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Joint modelling of longitudinal and time-to-event data : analysis of predictive factors of graft outcomes in kidney transplant recipients

Abstract : Prediction of graft outcome would be useful to optimize patient care. Follow-up of kidneytransplant patients include repeated measurements of longitudinal markers, such as serum creatinine and immunosuppressive drug exposure. Recently proposed joint models areappropriate to analyze relationship between longitudinal processes and time-to-event data. In the first part of present work, we used the approach of joint latent class mixed models tostudy the impact of time-profiles of serum creatinine collected within the first 18 months after kidney transplantation on long-term graft survival. The studied cohort was parted into three homogenous classes with a specific time-evolution of serum creatinine and a specific risk of graft failure. The individual predicted probabilities of graft failure up to 10 years posttransplantation, calculated from this joint model were satisfying in terms of sensitivity, specificity and overall accuracy, for patients who had not developed de novo donor specificanti-HLA antibodies. The clinical usefulness of developed predictive tooI needs to beevaluated with a dynamic approach. In the second part, non-linear mixed effects models witha mixture of distribution for random effects were used to investigate (i) the associationbetween variability over time of tacrolimus exposure and self-reported drug adherence and(ii) the impact of this variability on the acute rejection risk. This model found a significantimpact of tacrolimus time-exposure variability on acute rejection onset beyond 3 months posttransplantation. On the contrary, no association between adherence and (i) variability oftacrolimus time-exposure and (ii) acute rejection was observed in our study which included moderate non-adherent patients only. This result questions the impact of moderate nonadherence on graft outcome.
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Submitted on : Thursday, November 22, 2018 - 11:43:05 AM
Last modification on : Friday, November 23, 2018 - 1:20:56 AM
Long-term archiving on: : Saturday, February 23, 2019 - 2:12:07 PM


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  • HAL Id : tel-01930746, version 1



Danko Stamenic. Joint modelling of longitudinal and time-to-event data : analysis of predictive factors of graft outcomes in kidney transplant recipients. Human health and pathology. Université de Limoges, 2018. English. ⟨NNT : 2018LIMO0027⟩. ⟨tel-01930746⟩



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