Although cold ischemia time has been widely studied in renal transplantation area, there is no consensus on its precise relationship with the transplantation outcomes. To study this, we sampled data from 3839 adult recipients of a first heart-beating deceased donor kidney transplanted between 2000 and 2011 within the French observational multicentric prospective DIVAT cohort. A Cox model was used to assess the relationship between cold ischemia time and death-censored graft survival or patient survival by using piecewise log-linear function. There was a significant proportional increase in the risk of graft failure for each additional hour of cold ischemia time (hazard ratio, 1.013). As an example, a patient who received a kidney with a cold ischemia time of 30 h presented a risk of graft failure near 40% higher than a patient with a cold ischemia time of 6 h. Moreover, we found that the risk of death also proportionally increased for each additional hour of cold ischemia time (hazard ratio, 1.018). Thus, every additional hour of cold ischemia time must be taken into account in order to increase graft and patient survival. These findings are of practical clinical interest, as cold ischemia time is among one of the main modifiable pre-transplantation risk factors that can be minimized by improved management of the peri-transplantation period.

SUMMARYIn 2002, the United Network for Organ Sharing proposed increasing the pool of donor kidneys to include Expanded Criteria Donor (ECD). Outside the USA, the ECD definition remains the one used without questioning whether such a graft allocation criterion is valid worldwide. We performed a meta-analysis to quantify the differences between ECD and Standard Criteria Donor (SCD) transplants. We paid particular attention to select studies in which the methodology was appropriate and we took into consideration the geographical area. Thirty-two publications were included. Only five studies, all from the USA, reported confounderadjusted hazard ratios comparing the survival outcomes between ECD and SCD kidney transplant recipients. These five studies confirmed that ECD recipients seemed to have poorer prognosis. From 29 studies reporting appropriate survival curves, we estimated the 5-year pooled nonadjusted survivals for ECD and SCD recipients. The relative differences between the two groups were lower in Europe than in North America, particularly for death-censored graft failure. It is of primary importance to propose appropriate studies for external validation of the ECD criteria in non-US kidney transplant recipients.

Missing data and especially dropouts frequently arise in longitudinal data. Maximum likelihood estimates are consistent when data are missing at random (MAR) but, as this assumption is not checkable, pattern mixture models (PMM) have been developed to deal with informative dropout. More recently, latent class models (LCM) have been proposed as a way to relax PMM assumptions. The aim of this paper is to compare PMM and LCM in order to tackle informative dropout in a longitudinal study of cognitive ageing measured by several psychometric tests. Using a multivariate longitudinal model with a latent process, a sensitivity analysis was performed to compare estimates under the MAR assumption, from a PMM and from two LCM. In the PMM, dropout patterns are included as covariates in the multivariate longitudinal model. In the simple LCM, they are predictors of the class membership probabilities while, in the joint LCM, the dropout time is jointly modeled using a proportional hazard model depending on latent classes. We show that parameter interpretation is different in the two kinds of models and thus can lead to different estimated values. PMM parameters are adjusted on the dropout patterns while LCM parameters are adjusted on the latent classes. This difference is highlighted in our data set because the latent classes exhibit much more heterogeneity than dropout patterns. We suggest several complementary analyses to investigate the characteristics of latent classes in order to understand the meaning of the parameters when using LCM to deal with informative dropout.