Comparative incidence of acute kidney injury in patients on vancomycin therapy in combination with cefepime, piperacillin-tazobactam or meropenem.

Recent studies have shown that the incidence of nephrotoxicity increases when vancomycin is combined with a beta-lactam antibiotic. The objective of this study was to compare the incidence of acute kidney injury (AKI) in adult patients who received vancomycin with either piperacillin-tazobactam (VPT), cefepime (VC), or meropenem (VM). This was a single center retrospective chart review. Patients were included if they were 18 years or older, received 48 hours of combination therapy and antibiotics were started within 24 hours of each other. Exclusion criteria were receiving more than one combination of antibiotics, serum creatinine > 1.2 mg/dL, AKI at the time of inclusion, or any form of renal replacement therapy. Two hundred patients met inclusion criteria. A total of 27 (13%) patients experienced AKI. The incidence of AKI was 21.6%, 9%, and 7.4% in the VPT, VC and VM groups, respectively. A patient who received VPT was 5 times more likely to develop AKI when compared to a patient who received VC (adjusted OR 5.09 95% CI (1.51-17.08), p = 0.008) and 7 times more likely to develop AKI when compared to VM (adjusted OR 7.03 95% CI (1.97-28.08), p = 0.002). This study found a statistically significant difference in the incidence of AKI in patient receiving VPT when compared to VC or VM. This finding supports the need for careful monitoring of renal function in patients receiving VPT therapy and routine evaluation for de-escalation of antimicrobial therapy.

VMAT2 Inhibitors and the Path to Ingrezza (Valbenazine).

The dopaminergic system plays a key role in the central nervous system, regulating executive function, arousal, reward, and motor control. Dysregulation of this critical monoaminergic system has been associated with diseases of the central nervous system including schizophrenia, Parkinson's disease, and disorders such as attention deficit hyperactivity disorders and addiction. Drugs that modify the dopaminergic system by modulating the activity of dopamine have been successful in demonstrating clinical efficacy by providing treatments for these diseases. Specifically, antipsychotics, both typical and atypical, while acting on a number of monoaminergic systems in the brain, primarily target the dopamine system via inhibition of postsynaptic dopamine receptors. The vesicular monoamine transporter 2 (VMAT2) is an integral presynaptic protein that regulates the packaging and subsequent release of dopamine and other monoamines from neuronal vesicles into the synapse. Despite acting on opposing sides of the synapse, both antipsychotics and VMAT2 inhibitors act to decrease the activity of central dopaminergic systems. Tardive dyskinesia is a disorder characterized by involuntary repetitive movements and thought to be a result of a hyperdopaminergic state precipitated by the use of antipsychotics. Valbenazine (NBI-98854), a novel compound that selectively inhibits VMAT2 through an active metabolite, has been developed for the treatment of tardive dyskinesia and is the first drug approved for the treatment of this disorder. This chapter describes the process leading to the discovery of valbenazine, its pharmacological characteristics, along with preclinical and clinical evidence of its efficacy.

Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events.

OBJECTIVES: Many healthcare providers have implemented patient safety event reporting systems to better understand and improve patient safety. Reviewing and analyzing these reports is often time consuming and resource intensive because of both the quantity of reports and length of free-text descriptions in the reports. METHODS: Natural language processing (NLP) experts collaborated with clinical experts on a patient safety committee to assist in the identification and analysis of medication related patient safety events. Different NLP algorithmic approaches were developed to identify four types of medication related patient safety events and the models were compared. RESULTS: Well performing NLP models were generated to categorize medication related events into pharmacy delivery delays, dispensing errors, Pyxis discrepancies, and prescriber errors with receiver operating characteristic areas under the curve of 0.96, 0.87, 0.96, and 0.81 respectively. We also found that modeling the brief without the resolution text generally improved model performance. These models were integrated into a dashboard visualization to support the patient safety committee review process. CONCLUSIONS: We demonstrate the capabilities of various NLP models and the use of two text inclusion strategies at categorizing medication related patient safety events. The NLP models and visualization could be used to improve the efficiency of patient safety event data review and analysis.