Validation of the Southampton Difficulty Scoring System in Robotic Liver Surgery and Development of the International RoboLiver Difficulty Scoring System
BACKGROUND: Robotic liver surgery (RLS) provides technical advantages over laparoscopic liver surgery (LLS), but no validated robotic-specific difficulty scoring system (DSS) exists. We evaluated the applicability of the Southampton DSS to RLS and developed a dedicated RLS difficulty model. STUDY DESIGN: This multicenter retrospective cohort study included adults undergoing planned RLS across 24 international hepatobiliary centers. The Southampton DSS was assessed for calibration and discrimination in predicting intraoperative complications. Given limited performance, a robotic-specific model (International RoboLiver DSS) was developed using multivariable logistic regression with prolonged operative time (>280 minutes; 75th percentile) as a surrogate of technical difficulty. Model discrimination, calibration, and bootstrap internal validation were performed. RESULTS: Among 1,497 RLS patients, higher Southampton DSS categories were associated with increased intraoperative complications (p=0.003); however, discrimination was poor (AUC 0.571, 95% CI 0.530-0.612) with miscalibration (slope 0.43; intercept 0.06). Independent predictors of prolonged operative time included neoadjuvant chemotherapy, prior extrahepatic surgery, lesion >50 mm, multiple lesions, bilobar disease, and technically or anatomically major resection. The International RoboLiver DSS demonstrated moderate discrimination (AUC 0.719, 95% CI 0.686-0.751) with excellent calibration (intercept 0.00; slope 1.14). Bootstrap validation confirmed model stability (corrected AUC 0.719). CONCLUSIONS: Difficulty factors in RLS partially overlap with LLS but are not directly transferable. The International RoboLiver DSS provides a calibrated, robot-specific tool for preoperative complexity stratification and operative planning in RLS. External validation is required.
Authors
- U Cillo
- M Abu Hilal
- Михаил Ефанов (ORCID: https://orcid.org/0000-0003-0738-7642)
- R. Méméo
- Gabriela Pilz da Cunha (ORCID: https://orcid.org/0000-0001-9055-1049)
- Soufyan el Adel
- Mohammed Alzoubi
- Ricardo Robles Campos
- Brian K.P. Goh
- Rong Liu
- Iswanto Sucandy
- Steven White
- Jawad Ahmad
- Rutger-Jan Swijnenburg
- Elio Jovine
- Adnan Alseidi
- Francesca Ratti
- David Geller
- Mathieu D’Hondt
- Ernesto Sparrelid
- Andrea Ruzzenente
- Dionisios Vrochides
- Fabrizio Di Benedetto
- Roland Croner
- Tom K. Gallagher
- