Risk factors for graft loss and death among kidney transplant recipients: A competing risk analysis

• Published in: PloS one 2022-07, Vol.17 (7), p.e0269990-e0269990
• Main Authors: Pinto-Ramirez, Jessica, Garcia-Lopez, Andrea, Salcedo-Herrera, Sergio, Patino-Jaramillo, Nasly, Garcia-Lopez, Juan, Barbosa-Salinas, Jefferson, Riveros-Enriquez, Sergio, Hernandez-Herrera, Gilma, Giron-Luque, Fernando
• Format: Article
• Language: English
• Subjects: Antigens; Biology and Life Sciences; Biopsy; Blood & organ donations; Calibration; Consent; Creatinine; Death; Drug dosages; Evaluation; Graft rejection; Grafting; Health aspects; Health risks; Hemodialysis; Implants; Kidney transplantation; Kidney transplants; Kidneys; Mathematical analysis; Medical research; Medicine and Health Sciences; Modelling; Mortality; Nephropathy; Organ transplant recipients; Patients; Performance prediction; Prediction models; Risk analysis; Risk assessment; Risk factors; Survival; Transplantation; Variables
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0269990

Introduction Kidney transplantation is the best therapeutical option for CKD patients. Graft loss risk factors are usually estimated with the cox method. Competing risk analysis could be useful to determine the impact of different events affecting graft survival, the occurrence of an outcome of interest can be precluded by another. We aimed to determine the risk factors for graft loss in the presence of mortality as a competing event. Methods A retrospective cohort of 1454 kidney transplant recipients who were transplanted between July 1, 2008, to May 31, 2019, in Colombiana de Trasplantes, were analyzed to determine risk factors of graft loss and mortality at 5 years post-transplantation. Kidney and patient survival probabilities were estimated by the competing risk analysis. The Fine and Gray method was used to fit a multivariable model for each outcome. Three variable selection methods were compared, and the bootstrapping technique was used for internal validation as split method for resample. The performance of the final model was assessed calculating the prediction error, brier score, c-index and calibration plot. Results Graft loss occurred in 169 patients (11.6%) and death in 137 (9.4%). Cumulative incidence for graft loss and death was 15.8% and 13.8% respectively. In a multivariable analysis, we found that BKV nephropathy, serum creatinine and increased number of renal biopsies were significant risk factors for graft loss. On the other hand, recipient age, acute cellular rejection, CMV disease were risk factors for death, and recipients with living donor had better survival compared to deceased-donor transplant and coronary stent. The c-index were 0.6 and 0.72 for graft loss and death model respectively. Conclusion We developed two prediction models for graft loss and death 5 years post-transplantation by a unique transplant program in Colombia. Using a competing risk multivariable analysis, we were able to identify 3 significant risk factors for graft loss and 5 significant risk factors for death. This contributes to have a better understanding of risk factors for graft loss in a Latin-American population. The predictive performance of the models was mild.