Predicting kidney allograft survival with explainable machine learning.

ABSTRACT

INTRODUCTION: Despite significant progress over the last decades in the survival of kidney allografts, several risk factors remain contributing to worsening kidney function or even loss of transplants. We aimed to evaluate a new machine learning method to identify these variables which may predict the early graft loss in kidney transplant patients and to assess their usefulness for improving clinical decisions. MATERIAL AND METHODS: A retrospective cohort study was carried out with 627 kidney transplant patients followed at least three months. All these data were pre-processed, and their selected features were used to develop an automatically working a machine learning algorithm; this algorithm was then applied for training and parameterization of the model; and finally, the tested model was then used for the analysis of patients' features that were the most impactful for the prediction of clinical outcomes. Our models were evaluated using the Area Under the Curve (AUC), and the SHapley Additive exPlanations (SHAP) algorithm was used to interpret its predictions. RESULTS: The final selected model achieved a precision of 0.81, a sensitivity of 0.61, a specificity of 0.89, and an AUC value of 0.84. In our model, serum creatinine levels of kidney transplant patients, evaluated at the hospital discharge, proved to be the most important factor in the decision-making for the allograft loss. Patients with a weight equivalent to a BMI closer to the normal range prior to a kidney transplant are less likely to experience graft loss compared to patients with a BMI below the normal range. The age of patients at transplantation and Polyomavirus (BKPyV) infection had significant impact on clinical outcomes in our model. CONCLUSIONS: Our algorithm suggests that the main characteristics that impacted early allograft loss were serum creatinine levels at the hospital discharge, as well as the pre-transplant values such as body weight, age of patients, and their BKPyV infection. We propose that machine learning tools can be developed to effectively assist medical decision-making in kidney transplantation.