Development and Validation of Ultrasound Hemodynamic-based Prediction Models for Acute Kidney Injury After Renal Transplantation.

Abstract

Rationale and objectives: Acute kidney injury (AKI) post-renal transplantation often has a poor prognosis. This study aimed to identify patients with elevated risks of AKI after kidney transplantation.

Materials and methods: A retrospective analysis was conducted on 422 patients who underwent kidney transplants from January 2020 to April 2023. Participants from 2020 to 2022 were randomized to training group (n=261) and validation group 1 (n=113), and those in 2023, as validation group 2 (n=48). Risk factors were determined by employing logistic regression analysis alongside the least absolute shrinkage and selection operator, making use of ultrasound hemodynamic, clinical, and laboratory information. Models for prediction were developed using logistic regression analysis and six machine-learning techniques. The evaluation of the logistic regression model encompassed its discrimination, calibration, and applicability in clinical settings, and a nomogram was created to illustrate the model. SHapley Additive exPlanations were used to explain and visualize the best of the six machine learning models.

Results: The least absolute shrinkage and selection operator combined with logistic regression identified and incorporated five risk factors into the predictive model. The logistic regression model (AUC=0.927 in the validation set 1; AUC=0.968 in the validation set 2) and the random forest model (AUC=0.946 in the validation set 1;AUC=0.996 in the validation set 2) showed good performance post-validation, with no significant difference in their predictive accuracy.

Conclusion: These findings can assist clinicians in the early identification of patients at high risk for AKI, allowing for timely interventions and potentially enhancing the prognosis following kidney transplantation.