Comparison of the accuracy of predictive models in early detection of clinically relevant posthepatectomy liver failure

Post-hepatectomy liver failure (PHLF) is a leading cause of perioperative mortality following liver resection. Early detection and prediction of clinically relevant post-hepatectomy liver failure (CR-PHLF) remain critical but challenging. Lactate has shown promise as a biomarker, but its predictive power when combined with other factors remains unclear. This study retrospectively analyzed 915 patients who underwent liver resection at Zhejiang Provincial People's Hospital. Variables including demographics, liver function markers, intraoperative blood loss, and postoperative lactate levels were assessed. Multivariate logistic regression identified significant predictors for CR-PHLF, and a nomogram was created. The model's performance was evaluated using ROC curves and decision curve analysis. In this study, Multivariate logistic regression was applied to select 6 predictors from the relevant variables, which were gender, ICGR-15, intraoperative blood loss, transfusion, resection extent, and lactate. In the training set, the AUC of the model was 0.781, significantly outperforming traditional models like ALBI and APRI. In the validation set, the model's AUC was 0.812, indicating robust predictive accuracy. The integrated model combining lactate and intraoperative factors provides a more accurate prediction of CR-PHLF risk. It outperforms existing models and has significant potential for improving preoperative risk assessment and intraoperative decision-making.