), re-expressed Modification of Diet in Renal Disease (Re-MDRD4), Thai-MDRD4, and Thai-eGFR equations in Sheiner's and Konishi's pharmacokinetic models. Patients and methods: In this retrospective study, patients with cardiovascular disease with a steady-state of SDC within 0.5-2.0 mcg/L were enrolled. CrCl and studied eGFR adjusted for body surface area (BSA) were used in the models to determine the predicted SDC. The discrepancies of the measured and the predicted SDC were analyzed and compared. Results: One hundred and twenty-four patients ranging in age from 22 to 88 years (median 60 years, IQR 50.2, 69.2) were studied. Their serum creatinine ranged from 0.40 to 1.80 mg/dL (median 0.90 mg/dL, IQR 0.79, 1.10). The mean±SD of measured SDC was 1.12±0.34 mcg/L. In the Sheiner's model, the mean predicted SDC was calculated by using the CG and the BSA adjusted CKD-EPI equations and was not different when compared with the measured levels (1.10±0.36 mcg/L (p=0.669) and 1.08±0.42 mcg/L (p=0.374), respectively). The CG, CKD-EPI, and Re-MDRD4 equations were a better fit for patients with creatinine ≥0.9 mg/dL for prediction with minimal errors. In the Konishi's model, the predicted SDC using the CG and the studied eGFR equation was lower than the measured SDC (p<0.05). Conclusion: In Sheiner's model, the CG and the BSA adjusted CKD-EPI equations should be used for predicting SDC, especially in patients with serum creatinine ≥0.9 mg/dL. The other studied eGFRs underestimated SDC in both Sheiner's and Konishi's model.