A Prognostic Tool for Individualized Prediction of Graft Failure Risk within Ten Years after Kidney Transplantation

Stamenic, Danko; Rousseau, Annick; Essig, Marie; Gatault, Philippe; Büchler, Mathias; Filloux, Matthieu; Marquet, Pierre; Prémaud, Aurélie

doi:10.1155/2019/7245142

Cited by 7 publications

(16 citation statements)

References 34 publications

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“…The real parameters were chosen to mimic the real data, described in Stamenic et al paper [ 15 ], dealing with a prognostic tool for individualized prediction of graft failure risk within ten years after kidney transplantation, using serum creatinine progression as a longitudinal marker. Following Eqs.…”

Section: Resultsmentioning

confidence: 99%

“…The time points for repeated measures of the longitudinal marker are fixed to 1, 3, 6, 12, 18 and 24 months, following Stamenic et al [ 15 ]. The parameters vector for a 2-classes model, with class common random effect and error variance of mixed sub-model is as follows: …”

Section: Resultsmentioning

confidence: 99%

“…The real values for the parameters were chosen as follows: High separation framework. This setting is directly derived from Stamenic et al [ 15 ], resulted in β 01 =170, β 02 =100, β 11 =88 by year, β 12 =1.2 by year, and . Low separation framework.…”

Section: Resultsmentioning

confidence: 99%

“…These papers appeared following a comprehensive methodology review concerning the JLCM [ 7 ] and have different objectives. These objectives can be summarized as follows: 1) to study the relationship between a longitudinal biomarker and the risk of event [ 8 – 11 ]; 2) to identify sub-groups of longitudinal biomarker progression censored by the event occurrence [ 12 ]; 3) to study the impact of different factors on the longitudinal biomarker progression censored by the event occurrence [ 13 ]; 4) to predict the risk of an event based on the longitudinal biomarker progression [ 14 , 15 ]. Different implementations of the model were proposed to achieve a same objective.…”

Section: Introductionmentioning

confidence: 99%

“…For example, for the first objective, Syrjälä et al [ 8 ] search for the relation between childhood food consumption and the risk of advanced islet autoimmunity using a JLCM without covariates; Brilleman et al [ 9 ] explore the relationship between the changes in body mass index and the risk of death and/or transplant in hemodialysis patients by means of the JLCM for competing risks, including the pre-specified covariates with a common effect on latent classes only in the survival sub-model; Ogata et al [ 10 ] and Portegies et al [ 11 ] analyze the association between fasting plasma glucose progression and the risk of cardiovascular disease and the association between the blood pressure trajectories and the risk of stroke respectively by including the pre-specified covariates with a latent class-specific effect into the linear mixed sub-model and into the survival sub-model. As other examples, for the fourth objective, [ 14 ] search to prevent Alzeihmer disease using MMSE ( Mini-Mental State Examination ) score progression and creating a predictive risk model with class-specific covariates in both linear mixed sub-model and in the survival sub-model; Stamenic et al [ 15 ] defined latent classes to assess the impact of serum creatinine on graft failure risk with no covariates in JLCM, and performed a multivariable multinomial logistic analysis after defining these latent classes in order to analyze the factors associated to the classes.…”

Section: Introductionmentioning

confidence: 99%

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Joint latent class model: Simulation study of model properties and application to amyotrophic lateral sclerosis disease

Kyheng¹,

Babykina²,

Ternynck³

et al. 2021

BMC Med Res Methodol

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Background In many clinical applications, evolution of a longitudinal marker is censored by an event occurrence, and, symmetrically, event occurrence can be influenced by the longitudinal marker evolution. In such frameworks joint modeling is of high interest. The Joint Latent Class Model (JLCM) allows to stratify the population into groups (classes) of patients that are homogeneous both with respect to the evolution of a longitudinal marker and to the occurrence of an event; this model is widely employed in real-life applications. However, the finite sample-size properties of this model remain poorly explored. Methods In the present paper, a simulation study is carried out to assess the impact of the number of individuals, of the censoring rate and of the degree of class separation on the finite sample size properties of the JLCM. A real-life application from the neurology domain is also presented. This study assesses the precision of class membership prediction and the impact of covariates omission on the model parameter estimates. Results Simulation study reveals some departures from normality of the model for survival sub-model parameters. The censoring rate and the number of individuals impact the relative bias of parameters, especially when the classes are weakly distinguished. In real-data application the observed heterogeneity on individual profiles in terms of a longitudinal marker evolution and of the event occurrence remains after adjusting to clinically relevant and available covariates; Conclusion The JLCM properties have been evaluated. We have illustrated the discovery in practice and highlights the usefulness of the joint models with latent classes in this kind of data even with pre-specified factors. We made some recommendations for the use of this model and for future research.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Joint latent class model: Simulation study of model properties and application to amyotrophic lateral sclerosis disease

