Regional Disparities in Kidney Transplant Allocation in Brazil: A Retrospective Cohort Study.

BACKGROUND: Brazil has a large public transplant program, but it remains unclear if the kidney waitlist criteria effectively allocate organs. This study aimed to investigate whether gender, ethnicity, clinical characteristics, and Brazilian regions affect the chance of deceased donor kidney transplant (DDKT). METHODS: We conducted a retrospective cohort study using the National Transplant System/Brazil database, which included all patients on the kidney transplant waitlist from January 2012 to December 2022, followed until May 2023. The primary outcome assessed was the chance of DDKT, measured using subdistribution hazard and cause-specific hazard models (subdistribution hazard ratio [sHR]). RESULTS: We analyzed 118 617 waitlisted patients over a 10-year study period. Male patients had an sHR of 1.07 ([95% CI: 1.05-1.10], p < 0.001), indicating a higher chance of DDTK. Patients of mixed race and Yellow/Indigenous ethnicity had lower rates of receiving a transplant compared to Caucasian patients, with sHR of 0.97 (95% CI: 0.95-1) and 0.89 (95% CI: 0.95-1), respectively. Patients from the South region had the highest chance of DDKT, followed by those from the Midwest and Northeast, compared to patients from the Southeast, with sHR of 2.53 (95% CI: 2.47-2.61), 1.21 (95% CI: 1.16-1.27), and 1.10 (95% CI: 1.07-1.13), respectively. The North region had the lowest chance of DDTK, sHR of 0.29 (95% CI: 0.27-0.31). CONCLUSION: We found that women and racial minorities faced disadvantages in kidney transplantation. Additionally, we observed regional disparities, with the North region having the lowest chance of DDKT and longer times on dialysis before being waitlisted. In contrast, patients in the South regions had a chance of DDKT and shorter times on dialysis before being waitlisted. It is urgent to implement approaches to enhance transplant capacity in the North region and address race and gender disparities in transplantation.

Machine Learning Model to Predict Graft Rejection After Kidney Transplantation.

BACKGROUND: There are few predictive studies about early posttransplant outcomes taking into account baseline and posttransplant variables. The objective of this study was to create a predictive model for 30-day graft rejection using machine learning techniques. METHODS: Retrospective study with 1255 patients undergoing transplant from living and deceased donors at a tertiary health service in Brazil. Recipient, donor, transplantation, and postoperative period data were collected from physical and electronic records. We split the data into derivation (training) and validation (test) datasets. Five supervised machine learning algorithms were developed with this subset of variables in the training set: Simple Logistic Regression, Lasso, Multilayer Perceptron, XGBoost, and Light GBM. RESULTS: There were 147 (12.48%) cases of graft rejection within 30 days of transplantation. The best model was XGBoost (accuracy, 0.839; receiver operating characteristic area under the curve, 0.715; precision, 0.900). The model showed that deceased donor transplantation, glomerulopathy as an underlying disease, and donor's use of vasoactive drugs had more than 20% importance as rejection risk factors. The variables with the greatest predictive values were thymoglobulin induction and delayed graft function. CONCLUSIONS: We fitted a machine learning model to predict 30-day graft rejection after kidney transplantation that reaches a higher accuracy and precision. Machine learning models could contribute to predicting kidney survival using nontraditional approaches.

Impact of the COVID-19 Pandemic on Solid Organ Transplant and Rejection Episodes in Brazil's Unified Healthcare System.

BACKGROUND: Brazil has the world's largest public organ transplant program, which was severely affected by the COVID-19 pandemic. The primary aim of the study was to evaluate differences in solid organ transplants and rejection episodes during the COVID-19 pandemic compared to the five years before the pandemic in the country. METHODS: A seven-year database was built by downloading data from the DATASUS server. The pandemic period was defined as March 2020 to December 2021. The pre-pandemic period was from January 2015 to March 2020. RESULTS: During the pandemic, the number of solid organ transplants decreased by 19.3% in 2020 and 22.6% in 2021 compared to 2019. We found a decrease for each evaluated organ, which was more pronounced for lung, pancreas, and kidney transplants. The seasonal plot of rejection data indicated a high rejection rate between 2018 and 2021. There was also an 18% (IRR 1.18 (95% CI 1.01 to 1.37), p = 0.04) increase in the rejection rate during the COVID-19 pandemic. CONCLUSIONS: The total number of organ transplants performed in 2021 represents a setback of six years. Transplant procedures were concentrated in the Southeast region of the country, and a higher proportion of rejections occurred during the pandemic. Together, these findings could have an impact on transplant procedures and outcomes in Brazil.