BackgroundThe impact of intraoperative transfusion on postoperative mortality in lung transplant recipients is still elusive.MethodsUnivariate and multivariate analysis were performed to investigate the influence of red blood cells (RBCs) and fresh frozen plasma (FFP) on mortality in 134 consecutive lung transplants recipients from September 2003 until December 2008.ResultsIntraoperative transfusion of RBCs and FFP was associated with a significant increase in mortality with odds ratios (ORs) of 1.10 (1.03 to 1.16, P = 0.02) and 1.09 (1.02 to 1.15, P = 0.03), respectively. For more than four intraoperatively transfused RBCs multivariate analysis showed a hazard ratio for mortality of 3.8 (1.40 to 10.31, P = 0.003). Furthermore, non-survivors showed a significant increase in renal replacement therapy (RRT) (36.6% versus 6.9%, P <0.0001), primary graft dysfunction (PGD) (39.3% versus 5.9%, P <0.0001), postoperative need of extracorporeal membrane oxygenation (ECMO) (26.9% versus 3.1%, P = 0.0019), sepsis (24.2% versus 4.0%, P = 0.0004), multiple organ dysfunction syndrome (MODS) (26.9% versus 3.1%, P <0.0001), infections (18.1% versus 0.9%, P = 0.0004), retransplantation (12.1% versus 6.9%, P = 0.039) and readmission to the ICU (33.3% versus 12.8%, P = 0.024).ConclusionsIntraoperative transfusion is associated with a strong negative influence on outcome in lung transplant recipients.

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Deformable image registration (DIR) is a key element in adaptive radiotherapy (AR) to include anatomical modifications in the adaptive planning. In AR, daily 3D images are acquired and DIR can be used for structure propagation and to deform the daily dose to a reference anatomy. Quantifying the uncertainty associated with DIR is essential. Here, a probabilistic unsupervised deep learning method is presented to predict the variance of a given deformable vector field (DVF). It is shown that the proposed method can predict the uncertainty associated with various conventional DIR algorithms for breathing deformation in the lung. In addition, we show that the uncertainty prediction is accurate also for DIR algorithms not used during the training. Finally, we demonstrate how the resulting DVFs can be used to estimate the dosimetric uncertainty arising from dose deformation.