Predicting surgical difficulty in robotic liver resection: applicability of laparoscopic scores

Background Predicting surgical difficulty in robotic liver resection (RLR) is crucial for optimizing resource allocation, training programs, and patient outcomes. While several difficulty scoring systems (DSSs) have been validated for laparoscopic liver resection (LLR), their applicability to RLR remains uncertain. This study evaluates the predictive performance of five DSSs: Halls Southampton Score (HSS), Ban Iwate Score (BIS), Hasegawa Score (HGS), Institut Mutualiste Montsouris Score (IMM), and Tampa Difficulty Score (TAS), in the robotic setting. Methods A retrospective study was conducted on 124 patients who underwent RLR between January 2011 and June 2024 at two high-volume centers. Each DSS was retrospectively applied. Predictive accuracy for operative duration, intraoperative blood loss (>400 mL), transfusion need, postoperative complications, surgical reintervention, and 90-day readmission was assessed using R2 (continuous variables) and AUC (categorical outcomes). Results HSS demonstrated the highest overall predictive power, particularly for transfusion need (AUC = 0,85), postoperative complications (AUC = 0,74), and 90-day readmission (AUC = 0,86). BIS was the most accurate for intraoperative blood loss (R2 = 0,32). TAS showed the lowest predictive performance across most outcomes. Conclusion Laparoscopic DSSs are applicable to RLR, with HSS emerging as the most reliable. TAS requires further validation. A combined DSS approach could improve surgical planning and patient management.