Virginia Commonwealth University

About: Virginia Commonwealth University is a education organization based out in Richmond, Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 23822 authors who have published 49587 publications receiving 1787046 citations. The organization is also known as: VCU.

Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics.

Abstract: Background Traumatic brain injury (TBI) is a leading cause of death and disability. A reliable prediction of outcome on admission is of great clinical relevance. We aimed to develop prognostic models with readily available traditional and novel predictors. Methods and Findings Prospectively collected individual patient data were analyzed from 11 studies. We considered predictors available at admission in logistic regression models to predict mortality and unfavorable outcome according to the Glasgow Outcome Scale at 6 mo after injury. Prognostic models were developed in 8,509 patients with severe or moderate TBI, with cross-validation by omission of each of the 11 studies in turn. External validation was on 6,681 patients from the recent Medical Research Council Corticosteroid Randomisation after Significant Head Injury (MRC CRASH) trial. We found that the strongest predictors of outcome were age, motor score, pupillary reactivity, and CT characteristics, including the presence of traumatic subarachnoid hemorrhage. A prognostic model that combined age, motor score, and pupillary reactivity had an area under the receiver operating characteristic curve (AUC) between 0.66 and 0.84 at cross-validation. This performance could be improved (AUC increased by approximately 0.05) by considering CT characteristics, secondary insults (hypotension and hypoxia), and laboratory parameters (glucose and hemoglobin). External validation confirmed that the discriminative ability of the model was adequate (AUC 0.80). Outcomes were systematically worse than predicted, but less so in 1,588 patients who were from high-income countries in the CRASH trial. Conclusions Prognostic models using baseline characteristics provide adequate discrimination between patients with good and poor 6 mo outcomes after TBI, especially if CT and laboratory findings are considered in addition to traditional predictors. The model predictions may support clinical practice and research, including the design and analysis of randomized controlled trials.

Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury.

Abstract: Introduction: Acute kidney injury (AKI) can evolve quickly and clinical measures of function often fail to detect AKI at a time when interventions are likely to provide benefit. Identifying early markers of kidney damage has been difficult due to the complex nature of human AKI, in which multiple etiologies exist. The objective of this study was to identify and validate novel biomarkers of AKI. Methods: We performed two multicenter observational studies in critically ill patients at risk for AKI - discovery and validation. The top two markers from discovery were validated in a second study (Sapphire) and compared to a number of previously described biomarkers. In the discovery phase, we enrolled 522 adults in three distinct cohorts including patients with sepsis, shock, major surgery, and trauma and examined over 300 markers. In the Sapphire validation study, we enrolled 744 adult subjects with critical illness and without evidence of AKI at enrollment; the final analysis cohort was a heterogeneous sample of 728 critically ill patients. The primary endpoint was moderate to severe AKI (KDIGO stage 2 to 3) within 12 hours of sample collection. Results: Moderate to severe AKI occurred in 14% of Sapphire subjects. The two top biomarkers from discovery were validated. Urine insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinases-2 (TIMP-2), both inducers of G1 cell cycle arrest, a key mechanism implicated in AKI, together demonstrated an AUC of 0.80 (0.76 and 0.79 alone). Urine [TIMP-2]·[IGFBP7] was significantly superior to all previously described markers of AKI (P 0.72. Furthermore, [TIMP2]·[IGFBP7] significantly improved risk stratification when added to a nine-variable clinical model when analyzed using Cox proportional hazards model, generalized estimating equation, integrated discrimination improvement or net reclassification improvement. Finally, in sensitivity analyses [TIMP-2]·[IGFBP7] remained significant and superior to all other markers regardless of changes in reference creatinine method.

Stressful life events, genetic liability, and onset of an episode of major depression in women.

Abstract: Objective This study was undertaken to clarify how genetic liability and stressful life events interact in the etiology of major depression. Method Information about stressful life events and onset of major depressive episodes in the past year was collected in a population-based sample of female-female twin pairs including 2,164 individuals, 53,215 person-months of observation, and 492 onsets of depression. Results Nine "personal" and three aggregate "network" stressful events significantly predicted onset of major depression in the month of occurrence, four of which predicted onset with an odds ratio of > 10 and were termed "severe": death of a close relative, assault, serious marital problems, and divorce/breakup. Genetic liability also had a significant impact on risk of onset of depression. For severe stressful events, as well as for 10 of the 12 individual stressful events, the best-fitting model for the joint effect of stressful events and genetic liability on onset of major depression suggested genetic control of sensitivity to the depression-inducing effects of stressful life events. In individuals at lowest genetic risk (monozygotic twin, co-twin unaffected), the probability of onset of major depression per month was predicted to be 0.5% and 6.2%, respectively, for those unexposed and exposed to a severe event. In those at highest genetic risk (monozygotic twin, co-twin affected), these probabilities were 1.1% and 14.6%, respectively. Linear regression analysis indicated significant Genotype by Environment interaction in the prediction of onset of major depression. Conclusions Genetic factors influence the risk of onset of major depression in part by altering the sensitivity of individuals to the depression-inducing effect of stressful life events.