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Nephrol Dial Transplant. 2017 Apr 1;32(suppl_2):ii185-ii193. doi: 10.1093/ndt/gfw380.

Identifying subgroups of renal function trajectories.

Author information

1
Univ. Bordeaux, ISPED, Centre INSERM U1219-Bordeaux Population Health Research, Bordeaux, France.
2
CESP, Inserm, Univ Paris-Sud, UVSQ, Univ Paris-Saclay, Villejuif, France.
3
Centre Hospitalier Universitaire de Bordeaux, Service de Néphrologie Transplantation Dialyse, Bordeaux, France.
4
Unité INSERM 1026, University of Bordeaux, Bordeaux, France.

Abstract

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.

KEYWORDS:

chronic kidney disease progression; growth mixture modelling; latent class linear mixed model; measured glomerular filtration rate; trajectories

PMID:
28031345
DOI:
10.1093/ndt/gfw380

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