Antioxidant supplementation and atrial arrhythmias in critically ill trauma patients.

BACKGROUND: The purpose of this study is to determine if antioxidant supplementation influences the incidence of atrial arrhythmias in trauma intensive care unit (ICU) patients. MATERIALS AND METHODS: In this retrospective pre-post study, critically ill injured patients aged ≥18 years, admitted to a single-center trauma ICU for ≥48 hours were eligible for inclusion. The control group consists of patients admitted from January 2000 to September 2005, before routine antioxidant supplementation in our ICU. The antioxidant group consists of patients admitted from October 2005 to June 2011 who received an antioxidant protocol for ≥48 hours. The primary outcome is the incidence of atrial arrhythmias in the first 2 weeks of hospitalization or before discharge. RESULTS: Of the 4699 patients, 1622 patients were in the antioxidant group and 2414 patients were in the control group. Adjusted for age, sex, year, injury severity, past medical history, and medication administration, the unadjusted incidence of atrial arrhythmias was 3.02% in the antioxidant group versus 3.31% in the control group, with no adjusted difference in atrial arrhythmias among those exposed to antioxidants (odds ratio: 1.31 [95% confidence interval: 0.46, 3.75], P = 0.62). Although there was no change in overall mortality, the expected adjusted survival of patients in those without antioxidant therapy was lower (odds ratio: 0.65 [95% confidence interval: 0.43, 0.97], P = 0.04). CONCLUSIONS: ICU antioxidant supplementation did not decrease the incidence of atrial arrhythmias, nor alter the time from admission to development of arrhythmia. A longer expected survival time was observed in the antioxidant group compared with the control group but without a change in overall mortality between groups.

Healthy families study: design of a childhood obesity prevention trial for Hispanic families.

BACKGROUND: The childhood obesity epidemic disproportionately affects Hispanics. This paper reports on the design of the ongoing Healthy Families Study, a randomized controlled trial testing the efficacy of a community-based, behavioral family intervention to prevent excessive weight gain in Hispanic children using a community-based participatory research approach. METHODS: The study will enroll 272 Hispanic families with children ages 5-7 residing in greater Nashville, Tennessee, United States. Families are randomized to the active weight gain prevention intervention or an alternative intervention focused on oral health. Lay community health promoters implement the interventions primarily in Spanish in a community center. The active intervention was adapted from the We Can! parent program to be culturally-targeted for Hispanic families and for younger children. This 12-month intervention promotes healthy eating behaviors, increased physical activity, and decreased sedentary behavior, with an emphasis on parental modeling and experiential learning for children. Families attend eight bi-monthly group sessions during four months then receive information and/or support by phone or mail each month for eight months. The primary outcome is change in children's body mass index. Secondary outcomes are changes in children's waist circumference, dietary behaviors, preferences for fruits and vegetables, physical activity, and screen time. RESULTS: Enrollment and data collection are in progress. CONCLUSION: This study will contribute valuable evidence on efficacy of a childhood obesity prevention intervention targeting Hispanic families with implications for reducing disparities.

A new linear model-based approach for inferences about the mean area under the curve.

Outcome versus time data are commonly encountered in biomedical and clinical research. A common strategy adopted in analyzing such longitudinal data is to condense the repeated measurements on each individual into a single summary statistic such as the area under the response versus time curve. Standard parametric or non-parametric methods are then applied to perform inferences on the conditional area under the curve distribution. Disadvantages of this approach include the disregard of the within-subject variation in the longitudinal profile. We propose a general linear model approach, accounting for the within-subject variance, for estimation and hypothesis tests about the mean areas. Inferential properties of our approach are compared with those from standard methods of analysis using Monte Carlo simulation studies. The impact of missing data, within-subject heterogeneity and homogeneity of variance, are also evaluated. A real working example is used to illustrate the methodology. It is seen that the proposed approach is associated with a significant power advantage over traditional methods, especially when missing data are encountered.