Neural network-based risk score for acute kidney injury after liver transplantation

Acute kidney injury (AKI) after liver transplantation (LT) has a multifactorial origin, making risk prediction challenging. Given its significant impact on prognosis, a reliable and clinically applicable prediction model is needed. The present study aims to develop a risk score for predicting post-LT AKI using an artificial neural network (ANN) model.

A total of 145 patients who underwent deceased donor LT (DDLT) were analyzed. Data included recipient demographics and comorbidities, donor and graft characteristics, intraoperative variables, and laboratory findings. The primary outcome was postoperative AKI defined by the International Club of Ascites criteria. Independent predictors were identified using logistic regression and incorporated into the ANN model. Model performance was assessed and compared with logistic regression. A risk score was developed based on the β coefficients of the predictors and stratified into risk groups according to ANN outputs.

AKI incidence was 60.6% (88/145). Independent predictors included MELD ≥25, preexisting kidney dysfunction, extended criteria donor grafts, intraoperative arterial hypotension, intraoperative massive transfusion, and serum lactate ≥2 mmol/L at the end of surgery. The ANN model showed better discrimination than logistic regression (AUROC 0.81 vs. 0.71) and good calibration (Hosmer-Lemeshow χ² = 5.57, p = 0.612). Patients were stratified into low (0-6), moderate (7-15), and high (16-22) risk groups, with significantly different AKI incidences.

This ANN-based risk score is a practical tool for early identification of patients at risk of post-LT AKI, supporting timely preventive and therapeutic strategies.