Session Time: 8:30am-9:45am
Presentation Time: 9:00am-9:15am
Location: Veterans Auditorium
*Purpose: A significant proportion of patients develop chronic kidney disease after liver transplantation (LT). We aimed to develop clinical/protein models to predict future GFR deterioration in recipients with normal GFR early after LT.
*Methods: Using independent discovery and validation cohorts (CTOT14: a seven-center prospective NIAID study and BUMC: a single center cohort), we analyzed protocol serum/plasma samples for kidney injury proteins from LT recipients with month 3 preserved GFR (>60ml/min/1.73m2) who had early GFR deterioration (>10% persistent decline) vs. preservation at year 1. Late, more severe GFR deterioration (<45 ml/min/1.73m2) by year 5 was also analyzed at BUMC. In a linear mixed model, we also tested the correlation between serial changes in protein levels and GFR over the first year of CTOT14.
*Results: Using the CTOT14 cohort (n=61), a month 3 model was constructed from 7 clinical and 16 protein candidate variables. HCV infection and levels of β2-microglobulin and CD40 predicted early GFR deterioration (AUC 0.814, Figure 1). We observed excellent validation of this model (AUC 0.805, Figure 2) in the BUMC cohort (n=50) who had both early and late GFR deterioration. At an optimal threshold, the model had the following performance characteristics in CTOT14 and BUMC, respectively: accuracy (0.75, 0.8), sensitivity (0.71, 0.67), specificity (0.78, 0.88), positive predictive value (0.74, 0.75) and negative predictive value (0.76, 0.82). In the serial CTOT-14 analysis, several proteins, including α1-microglobulin, β2-microglobulin, cystatin C, trefoil factor 3, thrombomodulin, and uromodulin, correlated with deterioration in GFR over the first year.
*Conclusions: We have validated a clinical/protein model at month 3 post-LT (when GFR is preserved) that can predict future renal deterioration with high accuracy. In addition, several of these kidney injury proteins serially increase with GFR deterioration over time. These approaches may identify the most appropriate candidates for early proactive renal sparing strategies to protect long term kidney function.
To cite this abstract in AMA style:Levitsky J, Asrani S, Klintmalm G, Schiano T, Moss A, Chavin K, Miller C, Zhao L, Guo K, Bridges N, Odim J, Brown M, Ikle D, Armstrong B, Abecassis M. Discovery and Validation of an Early Post-Transplant Biomarker Model Predictive of Chronic Kidney Disease in Liver Transplant Recipients [abstract]. Am J Transplant. 2019; 19 (suppl 3). https://atcmeetingabstracts.com/abstract/discovery-and-validation-of-an-early-post-transplant-biomarker-model-predictive-of-chronic-kidney-disease-in-liver-transplant-recipients/. Accessed February 22, 2020.
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