Evaluating Prescriber Adherence to Guideline-Based Treatment Pathways of a Newly Initiated Antimicrobial Stewardship Program at a Rehabilitation Hospital

Background: Inappropriate use of antimicrobials in the healthcare setting is associated with consequences including antimicrobial resistance, Clostridium difficile infection (CDI), adverse drug reactions, and increased healthcare costs. To combat this, hospitals are creating antimicrobial stewardship programs (ASPs) which seek to optimize antimicrobial utilization. To date, no studies have been done to assess adherence to an ASP in a rehabilitation hospital setting. The objective of this study is to evaluate prescriber compliance to treatment pathways for common infections before and after ASP implementation. Methods: This was a retrospective cohort study of patients admitted to the Rehabilitation Hospital of Indiana (RHI) who received an antibiotic between October 1, 2015-December 31, 2015 (pre-ASP group) and January 1, 2016-September 30, 2016 (post-ASP group) for one of the following indications: pneumonia, urinary tract infection, CDI, bone and joint infection, skin or skin structure infection, febrile neutropenia, or central/peripherally inserted central catheter line bloodstream infection. Data extracted from the hospital’s electronic medical record system included patient demographic and clinical information, laboratory data, culture and susceptibility results, and antibiotic information. The primary outcome of this study was prescriber compliance to treatment pathways defined as correct drug based on the documented indication before and after the implementation of the antimicrobial stewardship program on January 1, 2016. Descriptive statistics were performed to analyze baseline characteristics and culture data, as well as antimicrobial class, indication, and overall compliance to the guideline-based treatment pathways. Results: Data was extracted from the hospital’s electronic medical record system for 381 patients (n=381) who received an antibiotic at RHI. There were 121 and 260 patients included in the pre- and post-ASP study groups, respectively. Urinary tract infections were the most common infection for which antibiotics were prescribed (n=293; 76.9%). The three most common antibiotics prescribed were ciprofloxacin (n=101; 26.5%), sulfamethoxazole/trimethoprim (n=81; 21.3%), and nitrofurantoin (n=49; 12.9%). Compliance was found to be 81% in the pre-ASP group and 78.5% in the post-ASP group (p=0.571). Overall compliance was found to be the highest (100% in both pre- and postASP groups) for osteomyelitis infections and CDI. Urinary tract infections had the next highest rate of compliance in both the pre- and post-ASP groups (86.5% and 81.7% respectively). Conclusions: No difference in rates of prescriber compliance to guideline-based treatment pathways was found in the pre- and post-ASP groups. Urinary tract infections were found to be the most common indication requiring antimicrobial usage at RHI and had the third highest rate of compliance out of the infections included in this study. Our study highlights a need for further investigation regarding the impact of the ASP on appropriate antimicrobial dose, duration of therapy, administration, and de-escalation based on culture data. Additionally, our study identified a need for formal prescriber education focusing on how to utilize the treatment pathways, especially for those infections with the lowest compliance rates

A biophysical model of endocannabinoid-mediated short term depression in hippocampal inhibition

Memories are believed to be represented in the synaptic pathways of vastly interconnected networks of neurons. The plasticity of synapses, that is, their strengthening and weakening depending on neuronal activity, is believed to be the basis of learning and establishing memories. An increasing number of studies indicate that endocannabinoids have a widespread action on brain function through modulation of synap–tic transmission and plasticity. Recent experimental studies have characterised the role of endocannabinoids in mediating both short- and long-term synaptic plasticity in various brain regions including the hippocampus, a brain region strongly associated with cognitive functions, such as learning and memory. Here, we present a biophysically plausible model of cannabinoid retrograde signalling at the synaptic level and investigate how this signalling mediates depolarisation induced suppression of inhibition (DSI), a prominent form of shortterm synaptic depression in inhibitory transmission in hippocampus. The model successfully captures many of the key characteristics of DSI in the hippocampus, as observed experimentally, with a minimal yet sufficient mathematical description of the major signalling molecules and cascades involved. More specifically, this model serves as a framework to test hypotheses on the factors determining the variability of DSI and investigate under which conditions it can be evoked. The model reveals the frequency and duration bands in which the post-synaptic cell can be sufficiently stimulated to elicit DSI. Moreover, the model provides key insights on how the state of the inhibitory cell modulates DSI according to its firing rate and relative timing to the post-synaptic activation. Thus, it provides concrete suggestions to further investigate experimentally how DSI modulates and is modulated by neuronal activity in the brain. Importantly, this model serves as a stepping stone for future deciphering of the role of endocannabinoids in synaptic transmission as a feedback mechanism both at synaptic and network level

The Challenges of Genome-Wide Interaction Studies : Lessons to Learn from the Analysis of HDL Blood Levels

Genome-wide association studies (GWAS) have revealed 74 single nucleotide polymorphisms (SNPs) associated with high-density lipoprotein cholesterol (HDL) blood levels. This study is, to our knowledge, the first genome-wide interaction study (GWIS) to identify SNP×SNP interactions associated with HDL levels. We performed a GWIS in the Rotterdam Study (RS) cohort I (RS-I) using the GLIDE tool which leverages the massively parallel computing power of Graphics Processing Units (GPUs) to perform linear regression on all genome-wide pairs of SNPs. By performing a meta-analysis together with Rotterdam Study cohorts II and III (RS-II and RS-III), we were able to filter 181 interaction terms with a p-value<1 · 10-8 that replicated in the two independent cohorts. We were not able to replicate any of these interaction term in the AGES, ARIC, CHS, ERF, FHS and NFBC-66 cohorts (Ntotal = 30,011) when adjusting for multiple testing. Our GWIS resulted in the consistent finding of a possible interaction between rs774801 in ARMC8 (ENSG00000114098) and rs12442098 in SPATA8 (ENSG00000185594) being associated with HDL levels. However, p-values do not reach the preset Bonferroni correction of the p-values. Our study suggest that even for highly genetically determined traits such as HDL the sample sizes needed to detect SNP×SNP interactions are large and the 2-step filtering approaches do not yield a solution. Here we present our analysis plan and our reservations concerning GWIS.Peer reviewe

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Impact of pre-matriculation course withdrawals on first year pharmacy school success

Objective:Determine the impact of pre-matriculation course withdrawals on first year pharmacy school (P1) success, defined as on-time progression to the second (P2) year without remediation. Methods:A retrospective review of students matriculating to a four-year private institution from 2018 to 2021 was conducted. Potential predictors of P1 year success including age, sex, highest degree achieved, pre-matriculation grade point average (GPA), and course withdrawals were collected. Results:Bivariate analysis indicates that age, cumulative GPA, science GPA, and pre-matriculation course withdrawals were significantly different between students who were successful versus unsuccessful in the first year of pharmacy school. Out of 220 students analyzed, 40.9% (n=90) were unsuccessful in the first year. Of those 90 P1 students, 52% did not progress to the P2 year, and 48% progressed but required course remediation. Multivariate analysis demonstrated that independent predictors of P1 success included cumulative GPA and having less than two pre-matriculation course withdrawals. In addition, the number of pre-matriculation course withdrawals, cumulative GPA, and having a bachelor\u27s degree or higher were independent predictors of P1 GPA. Conclusion:Pre-matriculation course withdrawal was an independent predictor of both P1 success and P1 GPA. Students with less than two pre-matriculation course withdrawals were more likely to be successful during the first year of pharmacy school. College of pharmacy admission committees may consider pre-matriculation course withdrawal frequency when making admission decisions or to identify students that may need additional academic support during the first year of pharmacy school