Screening for QT Prolongation in the Emergency Department: Is There a Better “Rule of Thumb?”

Introduction: Identification of QT prolongation in the emergency department (ED) is critical for appropriate monitoring, disposition, and treatment of patients at risk for torsades de pointes (TdP). Unfortunately, identifying prolonged QT is not straightforward. Computer algorithms are unreliable in identifying prolonged QT. Manual QT-interval assessment methods, including QT correction formulas and the QT nomogram, are time-consuming and are not ideal screening tools in the ED. Many emergency clinicians rely on the “rule of thumb” or “Half the RR” rule (Half-RR) as an initial screening method, but prior studies have shown that the Half-RR rule performs poorly as compared to other QT assessment methods. We sought to characterize the problems associated with the Half-RR rule and find a modified screening tool to more safely assess the QT interval of ED patients for prolonged QT.Methods: We created graphs comparing the prediction of the Half-RR rule to other common QT assessment methods for a spectrum of QT and heart rate pairs. We then proposed various modifications to the Half-RR rule and assessed these modifications to find an improved “rule of thumb.”Results: When compared to other methods of QT correction, the Half-RR rule appears to be more conservative at normal and elevated heart rates, making it a safe initial screening tool. However, in bradycardia, the Half-RR rule is not sufficiently sensitive in identifying prolonged QT. Adding a fixed QT cutoff of 485 milliseconds (ms) increases the sensitivity of the rule in bradycardia, creating a safer initial screening tool.Conclusion: For a rapid and more sensitive screening evaluation of the QT interval on electrocardiograms in the ED, we propose combining use of the Half-RR rule at normal and elevated heart rates with a fixed uncorrected QT cutoff of 485 ms in bradycardia

Body Mass Index, Sleep Quality, Stress Conditions Determine Menstrual Cycles Among Female Adolescents

Menstrual cycles are an important indicator of women's health. Menstrual cycles can be affected by body mass index (BMI), sleep quality, and stress. This study aimed to analyse the relationship between BMI, sleep quality, stress and the menstrual cycle. The subjects of this research were the female adolescents at the age of at least 15 years old that had experienced menstruation for at least 2 years. The dependent variable is the menstrual cycle while the independent variables are BMI, sleep quality, and stress conditions. Observational analytic research method with Cross sectional design was used in this research. The subjects were 148 female students. The BMI data were obtained through the anthropometric measurement. The sleep quality data were taken with PSQI questionnaire, and the stress condition data obtained from PSS-10 questionnaire which were then analysed using Chi-Square test and Logistic Regression. Results of study showed that there is a significant relationship between BMI, sleep quality, stress conditions and the adolescent menstrual cycle. The results of the multivariate analysis showed that the female adolescents with abnormal BMI are at risk of having menstrual cycle disorders 1.91 times. The adolescents with poor sleep quality are at risk experiencing menstrual cycle disorders 2.05 times, and the adolescents with stress conditions at risk of the menstrual cycle disorders 2.26 times. There is a relationship between BMI, sleep quality, stress conditions and the menstrual cycle. Stress conditions most influence the regularity of the menstrual cycle

Compelling the Courts to Question Gonzalez v. O Centro: A Public Harms Approach to Free Exercise Analysis

Part I will set forth the analytical framework established by the Supreme Court in the RFRA and RLUIPA contexts before 0 Centro. This Part will provide a brief background to RFRA and RLUIPA and set forth the definition of compelling interest before 0 Centro. Part II will focus on the decision in 0 Centro; specifically, how the Supreme Court\u27s redefinition of compelling interest significantly elevates the government\u27s burden. Part III will compare the government\u27s chance of winning on a compelling interest argument before 0 Centro with the chance of winning in its wake. This Part will discuss the merits, flaws, and methodology of the case survey at the core of this Article, and will conclude that outside the prison context the government has little chance of satisfying its burden. Finally, this Article will propose that courts should use the fact-specific, person-specific inquiry used in 0 Centro in conjunction with the more general, public-protecting compelling interest test used in United States v. Lee, Hernandez v. Commissioner of Internal Revenue, and Braunfield v. Brown

Firm Response to Low-Reimbursement Patients in the Market for Unscheduled Outpatient Care

Americans spent 13,400 person-years waiting in emergency departments (EDs) in 2009 alone, a figure that has been increasing at a compounded rate of 3.5% per year since at least the early 1990s. Furthermore, the quantity of emergency department services demanded has increased by 3.1% annually, but the supply of ED services has not increased concomitantly. This dissertation develops a theoretical model which explains this lack of supply response. In the model, consistent with anecdotal and cross-sectional evidence, hospitals are constrained from setting individual wait times based on non-clinical factors. However, the hospital chooses an overall set of policies (staffing levels, adoption of operations management innovations, etc.) which produces a hospital-wide baseline wait time. The hospital\u27s wait time is endogenous to the mix of patient profitabilities. Demand depends on the time price of services. The model predicts that higher wait times result from increased proportions of Medicaid and uninsured patients. A novel census of emergency department wait times in two states (MA, NJ) is used to test these predictions. First, the model\u27s assumption that hospitals are constrained in setting individual wait times based on profitability is supported by cross-sectional regression coefficients: hospitals with 50 percentage point greater uninsurance rates have 26.0 minute longer wait times (p\u3c.01; national mean wait time is 58 minutes), whereas conditional on hospital uninsurance rate individuals who are uninsured are not shown to have longer wait times (coefficient of 0.86 minutes, p=0.13). Next I use cross-sectional models which instrument for area uninsurance/Medicaid rates, models assessing the effect of entry of urgent care clinics into the market (since these clinics see predominantly insured, less severely injured patients), and triple-difference estimates of the differential effect of Massachusetts\u27 insurance expansion across the change in hospital insurance mix. Results support the theoretical model\u27s conclusions. The recent national expansion of insurance may mitigate the negative externality on the privately insured, providing a substantial welfare gain to those who do not otherwise benefit from the Affordable Care Act. Given the uncertainty as to the marginal costs of ED care, however, the full welfare implications are unknown

SOCIAL CONNECTEDNESS, COMMUNITY PARTICIPATION, AND HEALTH

Persons with disabilities (PWD) experience social, economic, and environmental disadvantages which have contributed to marginalization, health disparities, and challenges with community participation. Various forms of social closeness appear to serve as protective factors against physical and mental health for the non-disabled population, but it is unclear whether social connectedness is associated with community participation and health for PWD. This within-subjects, correlational design study used survey and Ecological Momentary Assessment (EMA) data from a sample of persons with mobility impairments (MI) to determine the direct and indirect effect of social connectedness and participation on health, the barriers and facilitators to participation that contribute to social connectedness, and whether social connectedness explains well-being during socializing experiences. Results indicated that social connectedness predicted health related outcomes and mediated the relationship between measures of community participation and mental health. Social connectedness can be best predicted by examining one\u27s sense of community integration and the severity of personal and environmental barriers. Finally, the effect of socializing on in-the-moment and later-day well-being does not seem to be significantly affected by social connectedness. Limitations and future directions are discussed