Context: Very preterm neonates (VPNs) are unable to digest breast milk and therefore rely on parenteral nutrition (PN) formulations. This systematic review was prepared following PRISMA-P 2015 guidelines. For the purpose of this review, desirable mean plasma arginine concentration is defined as ≥80 micromoles/L.Objective: The review was performed to answer the following research question: “In VPNs, are high amounts of arginine in PN, compared with low amounts of arginine, associated with appropriate circulating concentrations of arginine?” Therefore, the aims were to 1) quantify the relationship between parenteral arginine intakes and plasma arginine concentrations in PN-dependent VPNs; 2) identify any features of study design that affect this relationship; and 3) estimate the target parenteral arginine dose to achieve desirable preterm plasma arginine concentrations.Data Sources: The PubMed, Scopus, Web of Science, and Cochrane databases were searched regardless of study design; review articles were not included.Data Extraction: Only articles that discussed amino acid (AA) intake and measured plasma AA profile post PN in VPNs were included. Data were obtained using a data extraction checklist that was devised for the purpose of this review.Data Analysis: Twelve articles met the inclusion criteria. The dose–concentration relationship of arginine content (%) and absolute arginine intake (mg/(kg × d)) with plasma arginine concentrations showed a significant positive correlation (P < 0.001).Conclusion: Future studies using AA solutions with arginine content of 17%–20% and protein intakes of 3.5–4.0 g/kg per day may be needed to achieve higher plasma arginine concentrations.

Context: Pain is a common and distressing symptom among cancer patients. Opioid analgesics are the mainstay of cancer pain management, and adequate adherence plays an important role in achieving good pain control. Purpose: To determine the level of adherence to opioid analgesics in patients with cancer pain and to identify factors that may influence the adherence. Patient and Methods: This was a cross-sectional study conducted from March to June 2018 at two tertiary care hospitals in Malaysia. Study instruments consisted of a set of validated questionnaires; the Medication Compliance Questionnaire, Brief Pain Inventory and Pain Opioid Analgesic Beliefs─Cancer scale. Results: A total of 134 patients participated in this study. The patients’ adherence scores ranged from 52– 100%. Factors with a moderate, statistically significant negative correlation with adherence were negative effect beliefs (r_s= − 0.53, p< 0.001), pain endurance beliefs (r_s = − 0.49, p< 0.001) and the use of aqueous morphine (r_s = − 0.26, p=0.002). A multiple linear regression model on these predictors resulted in a final model which accounted for 47.0% of the total variance in adherence (R² = 0.47, F (7, 126) = 15.75, p< 0.001). After controlling for other variables, negative effect beliefs were the strongest contributor to the model (β = − 0.39, p< 0.001) and uniquely explained 12.3% of the total variance. Conclusion: The overall adherence to opioid analgesics among Malaysian patients with cancer pain was good. Negative effects beliefs regarding cancer pain and opioids strongly predicted adherence.

Introduction Medication administration errors (MAEs) are the most common type of medication error. Furthermore, they are more common among neonates as compared with adults. MAEs can result in severe patient harm, subsequently causing a significant economic burden to the healthcare system. Targeting and prioritising neonates at high risk of MAEs is crucial in reducing MAEs. To the best of our knowledge, there is no predictive risk score available for the identification of neonates at risk of MAEs. Therefore, this study aims to develop and validate a risk prediction model to identify neonates at risk of MAEs. Methods and analysis This is a prospective direct observational study that will be conducted in five neonatal intensive care units. A minimum sample size of 820 drug preparations and administrations will be observed. Data including patient characteristics, drug preparation-related and administration-related information and other procedures will be recorded. After each round of observation, the observers will compare his/her observations with the prescriber’s medication order, hospital policies and manufacturer’s recommendations to determine whether MAE has occurred. To ensure reliability, the error identification will be independently performed by two clinical pharmacists after the completion of data collection for all study sites. Any disagreements will be discussed with the research team for consensus. To reduce overfitting and improve the quality of risk predictions, we have prespecified a priori the analytical plan, that is, prespecifying the candidate predictor variables, handling missing data and validation of the developed model. The model’s performance will also be assessed. Finally, various modes of presentation formats such as a simplified scoring tool or web-based electronic risk calculators will be considered.