Identifying subgroups of renal function trajectories.

Background: Renal function in patients with chronic kidney disease (CKD) may follow different trajectory profiles. The aim of this study was to evaluate and illustrate the ability of the latent class linear mixed model (LCMM) to identify clinically relevant subgroups of renal function trajectories within a multicenter hospital-based cohort of CKD patients. Methods: We analysed data from the NephroTest cohort including 1967 patients with all-stage CKD at baseline who had glomerular filtration rate (GFR) both measured by 51 Cr-EDTA renal clearance (mGFR) and estimated by the CKD-EPI equation (eGFR); 1103 patients had at least two measurements. The LCMM was used to identify subgroups of GFR trajectories, and patients' characteristics at baseline were compared between the subgroups identified. Results: Five classes of mGFR trajectories were identified. Three had a slow linear decline of mGFR over time at different levels. In the two others, patients had a high level of mGFR at baseline with either a strong nonlinear decline over time ( n = 11) or a nonlinear improvement ( n = 94) of mGFR. Higher levels of proteinuria and blood pressure at baseline were observed in classes with either severely decreased mGFR or strong mGFR decline over time. Using eGFR provided similar findings. Conclusion: The LCMM allowed us to identify in our cohort five clinically relevant subgroups of renal function trajectories. It could be used in other CKD cohorts to better characterize their different profiles of disease progression, as well as to investigate specific risk factors associated with each profile.

Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks.

Thanks to the growing interest in personalized medicine, joint modeling of longitudinal marker and time-to-event data has recently started to be used to derive dynamic individual risk predictions. Individual predictions are called dynamic because they are updated when information on the subject's health profile grows with time. We focus in this work on statistical methods for quantifying and comparing dynamic predictive accuracy of this kind of prognostic models, accounting for right censoring and possibly competing events. Dynamic area under the ROC curve (AUC) and Brier Score (BS) are used to quantify predictive accuracy. Nonparametric inverse probability of censoring weighting is used to estimate dynamic curves of AUC and BS as functions of the time at which predictions are made. Asymptotic results are established and both pointwise confidence intervals and simultaneous confidence bands are derived. Tests are also proposed to compare the dynamic prediction accuracy curves of two prognostic models. The finite sample behavior of the inference procedures is assessed via simulations. We apply the proposed methodology to compare various prediction models using repeated measures of two psychometric tests to predict dementia in the elderly, accounting for the competing risk of death. Models are estimated on the French Paquid cohort and predictive accuracies are evaluated and compared on the French Three-City cohort.

Prognostic score for predicting risk of dementia over 10 years while accounting for competing risk of death.

Early detection of subjects at high risk of developing dementia is essential. By dealing with censoring and competing risk of death, we developed a score for predicting 10-year dementia risk by combining cognitive tests, and we assessed whether inclusion of cognitive change over the previous year increased its discrimination. Data came from the French prospective cohort study Personnes Agées QUID (PAQUID) and included 3,777 subjects aged 65 years or older (1988-1998). The combined prediction score was estimated by means of an illness-death model handling interval censoring and competing risk of death. Its predictive ability was measured using the receiver operating characteristic (ROC) curve, with 2 different definitions depending on the way subjects who died without a dementia diagnosis were considered. To account for right-censoring and interval censoring, we estimated the ROC curves by means of a weighting approach and a model-based imputation estimator. The combined score exhibited an area under the ROC curve (AUROC) of 0.81 for discriminating future demented subjects from subjects alive and nondemented 10 years later and an AUROC of 0.75 for discriminating future demented subjects from all other subjects (including deceased persons). Adjustment for cognitive change over the previous year did not improve prediction.