A Multivariable Model for Predicting Intraoperative Blood Loss in Pediatric Liver Transplantation

Background/Objectives: Intraoperative bleeding remains one of the major challenges in pediatric liver transplantation (PLT), contributing significantly to perioperative morbidity, transfusion-related complications, and prolonged recovery. Although viscoelastic testing has improved intraoperative hemostatic management, there are currently no validated preoperative tools capable of predicting bleeding risk in this vulnerable population. Methods: We conducted a prospective, single-center observational study including 43 consecutive pediatric patients who underwent orthotopic liver transplantation between May 2008 and August 2009. A comprehensive dataset encompassing demographic, clinical, biochemical, and surgical variables was collected. A multivariable linear regression model was developed to predict intraoperative blood loss (IBL). Variable selection was guided by Mallows’ Cp criterion to ensure optimal model fit and clinical interpretability. Model performance was assessed using adjusted R2, diagnostic residual analysis, and internal validation to verify regression assumptions. Results: Six independent predictors of IBL were identified: presence of ascites, prior abdominal surgery, operative time, baseline fibrinogen concentration, platelet count, and recipient weight. The final model explained 35.2% of IBL variance (adjusted R2 = 0.352; F = 7.68; p < 0.001). Model diagnostics confirmed linearity, normal distribution of residuals, and homoscedasticity, supporting its robustness and reliability. Conclusions: This multivariable model provides an interpretable, clinically applicable framework for individualized preoperative estimation of blood loss in PLT. It may assist in planning perioperative patient blood management strategies and serve as a foundation for future decision-support systems. Limitations include the single-center design and modest sample size; however, internal validation supported the stability and reliability of the model.