Kyheng¹,

Babykina²,

Ternynck³

et al. 2021

BMC Med Res Methodol

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Better Rejection‐Free Survival at Three Years in Kidney Transplant Recipients With Model‐Informed Precision Dosing of Mycophenolate Mofetil

Villeneuve,

Humeau,

Monchaud

et al. 2024

Clin Pharma and Therapeutics

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The clinical impact of individual dose adjustment of mycophenolate mofetil is still debated, due to conflicting results from randomized clinical trials. This retrospective study aimed to compare 3‐year rejection‐free survival and adverse effects between adult kidney transplant recipients (KTRs) with or without mycophenolate mofetil model‐informed precision dosing (MIPD). MIPD is defined here as mycophenolic acid area under the curve (AUC0–12h) estimation using a limited sampling strategy, pharmacokinetic models and Bayesian estimators; dose recommendation to reach AUC0–12h = 45 mg.h/L; using a widely used online expert system. The study, nested in two multicenter prospective cohort studies, focused on patients who received a mycophenolate drug and were followed up for 1–3 years. Mycophenolate mofetil MIPD was prescribed as per local practice, on a regular basis, when deemed necessary, or not at all. The MIPD group included 341 KTRs and the control group 392. At 3 years, rejection‐free survival was respectively 91.2% and 80.6% (P < 0.001) and the cumulative incidence of rejection 5.08% vs. 12.7% per patient × year (hazard ratio = 0.49 (0.34, 0.71), P < 0.001), corresponding to a 2.5‐fold reduction. Significant association with rejection‐free survival was confirmed in patients at low or high risk of rejection (P = 0.017 and 0.013) and in patients on tacrolimus, but not on cyclosporine (P < 0.001 and 0.205). The mycophenolate mofetil MIPD group had significantly more adverse effects, but most occurred before the first AUC0–12h, suggesting some may be the reason why MIPD was ordered.

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Potential utility of urinary chemokine CCL2 to creatinine ratio in prognosis of 5‐year graft failure and mortality post 1‐year protocol biopsy in kidney transplant recipients

Gniewkiewicz

Gozdowska

Dęborska−Materkowska

et al. 2023

Immunity Inflam & Disease

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Background: Chemokines (chemotactic cytokines) are small proteins which are engaged in many pathophysiological processes, including inflammation and homeostasis. In recent years, application of chemokines in transplant medicine was intensively studied. The aim of this study was to determine the utility of urinary chemokines CCL2 (C-C motif ligand 2) and CXCL10 (C-X-C motif chemokine ligand 10) in prognosis of 5-year graft failure and mortality post 1-year protocol biopsy in renal transplant recipients.Methods: Forty patients who had a protocol biopsy 1 year after renal transplantation were included. Concentrations of CCL2 and CXCL10 in urine with reference to urine creatinine were measured. All patients were under the supervision of one transplant center. Long-term outcomes within 5 years after 1-year posttransplant biopsy were analyzed.Results: Urinary CCL2:Cr at the time of biopsy was significantly increased in patients who died or had graft failure. CCL2:Cr was proven to be a significant predictor of 5-year graft failure and mortality (odds ratio [OR]: 1.09, 95% confidence interval [CI]: 1.02-1.19, p = .02; OR: 1.08, 95% CI: 1.02-1.16, p = .04; respectively). Conclusion:Chemokines are easily detected by current methods. In the era of personalized medicine, urinary CCL2:Cr can be considered as a factor providing complementary information regarding risk of graft failure or increased mortality.

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A Prognostic Tool for Individualized Prediction of Graft Failure Risk within Ten Years after Kidney Transplantation

Cited by 7 publications

References 34 publications

Joint latent class model: Simulation study of model properties and application to amyotrophic lateral sclerosis disease

Joint latent class model: Simulation study of model properties and application to amyotrophic lateral sclerosis disease

Better Rejection‐Free Survival at Three Years in Kidney Transplant Recipients With Model‐Informed Precision Dosing of Mycophenolate Mofetil

Potential utility of urinary chemokine CCL2 to creatinine ratio in prognosis of 5‐year graft failure and mortality post 1‐year protocol biopsy in kidney transplant recipients

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