245 * A RISK MODEL TO PREDICT LATE-ONSET CHRONIC RENAL FAILURE POST LUNG TRANSPLANTATION

Abstract

Objectives: Chronic renal failure (CRF) complicates upto 25% of all lung transplantation (LT) recipients. We sought to develop a simple model to predict this complication Methods: We retrospectively reviewed data from the United Network for Organ Sharing (UNOS) registry from 2000 to 2012. We evaluated adult recipients with estimated glomerular filtration rate (eGFR) >60 ml/min per 1.73 m2 at the time of transplant which dropped to less than 60 at 5 years post LT. We used Cox proportional modelling to identify risk factors most closely associated with the development of CRF. The most parsimonious model was selected using Akaike's information criteria. The risk calculator was generated using the D'Agostino-Framingham method. The estimated risk of CRF to predict the risk of developing CRF was obtained by summing the products of the coefficients and derived risk factors. CRF = 1 - 0.9α, where α is the sum of the products of the coefficients obtained from the risk model Results: A total of 3727 recipients were included in the analysis. Approximately 9% developed chronic renal failure. There was a 91% 5-year renal-failure-free survival. The data show that cumulative probability of CRF increased dramatically after 5 years. The strongest predictors included recipient age, diabetes, IPF, cystic fibrosis and intensive care unit (ICU) hospitalisation at the time of transplantation, allowing an estimation of the risk of developing CRF using pretransplant variables Conclusion: Risk of CRF increases exponentially 5 years post LT and varies with pretransplant characteristics. We have identified a novel mathematical tool to predict CRF post LT.