ABSTRACT
Pneumocystis jiroveci pneumonia (PJP) is associated with high morbidity and mortality after hematopoietic stem cell transplantation (HSCT). Little is known about PJP infections after HSCT because of the rarity of disease given routine prophylaxis. We report the results of a Center for International Blood and Marrow Transplant Research study evaluating the incidence, timing, prophylaxis agents, risk factors and mortality of PJP after autologous (auto) and allogeneic (allo) HSCT. Between 1995 and 2005, 0.63% allo recipients and 0.28% auto recipients of first HSCT developed PJP. Cases occurred as early as 30 days to beyond a year after allo HSCT. A nested case cohort analysis with supplemental data (n=68 allo cases, n=111 allo controls) revealed that risk factors for PJP infection included lymphopenia and mismatch after HSCT. After allo or auto HSCT, overall survival was significantly poorer among cases vs controls (P=0.0004). After controlling for significant variables, the proportional hazards model revealed that PJP cases were 6.87 times more likely to die vs matched controls (P<0.0001). We conclude PJP infection is rare after HSCT but is associated with high mortality. Factors associated with GVHD and with poor immune reconstitution are among the risk factors for PJP and suggest that protracted prophylaxis for PJP in high-risk HSCT recipients may improve outcomes.
Subject(s)
Hematopoietic Stem Cell Transplantation , Pneumocystis carinii , Pneumonia, Pneumocystis , Allografts , Autografts , Female , Humans , Incidence , Male , Pneumonia, Pneumocystis/etiology , Pneumonia, Pneumocystis/mortality , Pneumonia, Pneumocystis/prevention & control , Risk FactorsABSTRACT
Understanding the cause of differences among general circulation model projections of carbon dioxide-induced climatic change is a necessary step toward improving the models. An intercomparison of 14 atmospheric general circulation models, for which sea surface temperature perturbations were used as a surrogate climate change, showed that there was a roughly threefold variation in global climate sensitivity. Most of this variation is attributable to differences in the models' depictions of cloud-climate feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as climatic predictors.