Kidney Donor Profile Index and allograft outcomes: interactive effects of estimated post-transplant survival score and ischaemic time

Background. The Kidney Donor Profile Index (KDPI) is routinely reported by the donation agencies in Australia.We determined the association between KDPI and short-term allograft loss and assessed if this association was modified by the estimated post-transplant survival (EPTS) score and total ischaemic time. Methods. Using data from the Australia and New Zealand Dialysis and Transplant Registry, the association between KDPI (in quartiles) and 3-year overall allograft loss was examined using adjusted Cox regression analysis. The interactive effects between KDPI, EPTS score and total ischaemic time on allograft loss were assessed. Results. Of 4006 deceased donor kidney transplant recipients transplanted between 2010 and 2015, 451 (11%) recipients experienced allograft loss within 3 years post-transplant. Compared with recipients of kidneys with a KDPI of 0–25%, recipients who received donor kidneys with a KDPI >75% experienced a 2-fold increased risk of 3-year allograft loss {adjusted hazard ratio [HR] 2.04 [95% confidence interval (CI) 1.53–2.71]}. The adjusted HRs for kidneys with a KDPI of 26–50% and 51–75% were 1.27 (95% CI 0.94–1.71) and 1.31 (95% CI 0.96–1.77), respectively. There were significant interactions between KDPI and EPTS scores (P-value for interaction <.01) and total ischaemic time (P-value for interaction <.01) such that the associations between higher KDPI quartiles and 3-year allograft loss were strongest in recipients with the lowest EPTS scores and longest total ischaemic time. Conclusion. Recipients with higher post-transplant expected survival and transplants with longer total ischaemia who received donor allografts with higher KDPI scores experienced a greater risk of short-term allograft loss compared with those recipients with reduced post-transplant expected survival and with shorter total ischemia.Janelle Prunster, Germaine Wong, Nicholas Larkins, Kate Wyburn, Ross Francis, William R. Mulley, Esther Ooi, Helen Pilmore, Christopher E. Davies and Wai H. Li

Theoretical versus Ex Vivo Assessment of Radiation Damage Repair: An Investigation in Normal Breast Tissue

In vivo validation of models of DNA damage repair will enable their use for optimizing clinical radiotherapy. In this study, a theoretical assessment was made of DNA double-strand break (DSB) induction in normal breast tissue after intraoperative radiation therapy (IORT), which is now an accepted form of adjuvant radiotherapy for selected patients with early breast cancer. DSB rates and relative biological effectiveness (RBE) were calculated as a function of dose, radiation quality and dose rate, each varying based on the applicator size used during IORT. The spectra of primary electrons in breast tissue adjacent to each applicator were calculated using measured X-ray spectra and Monte Carlo methods, and were used to inform a Monte Carlo damage simulation code. In the absence of repair, asymptotic RBE values (relative to 60Co) were approximately 1.5. Beam-quality changes led to only minor variations in RBE among applicators, though differences in dose rate and overall dose delivery time led to larger variations and a rapid decrease in RBE. An experimental assessment of DSB induction was performed ex vivo using pre- and postirradiation tissue samples from patients receiving breast intraoperative radiation therapy. Relative DSB rates were assessed via ?-H2AX immunohistochemistry using proportional staining. Maximum-likelihood parameter estimation yielded a DSB repair halftime of 25.9 min (95% CI, 21.5-30.4 min), although the resulting model was not statistically distinguishable from one where there was no change in DSB yield among patients. Although the model yielded an in vivo repair halftime of the order of previous estimates for in vitro repair halftimes, we cannot conclude that it is valid in this context. This study highlights some of the uncertainties inherent in population analysis of ex vivo samples, and of the quantitative limitations of immunohistochemistry for assessment of DSB repair

Controlling the reinforcement in Bayesian non-parametric mixture models

The paper deals with the problem of determining the number of components in a mixture model. We take a Bayesian non-parametric approach and adopt a hierarchical model with a suitable non-parametric prior for the latent structure. A commonly used model for such a problem is the mixture of Dirichlet process model. Here, we replace the Dirichlet process with a more general non-parametric prior obtained from a generalized gamma process. The basic feature of this model is that it yields a partition structure for the latent variables which is of Gibbs type. This relates to the well-known (exchangeable) product partition models. If compared with the usual mixture of Dirichlet process model the advantage of the generalization that we are examining relies on the availability of an additional parameter "σ" belonging to the interval (0,1): it is shown that such a parameter greatly influences the clustering behaviour of the model. A value of "σ" that is close to 1 generates a large number of clusters, most of which are of small size. Then, a reinforcement mechanism which is driven by "σ" acts on the mass allocation by penalizing clusters of small size and favouring those few groups containing a large number of elements. These features turn out to be very useful in the context of mixture modelling. Since it is difficult to specify "a priori" the reinforcement rate, it is reasonable to specify a prior for "σ". Hence, the strength of the reinforcement mechanism is controlled by the data. Copyright 2007 Royal Statistical Society.