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Few prognostic models have been created in children that receive liver retransplantation (rLT). We examined the SRTR database of 731 children that underwent second liver transplant between 2002 and 2018. Proportional hazards models using backward variable selection were used to identify recipient, donor, and surgical characteristics associated with survival. A simple prognostic scoring system or nomogram (ie, each risk factor was weighted on a five-point scale) was constructed based on the fitted model. Recipient age (P < .001), MELD/PELD (P < .001), recipient ventilated (P = .003), donor cause of death (P = .024), graft type (P = .045), first graft loss due to biliary tract complications (P = .048), and survival time of the first graft (P = .006) were significant predictors of retransplant survival. The bias-corrected Harrell's C-index for the multivariable model was 0.63. Survival was significantly different (P < .001) for those at low risk (0-4 points), medium risk (5-7 points), and high risk (8+ points). Survival was equivalent between low risk pediatric second transplant recipients and pediatric primary liver transplant recipients (P = .67) but significantly worse for medium- (P < .001) and high-risk (P < .001) recipients. With simple clinical characteristics, this scoring tool can modestly discriminate between those children at high risk and those children at low risk of poor outcomes after second liver transplant. [ABSTRACT FROM AUTHOR]

The difference in restricted mean survival times between two groups is a clinically relevant summary measure. With observational data, there may be imbalances in confounding variables between the two groups. One approach to account for such imbalances is estimating a covariate-adjusted restricted mean difference by modeling the covariate-adjusted survival distribution and then marginalizing over the covariate distribution. Because the estimator for the restricted mean difference is defined by the estimator for the covariate-adjusted survival distribution, it is natural to expect that a better estimator of the covariate-adjusted survival distribution is associated with a better estimator of the restricted mean difference. We therefore propose estimating restricted mean differences with stacked survival models. Stacked survival models estimate a weighted average of several survival models by minimizing predicted error. By including a range of parametric, semi-parametric, and non-parametric models, stacked survival models can robustly estimate a covariate-adjusted survival distribution and, therefore, the restricted mean treatment effect in a wide range of scenarios. We demonstrate through a simulation study that better performance of the covariate-adjusted survival distribution often leads to better mean squared error of the restricted mean difference although there are notable exceptions. In addition, we demonstrate that the proposed estimator can perform nearly as well as Cox regression when the proportional hazards assumption is satisfied and significantly better when proportional hazards is violated. Finally, the proposed estimator is illustrated with data from the United Network for Organ Sharing to evaluate post-lung transplant survival between large-volume and small-volume centers. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]