Bayesian analysis of glomerular filtration rate trajectories in kidney transplant recipients: a pilot study

Detailed modeling and analysis of renal (dys)function trajectories has not been undertaken in kidney transplant recipients. Although previous studies have assumed linear trajectories, this likely represents an oversimplification.

In this study, a Bayesian smoothing technique was undertaken to create 10,000 Monte Carlo samples for each of 158 patients over a median of 88 months. Specific parameters investigated were the prevalence of nonlinear trajectories, periods of nonprogression, and of rapid progression.

Forty-five (28%) patients displayed high probability (>80%) for a nonlinear trajectory. Periods of nonprogression were also common, present in 110 (70%) patients. A substantial proportion of patients showed deviation from the classic paradigm of progressive linear loss of graft function with 137 (87%) patients displaying nonlinearity or nonprogression. Only nine (6%) patients demonstrated at least one episode of nonprogression after an episode of progression, that is, once progression occurred, a subsequent period of nonprogression was uncommon. Episodes of nonprogression were less common (P < 0.001) in patients whose grafts subsequently failed, whereas episodes of rapid progression were more common (P = 0.04).

This study highlights the often nonlinear and nonprogressive nature of renal function decline after transplantation. Heightened understanding of the factors influencing these trajectories should help inform patients and clinicians alike.