ABSTRACT IMPACT: This is the first study to use QUINT analyses to examine heterogeneity of treatment effect for group medical visits among individuals with type 2 diabetes. QUINT is a data driven method that assumes no a priori assumptions regarding effect moderators - an important step in the path towards personalized medicine. OBJECTIVES/GOALS: To examine heterogeneity of treatment effect (HTE) in Jump Start, a trial that compared the effectiveness of group medical visits (GMVs) focused on medication management only versus the addition of intensive weight management (WM) on glycemic control for patients with type 2 diabetes and body mass index >=27kg/m^2. METHODS/STUDY POPULATION: Jump Start patients (n=263) were randomized to a GMV-based medication management plus low carbohydrate diet-focused WM program (WM/GMV; n = 127) or GMV-based medication management only (GMV; n = 136) for diabetes control. We used QUalitative INteraction Trees (QUINT), a tree-based clustering method, to determine if there were subgroups of patients who derived greater benefit from either WM/GMV or GMV. Subgroup predictors included 32 baseline demographic, clinical, and psychosocial factors. Outcome was hemoglobin A1c (HbA1c). We conducted internal validation via bootstrap resampling to estimate bias in the range of mean outcome differences among arms. RESULTS/ANTICIPATED RESULTS: QUINT analyses indicated that for patients who had not previously attempted weight loss, WM/GMV resulted in better glycemic control than GMV alone (mean difference in HbA1c improvement = 1.48%). For patients who had previously attempted weight loss and had lower cholesterol and blood urea nitrogen levels, GMV alone was better than WM/GMV (mean difference in HbA1c improvement = 1.51%). Internal validation resulted in moderate corrections in the mean HbA1c differences between arms; however, differences remained in the clinically significant range. DISCUSSION/SIGNIFICANCE OF FINDINGS: Among patients with diabetes and BMI>=27kg/m^2, a low-carbohydrate, weight loss focus may better improve HbA1c in those who have never attempted weight loss. A medication management focus may be better in those who have attempted weight loss and have lower cholesterol and blood urea nitrogen.

OBJECTIVES/GOALS: The impact of baseline BMI on glycemic response to group medical visits (GMV) and weight management (WM)-based interventions is unclear. Our objective is to determine how baseline BMI class impacts patient responses to GMV and interventions that combine WM/GMV. METHODS/STUDY POPULATION: We will perform a secondary analysis of Jump Start, a randomized, controlled trial that compared the effectiveness of a GMV-based low carbohydrate diet-focused WM program (WM/GMV) to traditional GMV-based medication management (GMV) on diabetes control. The primary and secondary outcomes will be change in hemoglobin A1c (HbA1c) and weight at 48 months, respectively. Study participants will be stratified into BMI categories defined by BMI 27-29.9kg/m2, 30.0-34.9kg/m2, 35.0-39.9kg/m2, and ≥40.0kg/m2. Hierarchical mixed models will be used to examine the differential impact of the WM/GMV intervention compared to GMV on changes in outcomes by BMI class category. RESULTS/ANTICIPATED RESULTS: Jump Start enrolled 263 overweight Veterans (BMI ≥ 27kg/m2) with type 2 diabetes. At baseline, mean BMI was 35.3 and mean HbA1c was 9.1. 14.5% were overweight (BMI 27–29.9) and 84.5% were obese (BMI ≥ 30). The proposed analyses are ongoing. We anticipate that patients in the higher BMI obesity classes will demonstrate greater reductions in HbA1c and weight with the WM/GMV intervention relative to traditional GMV. DISCUSSION/SIGNIFICANCE OF IMPACT: This work will advance the understanding of the relationship between BMI and glycemic response to targeted interventions, and may ultimately provide guidance for interventions for type 2 diabetes.

Field investigations were conducted for 2 yr to characterize the responses of wheat, kidney bean, field corn, squash, okra, potato, and banana to soil-applied hexazinone, imazapyr, tebuthiuron, and triclopyr at rates used for control of woody perennial plants. Test species were planted at seven selected intervals through the first and second years after herbicide applications to assess residual herbicide activity. Tebuthiuron treatments of 2.2 kg ai ha−1 were not tolerated by any bioassay species planted 436 d after application (DAA). Only potato was tolerant of residual hexazinone 436 DAA. All species except banana generally tolerated residual imazapyr and triclopyr by the second growing season. Indigenous plants repopulated all herbicide-treated areas, except those treated with tebuthiuron, by the third growing season. Plots treated with tebuthiuron were 90% free of indigenous plants 40 mo after herbicide application.

We report a severe outbreak of Clostridium difficile infection. According to a chart review, half of the patients who received treatment for bacterial pneumonia before they developed C. difficile infection may not have had pneumonia. Excessive use of the hospital's new pneumonia care plan during the influenza season may have contributed to the intensity of this outbreak.

The points of a graph G will form clusters as a result of a flow process. Initially, points i of G own resources xi which are i.i.d. random real numbers. Afterwards, resources flow between points, but always from a point to a neighbor that has accumulated a larger total resource. Thus points with small resource tend to lose it and points with large resource tend to gain. Eventually the flow stops with only two kinds of points, nulls with no resource left and absorbers with such large resource that no neighbor can take it. The final resource at an absorber is a sum of certain initial resources xi, and the corresponding points i form one cluster. Analytical results are obtainable when G is the chain of integer points on the line. Probability distributions are derived for the distance between consecutive absorbers and the size of a cluster. Indeed these distributions do not involve the given distribution for the xi. The Laplace transform of the distribution of final resources at absorbers is derived but the distribution itself is obtained by a simulation. For general graphs G only partial results are obtained.