Assessment of candidemia-attributable mortality in critically ill patients using propensity score matching analysis

[EN] Introduction: Candidemia in critically ill patients is usually a severe and life-threatening condition with a high crude mortality. Very few studies have focused on the impact of candidemia on ICU patient outcome and attributable mortality still remains controversial. This study was carried out to determine the attributable mortality of ICU-acquired candidemia in critically ill patients using propensity score matching analysis. Methods: A prospective observational study was conducted of all consecutive non-neutropenic adult patients admitted for at least seven days to 36 ICUs in Spain, France, and Argentina between April 2006 and June 2007. The probability of developing candidemia was estimated using a multivariate logistic regression model. Each patient with ICU-acquired candidemia was matched with two control patients with the nearest available Mahalanobis metric matching within the calipers defined by the propensity score. Standardized differences tests (SDT) for each variable before and after matching were calculated. Attributable mortality was determined by a modified Poisson regression model adjusted by those variables that still presented certain misalignments defined as a SDT > 10%. Results: Thirty-eight candidemias were diagnosed in 1,107 patients (34.3 episodes/1,000 ICU patients). Patients with and without candidemia had an ICU crude mortality of 52.6% versus 20.6% (P < 0.001) and a crude hospital mortality of 55.3% versus 29.6% (P = 0.01), respectively. In the propensity matched analysis, the corresponding figures were 51.4% versus 37.1% (P = 0.222) and 54.3% versus 50% (P = 0.680). After controlling residual confusion by the Poisson regression model, the relative risk (RR) of ICU- and hospital-attributable mortality from candidemia was RR 1.298 (95% confidence interval (CI) 0.88 to 1.98) and RR 1.096 (95% CI 0.68 to 1.69), respectively. Conclusions: ICU-acquired candidemia in critically ill patients is not associated with an increase in either ICU or hospital mortality.

An efficient parallel MoM to analyze microstrip structures

[EN]The increasing use of microstrip technology require more accurate analysis methods like full wave method of moments. However, this involves a great computational effort. To reduce the computation time, an alternative parallel method to analyze irregular microstrip structures is presented in this paper. This method calculates the unknown surface current on the planar structure trough a irregular rectangular division using basis and weighted functions. The parallel algorithm performs the calculus of a [Z] matrix and then solves the system using current densities as the unknowns. This parallel program was implemented in the IBM-SP2 using MPI library.