High-risk motor vehicle drivers engage in more risk behaviors than motorcyclists.

OBJECTIVE: The purpose of this study was to characterize and compare risk behaviors between motorcyclists and motor vehicle drivers who were involved in accidents and required hospitalization. The study focused on patients who were recently involved in motorcycle collisions (MCCs) and motor vehicle collisions (MVCs). METHODS: We identified 63 patients involved in MCCs and 39 patients involved in MVCs who were admitted to our level-1 trauma center from April 2014 to September 2015. These 102 patients completed a questionnaire designed to evaluate risky driving behaviors. Pearson's chi-squared tests and unpaired two-tailed t-tests were used to evaluate categorical and normally distributed continuous variables, respectively. Multivariable linear regression was used to analyze predictors of risk behavior. Significance was set at p < 0.05. RESULTS: When compared to patients involved in an MCC, patients involved in MVCs were more likely to be female (p = 0.007), drive more frequently (p < 0.001), and never perceive the risk of an accident (p = 0.036). MVC patients were more likely to have admitted to substance use on the day of the accident (p = 0.030), historically drive under the influence of drugs (p = 0.031), drive while tired (p < 0.001), drive while text messaging (p < 0.001), and speed while overtaking vehicles (p = 0.011). Overall, MVC patients engaged in more risk behaviors (3.3 ± 1.3 vs. 2.0 ± 1.5; p < 0.001) and were more likely to engage in multiple risk behaviors (p < 0.001). MVCs were associated with increased risk behavior, even after controlling for protective behaviors, driving history, and demographics (p = 0.045). CONCLUSIONS: Within our cohort of trauma patients at our institution, motor vehicle drivers were more likely than motorcyclists to engage in any one risk behavior and engage in a higher number of risk behaviors. In addition, motor vehicle drivers perceived their risk of a potential accident as lower than riding a motorcycle. Education initiatives should focus on motor vehicle driver safety interventions that reduce risk behaviors.

Predictors of Family Dissatisfaction with Support During Neurocritical Care Shared Decision-Making.

BACKGROUND: There is a critical need to improve support for families making difficult shared decisions about patient care with clinicians in the neuroscience ICU (neuro-ICU). The aim of this study is to identify patient- and family-related factors associated with dissatisfaction with shared decision-making support among families of neuro-critically ill patients. METHODS: We conducted a retrospective observational cohort study using survey data that had been collected from a consecutive sample of family members of patients in the neuro-ICU (one family member per patient) at two US academic centers. Satisfaction with shared decision-making support on ICU discharge had been measured among family members using one specific Likert scale item on the Family Satisfaction in the ICU 24 survey, a validated survey instrument for families of patients in the ICU. We dichotomized top-box responses for this particular item as an outcome variable and identified available patient- and family-related covariates associated with dissatisfaction (i.e., less than complete satisfaction) via univariate and multivariate analyses. RESULTS: Among 355 surveys, 180 (49.5%) of the surveys indicated dissatisfaction with support during decision-making. In a multivariate model, no preexisting characteristics of families or patients ascertainable on ICU admission were predictive of dissatisfaction. However, among family factors determined during the ICU course, experiencing three or fewer formal family meetings (odds ratio 1.93 [confidence interval 1.13-3.31]; p = 0.01) was significantly predictive of dissatisfaction with decisional support in this cohort with an average patient length of stay of 8.6 days (SD 8.4). There was also a trend toward a family's decision to keep a patient as full code, without treatment limitations, being predictive of dissatisfaction (odds ratio 1.80 [confidence interval 0.93-3.51]; p = 0.08). CONCLUSIONS: Family dissatisfaction with neuro-ICU shared decision-making support is not necessarily predicted by any preexisting family or patient variables but appears to correlate with participating in fewer formal family meetings during ICU admission. Future studies to improve family satisfaction with neurocritical care decision-making support should have broad inclusion criteria for participants and should consider promoting frequency of family meetings as a core strategy.

A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics.

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD's correlation with cognitive traits and hints at an autoimmune component for ALS.

Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease.

Continuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations. After validating our annotations with a catalog of tissue-specific non-coding elements previously identified in the literature, we apply our method using data from 127 different cell and tissue types to present an atlas of heritability enrichment across 45 different GWAS traits. We show that broader organ system categories (e.g. immune system) increase statistical power in identifying biologically relevant tissue types for complex diseases while annotations of individual cell types (e.g. monocytes or B-cells) provide deeper insights into disease etiology. Additionally, we use our GenoSkyline-Plus annotations in an in-depth case study of late-onset Alzheimer's disease (LOAD). Our analyses suggest a strong connection between LOAD heritability and genetic variants contained in regions of the genome functional in monocytes. Furthermore, we show that LOAD shares a similar localization of SNPs to monocyte-functional regions with Parkinson's disease. Overall, we demonstrate that integrated genome annotations at the single tissue level provide a valuable tool for understanding the etiology of complex human diseases. Our GenoSkyline-Plus annotations are freely available at http://genocanyon.med.yale.edu/GenoSkyline.

Diagnosis and management of urinary tract infections in the emergency department.

Urinary tract infections are a heterogeneous group of disorders, involving infection of all or part of the urinary tract, and are defined by bacteria in the urine with clinical symptoms that may be acute or chronic. Approximately 1 million urinary tract infections are treated every year in United States emergency departments. The female-to-male ratio is 6:1. Urinary tract infections are categorized as upper versus lower tract involvement and as uncomplicated versus complicated. The emergency clinician must carefully categorize the infection and take into account patient host factors to optimally treat and disposition patients. A working knowledge of local or at least national susceptibility patterns of the most likely pathogens is essential. A variety of special populations exist that require special management, including pregnant females, patients with anatomic abnormalities, and instrumented patients.