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Introduction: Hemodialysis (HD) patients have significant burden of cerebral ischemic pathology noted on brain imaging. These ischemic type lesions maybe due to cerebral hypoperfusion that may be occurring during blood pressure (BP) fluctuations commonly noted during HD sessions. We evaluated changes in cerebral perfusion and measured an index of cerebral autoregulation (CA index) during HD to identify potential risk factors for intradialytic decline in cerebral perfusion and impaired cerebral autoregulation. Methods: In this cross‐sectional study, we included HD patients age 50 years or older receiving conventional in‐center HD. We measured cerebral perfusion during HD, using cerebral oximetry, and calculated the correlation between cerebral perfusion and BP during HD as an index of CA. We measured the association between potential risk factors for intradialytic decline in cerebral perfusion and CA index. Findings: We included 32 participants and 118 HD sessions in our analysis. The mean ± SD decline in cerebral oxygen saturation during HD was 6.5% ± 2.9% with a relative decline from baseline values of 9.2% ± 4.4%. Greater drop in systolic BP (SBP) during HD was associated with decline in cerebral oxygen saturation, p = 0.02. Impaired CA index was noted in 37.3% of HD sessions. Having diabetes and >20 mmHg drop in SBP during HD were associated with increased (worse) CA index with an increase of 0.24 95%CI [0.06, 0.41] for diabetes and increase of 0.43 95%CI [0.27, 0.56] for a >20 mmHg drop in SBP during HD. Discussion: Cerebral perfusion can decline during HD and is associated with changes in systemic BP. This may be due to impaired cerebral autoregulation in HD patients. Risk factors for worse CA index include diabetes and >20 mmHg drop in SBP during HD. This study highlights the risk of intradialytic decline in cerebral perfusion and impaired cerebral autoregulation in HD patients. [ABSTRACT FROM AUTHOR]

Many time-to-event studies are complicated by the presence of competing risks. Such data are often analyzed using Cox models for the cause-specific hazard function or Fine and Gray models for the subdistribution hazard. In practice, regression relationships in competing risks data are often complex and may include nonlinear functions of covariates, interactions, high-dimensional parameter spaces and nonproportional cause-specific, or subdistribution, hazards. Model misspecification can lead to poor predictive performance. To address these issues, we propose a novel approach: flexible prediction modeling of competing risks data using Bayesian Additive Regression Trees (BART). We study the simulation performance in two-sample scenarios as well as a complex regression setting, and benchmark its performance against standard regression techniques as well as random survival forests. We illustrate the use of the proposed method on a recently published study of patients undergoing hematopoietic stem cell transplantation. [ABSTRACT FROM AUTHOR]

Background: Men tend to have more upper body mass and fat than women, a physical characteristic that may predispose them to severe motor vehicle crash (MVC) injuries, particularly in certain body regions. This study examined MVC- related regional body injury and its association with the presence of driver obesity using both real-world data and computer crash simulation. Methods and Findings: Real-world data were from the 2001 to 2005 National Automotive Sampling System Crashworthiness Data System. A total of 10,941 drivers who were aged 18 years or older involved in frontal collision crashes were eligible for the study. Sex-specific logistic regression models were developed to analyze the associations between MVC injury and the presence of driver obesity. in order to confirm the findings from real-world data, computer models of obese subjects were constructed and crash simulations were performed. According to real-world data, obese men had a substantially higher risk of injury, especially serious injury, to the upper body regions including head, face, thorax, and spine than normal weight men (all p Conclusions: Obese men endured a much higher risk of injury to upper body regions during MVCs. This higher risk may be attributed to differences in body shape, fat distribution, and center of gravity between obese and normal-weight subjects, and between men and women. Please see later in the article for the Editors' Summary., Introduction Motor vehicle accidents are the leading cause of injury-related death in the United States. In 2005, approximately 45,000 persons died and more than 3 million persons were injured in [...]

Dirichlet process mixture (DPM) models provide flexible modeling for distributions of data as an infinite mixture of distributions from a chosen collection. Specifying priors for these models in individual data contexts can be challenging. In this paper, we introduce a scheme which requires the investigator to specify only simple scaling information. This is used to transform the data to a fixed scale on which a low information prior is constructed. Samples from the posterior with the rescaled data are transformed back for inference on the original scale. The low information prior is selected to provide a wide variety of components for the DPM to generate flexible distributions for the data on the fixed scale. The method can be applied to all DPM models with kernel functions closed under a suitable scaling transformation. Construction of the low information prior, however, is kernel dependent. Using DPM-of-Gaussians and DPM-of-Weibulls models as examples, we show that the method provides accurate estimates of a diverse collection of distributions that includes skewed, multimodal, and highly dispersed members. With the recommended priors, repeated data simulations show performance comparable to that of standard empirical estimates. Finally, we show weak convergence of posteriors with the proposed priors for both kernels considered. [ABSTRACT FROM AUTHOR]

Individualized treatment rules can improve health outcomes by recognizing that patients may respond differently to treatment and assigning therapy with the most desirable predicted outcome for each individual. Flexible and efficient prediction models are desired as a basis for such individualized treatment rules to handle potentially complex interactions between patient factors and treatment. Modern Bayesian semiparametric and nonparametric regression models provide an attractive avenue in this regard as these allow natural posterior uncertainty quantification of patient specific treatment decisions as well as the population wide value of the prediction-based individualized treatment rule. In addition, via the use of such models, inference is also available for the value of the optimal individualized treatment rules. We propose such an approach and implement it using Bayesian Additive Regression Trees as this model has been shown to perform well in fitting nonparametric regression functions to continuous and binary responses, even with many covariates. It is also computationally efficient for use in practice. With Bayesian Additive Regression Trees, we investigate a treatment strategy which utilizes individualized predictions of patient outcomes from Bayesian Additive Regression Trees models. Posterior distributions of patient outcomes under each treatment are used to assign the treatment that maximizes the expected posterior utility. We also describe how to approximate such a treatment policy with a clinically interpretable individualized treatment rule, and quantify its expected outcome. The proposed method performs very well in extensive simulation studies in comparison with several existing methods. We illustrate the usage of the proposed method to identify an individualized choice of conditioning regimen for patients undergoing hematopoietic cell transplantation and quantify the value of this method of choice in relation to the optimal individualized treatment rule as well as non-individualized treatment strategies. [ABSTRACT FROM AUTHOR]