Revista do Colégio Brasileiro de Cirurgiões
https://revistadocbc.org.br/article/doi/10.1590/0100-6991e-2026006026
Revista do Colégio Brasileiro de Cirurgiões
Artigo Original

Escore de risco para injúria renal aguda após transplante hepático baseado em rede neural artificial

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

Luis César Bredt; Luis Alberto Batista Peres

http://dx.doi.org/10.1590/0100-6991e-2026006026 RCBC, vol.53, e2026006026, 2026
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Resumo

Introdução: A injúria renal aguda (IRA) após transplante hepático (TH) apresenta origem multifatorial, tornando a predição de risco um desafio. Dado seu impacto significativo no prognóstico, há necessidade de um modelo preditivo confiável e aplicável na prática clínica. O presente estudo tem o objetivo de desenvolver um escore de risco para predizer IRA após TH utilizando um modelo de rede neural artificial (RNA).

Métodos: Foram analisados 145 pacientes submetidos a TH com doador falecido (THDF). Os dados incluíram características demográficas e comorbidades do receptor, aspectos do doador e do enxerto, variáveis intraoperatórias e exames laboratoriais. O desfecho primário foi IRA pós-operatória definida pelos critérios do International Club of Ascites. Os preditores independentes foram identificados por regressão logística e incorporados ao modelo de RNA. O desempenho do modelo foi avaliado e comparado ao da regressão logística. Um escore de risco foi desenvolvido com base nos coeficientes β e estratificado em grupos de risco conforme a RNA.

Resultados: A incidência de IRA foi de 60,6% (88/145). Os preditores independentes foram: escore MELD ≥25, disfunção renal prévia, enxertos de doadores com critérios expandidos, hipotensão arterial intraoperatória, transfusão maciça intra-operatória, e lactato sérico ≥2 mmol/L ao final do transplante. O modelo de RNA apresentou melhor discriminação (AUROC 0,81 vs. 0,71) e boa calibração (Hosmer-Lemeshow χ² = 5,57; p = 0,612). Os pacientes foram estratificados em baixo (0-6), moderado (7-15) e alto risco (16-22), com diferenças significativas na incidência de IRA.

Conclusões: Este escore baseado em RNA é uma ferramenta prática para identificação precoce de pacientes com risco de IRA pós-THDF, auxiliando na adoção de estratégias preventivas e terapêuticas.

Palavras-chave

Injúria Renal Aguda; Transplante Hepático; Redes Neurais de Computação

Abstract

Introduction: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords

Acute Kidney Injury; Liver Transplantation; Computer Neural Networks

Referências

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Submetido em:
19/05/2026

Aceito em:
28/07/2026

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