A time‐to‐event model for acute rejections in paediatric renal transplant recipients treated with ciclosporin A

Abstract

AIMSCiclosporin A (CsA) dosing in immunosuppression after paediatric kidney transplantation remains challenging, and appropriate target CsA exposures (AUCs) are controversial. This study aimed to develop a time-to-first-acute rejection (AR) model and to explore predictive factors for therapy outcome. METHODSPatient records at the Children's Hospital in Helsinki, Finland, were analysed. A parametric survival model in NONMEM was used to describe the time to first AR. The influences of AUC and other covariates were explored using stepwise covariate modelling, bootstrap-stepwise covariate modelling and cross-validated stepwise covariate modelling. The clinical relevance of the effects was assessed with the time at which 90% of the patients were AR free (t90). RESULTSData from 87 patients (0.7-19.8 years old, 54 experiencing an AR) were analysed. The baseline hazard was described with a function changing in steps over time. No statistically significant covariate effects were identified, a finding substantiated by all methods used. Thus, within the observed AUC range (90% interval 1.13-8.40 h mg l -1 ), a rise in AUC was not found to increase protection from AR. Dialysis time, sex and baseline weight were potential covariates, but the predicted clinical relevance of their effects was low. For the strongest covariate, dialysis time, median t90 was 5.8 days (90% confidence interval 5.1-6.8) for long dialysis times (90th percentile) and 7.4 days (6.4-11.7) for short dialysis times (10th percentile). CONCLUSIONSA survival model with discrete time-varying hazards described the data. Within the observed range, AUC was not identified as a covariate. This feedback on clinical practice may help to avoid unnecessarily high CsA dosing in children.

Exposure to Mycophenolic Acid Better Predicts Immunosuppressive Efficacy Than Exposure to Calcineurin Inhibitors in Renal Transplant Patients

“…A few studies used survival models with time–dependent covariates to investigate the relationships between AR and longitudinal exposure to a single immunosuppressive drug (i.e., joint model for MPA, 33 Belatacept, 34 CsA, 7 and Cox model for tacrolimus 35 ). Within the observed concentration ranges, CsA 7 as well as Belatacept 34 longitudinal exposures were not statistically significant predictors of the hazard of AR. For tacrolimus, the association of time–varying tacrolimus trough levels with AR occurring in the first 6 months posttransplantation was analyzed using a Cox model 35 .…”

“…Recent studies challenge the benefit of TDM for CNIs based on the recommended targets. The studies minimizing exposure to CNIs (i.e., Symphony 5 and Caesar for CsA 6 ) found a low impact of minimization of CNI exposure on AR risk, whereas modeling studies found contradictory results 7 , 8 . The added value of TDM for MPA is still under debate 9 .…”

Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations

“…We would especially like to thank Mats O. Karlsson, Kajsa Harling, Anne-Gaëlle Dosne, Anders Kristoffersson, Oskar Alskär and Giulia Corn for discussions and extensive testing. In addition, we would like to thank the authors of (11)(12)(13)(14) who gave us permission to use their NONMEM model files and data sets for the numerical experiments.…”

Time-to-Seizure Modeling of Lacosamide Used in Monotherapy in Patients with Newly Diagnosed Epilepsy

“…The probability of not having an event (i.e., no dropout) by time t is described by the survivor function S ( t ), which is determined by the hazard. A flexible empirical function was applied as described in Frobel et al [ 14 ], where the hazard changes in a step-wise fashion at given breakpoints. To avoid discontinuities in the hazard when passing a breakpoint, often leading to numerical problems during estimation, several sigmoidal functions were connected to achieve some smoothness.…”