Hemodialysis Water Parameters as Predisposing Factors for Anemia in Patients in Dialytic Treatment: Application of Mixed Regression Models.

Contamination by metals and microbiological agents in hemodialysis water can cause clinical intercurrences in hemodialysis patients. Evaluating and assuring minimum levels of contamination from metals and microorganisms in hemodialysis water can improve patient safety. The objective of this study was to assess hemodialysis water quality in a major tertiary hospital in Brazil and investigate the relationship between hemodialysis water quality and clinical intercurrences in hemodialysis patients. A prospective-observational cohort study for evaluation of water quality parameters and clinical intercurrences present by patients (n = 52) were performed from May 2014 to April 2015. Mixed linear regression models and binary regression models were fitted for water quality parameters and patients' clinical parameters. The increase in the levels of copper and nitrate in hemodialysis water was significant (p < 0.05) to explain anemia. Bone pain was related to age, sex (female), and hemodialysis treatment duration (years). Hypotensive episodes were related to serum sodium decrease. Aluminum in hemodialysis water was present in an average concentration higher than the permitted threshold (15.35 ± 14.53 µg/L). Cadmium, total coliforms, Escherichia coli, and endotoxins in hemodialysis water were not detected and the heterotrophic bacteria count was below the reference limit. These parameters' concentrations varied during the study, evidencing the need for continuous monitoring.

Bayesian Computational Methods for Sampling from the Posterior Distribution of a Bivariate Survival Model, Based on AMH Copula in the Presence of Right-Censored Data.

In this paper, we study the performance of Bayesian computational methods to estimate the parameters of a bivariate survival model based on the Ali-Mikhail-Haq copula with marginal distributions given by Weibull distributions. The estimation procedure was based on Monte Carlo Markov Chain (MCMC) algorithms. We present three version of the Metropolis-Hastings algorithm: Independent Metropolis-Hastings (IMH), Random Walk Metropolis (RWM) and Metropolis-Hastings with a natural-candidate generating density (MH). Since the creation of a good candidate generating density in IMH and RWM may be difficult, we also describe how to update a parameter of interest using the slice sampling (SS) method. A simulation study was carried out to compare the performances of the IMH, RWM and SS. A comparison was made using the sample root mean square error as an indicator of performance. Results obtained from the simulations show that the SS algorithm is an effective alternative to the IMH and RWM methods when simulating values from the posterior distribution, especially for small sample sizes. We also applied these methods to a real data set.