Longitudinal progression trajectory of GFR among patients with CKD

Abstract

Background: The traditional paradigm of glomerular filtration rate (GFR) progression in patients with chronic kidney disease (CKD) is a steady nearly linear decline over time. We describe individual GFR progression trajectories over 12 years of follow-up in participants in the African American Study of Kidney Disease and Hypertension (AASK).

Study design: Longitudinal observational study.

Setting & participants: 846 AASK patients with at least 3 years of follow-up and 8 GFR estimates.

Measurements: Longitudinal GFR estimates from creatinine-based equations.

Predictors: Patient demographic and clinical features.

Outcomes: Probability of a nonlinear trajectory and probability of a period of nonprogression calculated for each patient from a Bayesian model of individual estimated GFR (eGFR) trajectories.

Results: 352 (41.6%) patients showed a > 0.9 probability of having either a nonlinear trajectory or a prolonged nonprogression period; in 559 (66.1%), the probability was > 0.5. Baseline eGFR > 40 mL/min/1.73 m2 and urine protein-creatinine ratio < 0.22 g/g were associated with a higher likelihood of a nonprogression period. 74 patients (8.7%) had both a substantial period of stable or increasing eGFR and a substantial period of rapid eGFR decrease.

Limitations: Clinical trial population; absence of direct GFR measurements.

Conclusions: In contrast to the traditional paradigm of steady GFR progression over time, many patients with CKD have a nonlinear GFR trajectory or a prolonged period of nonprogression. These findings highlight the possibility that stable kidney disease progression can accelerate and, conversely, provide hope that CKD need not be relentlessly progressive. These results should encourage researchers to identify time-dependent factors associated with periods of nonprogression and other desirable trajectories.

Figure 1

Distribution of the probability of nonlinearity, visualized by a percentage histogram (columns drawn to the scale of the vertical axis on the left), and a curve of the proportion of patients in the study sample (n=846) with higher probability (blue curve drawn to the scale of the vertical axis on the right). Below are three example trajectories with probabilities close to 0, 0.5, and 1. These were selected to illustrate the increasing oscillation as the probability of nonlinearity increases. On each trajectory plot, the horizontal axis is year since randomization, and the vertical axis is eGFR (mL/min/1.73m²). The blue dots are eGFR data, the black smooth curve is the estimated trajectory, and the bisque color band is the pointwise 95% Bayesian confidence interval. The red vertical line represents time of either censoring (dashed) or dialysis (solid). Fifteen of the 3,000 Monte Carlo trajectories are randomly selected and plotted for illustration (green curves).

Figure 2

Distribution of the probability of nonprogression. The layout is similar to Figure 1. The three example trajectories were selected to illustrate the increasing upward trend as the probability of nonprogression increases.

Figure 3

Distribution of the probability of either nonlinearity or nonprogression. The layout is similar to Figures 1 and 2, but without the sample trajectory plots.

Figure 4

GFR Trajectories of twelve patients and their probabilities of nonlinearity (Pr.nlin) and nonprogression (Pr.nprog). The setup of each trajectory plot is similar to those in Figure 1.

Figure 5

GFR Trajectories of nine patients, each having one of the features in Table 3. The setup of each trajectory plot is similar to those in Figure 1, except that the green, yellow or red segments highlight periods with the corresponding feature in Table 3.