INTRODUCTION: Postoperative complications, including post-hepatectomy liver failure (PHLF), remain a major limiting factor in liver surgery and are directly associated with increased mortality and impaired long-term survival.AIM: To evaluate the predictive performance of contemporary objective functional (ALBI, MELD-Na) and structural models for postoperative risk assessment following liver resection for oncologic diseases, in comparison with the conventional Child–Pugh classification.MATERIALS AND METHODS: A retrospective–prospective analysis was conducted on 278 patients (2010–2024) who underwent liver resection for primary and metastatic liver tumors. Preoperative assessment included Child–Pugh, ALBI, MELD-Na, APRI, and FIB-4 scores. Correlation, univariate, and multivariate logistic regression analyses were performed, along with Kaplan–Meier survival analysis.RESULTS AND DISCUSSION: The Child–Pugh classification categorized over 80% of patients as class A, without adequate risk stratification. ALBI and MELD-Na demonstrated significant predictive value for postoperative complications and PHLF. Multivariate analysis identified ALBI grade 3 and elevated MELD-Na values as independent predictors of postoperative liver dysfunction (OR=6.892 and OR=5.758, respectively). Stratified analysis revealed distinct risk profiles: functional markers predominated in primary tumors, whereas in synchronous metastases, a combined effect of functional and structural factors was observed. Patients with high functional risk (ALBI 3, high MELD-Na) exhibited significantly reduced long-term survival, and the occurrence of liver-specific complications was associated with a substantial decrease in overall survival.CONCLUSION: Objective mathematical models significantly outperform conventional systems in predicting postoperative risk. Their implementation enables precise individualization of surgical strategy and represents a critical prerequisite for improving outcomes in liver oncologic surgery.
Building similarity graph...
Analyzing shared references across papers
Loading...
G. Korukov
Elena Arabadzhieva
Atanas Yonkov
Medical University of Sofia
Alexandrovska Hospital
Building similarity graph...
Analyzing shared references across papers
Loading...
Korukov et al. (Fri,) studied this question.
synapsesocial.com/papers/69f6e6968071d4f1bdfc7477 — DOI: https://doi.org/10.5281/zenodo.19960475
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: