Prognostic models in liver surgery for oncologic diseases: a comparative analysis of conventional and contemporary systems for operative risk assessment
DOI:
https://doi.org/10.67572/8ck19997Keywords:
liver resection, Child–Pugh, post-hepatectomy liver failure, ALBI score, MELD-Na, operative riskAbstract
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.
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