Predicting prognostic factors in kidney transplantation using a machine learning approach to enhance outcome predictions: a retrospective cohort study.

BACKGROUND: Accurate forecasting of clinical outcomes after kidney transplantation is essential for improving patient care and increasing the success rates of transplants. Our study employs advanced machine learning (ML) algorithms to identify crucial prognostic indicators for kidney transplantation. By analyzing complex datasets with ML models, we aim to enhance prediction accuracy and provide valuable insights to support clinical decision-making. MATERIALS AND METHODS: Analyzing data from 4077 KT patients (June 1990 - May 2015) at a single center, this research included 27 features encompassing recipient/donor traits and peri-transplant data. The dataset was divided into training (80%) and testing (20%) sets. Four ML models-eXtreme Gradient Boosting (XGBoost), Feedforward Neural Network, Logistic Regression, and Support Vector Machine-were trained on carefully selected features to predict the success of graft survival. Performance was assessed by precision, sensitivity, F1 score, Area Under the Receiver Operating Characteristic (AUROC), and Area Under the Precision-Recall Curve. RESULTS: XGBoost emerged as the best model, with an AUROC of 0.828, identifying key survival predictors like T-cell flow crossmatch positivity, creatinine levels two years post-transplant and human leukocyte antigen mismatch. The study also examined the prognostic importance of histological features identified by the Banff criteria for renal biopsy, emphasizing the significance of intimal arteritis, interstitial inflammation, and chronic glomerulopathy. CONCLUSION: The study developed ML models that pinpoint clinical factors crucial for KT graft survival, aiding clinicians in making informed post-transplant care decisions. Incorporating these findings with the Banff classification could improve renal pathology diagnosis and treatment, offering a data-driven approach to prioritizing pathology scores.

Development and validation of risk prediction model for post-donation renal function in living kidney donors.

This study aimed to create and validate a predictive model for renal function following live kidney donation, using pre-donation factors. Accurately predicting remaining renal function post live kidney donation is currently insufficient, necessitating an effective assessment tool. A multicenter retrospective study of 2318 live kidney donors from two independent centers (May 2007-December 2019) was conducted. The primary endpoint was the reduction in eGFR to below 60 mL/min/m2 6 months post-donation. The primary endpoint was achieved in 14.4% of the training cohort and 25.8% of the validation cohort. Sex, age, BMI, hypertension, preoperative eGFR, and remnant kidney proportion (RKP) measured by computerized tomography (CT) volumetry were found significant in the univariable analysis. These variables informed a scoring system based on multivariable analysis: sex (male: 1, female: 0), age at operation (< 30: 0, 30-39: 1, 40-59: 2, ≥ 60: 3), preoperative eGFR (≥ 100: 0, 90-99: 2, 80-89: 4, < 80: 5), and RKP (≥ 52%: 0, < 52%: 1). The total score ranged from 0 to 10. The model showed good discrimination for the primary endpoint in both cohorts. The prediction model provides a useful tool for estimating post-donation renal dysfunction risk, factoring in the side of the donated kidney. It offers potential enhancement to pre-donation evaluations.

Immunoprotective Effect of Liver Allograft on Patients with Combined Liver and Kidney Transplantation.

BACKGROUND Simultaneous liver-kidney transplantation (SLKT) and kidney transplantation (KT) after liver transplantation (LT) provide potential treatment options for patients with end-stage liver and kidney disease. There is increasing attention being given to liver-kidney transplantation (LTKT), particularly regarding the immune-protective effects of the liver graft. This retrospective, single-center, observational study aimed to evaluate the clinical outcomes of KT in LTKT patients - either SLKT or KT after LT (KALT) - compared to KT alone (KTA). MATERIAL AND METHODS We included patients who underwent KT between January 2005 and December 2020, comprising a total of 4312 patients divided into KTA (n=4268) and LTKT (n=44) groups. The LTKT group included 11 SLKT and 33 KALT patients. To balance the difference in sample sizes between the 2 groups, we performed 3: 1 propensity score matching (PSM). RESULTS There was no significant difference in graft survival between the groups. However, the LTKT group exhibited significantly superior rejection-free survival compared to the KTA group (P.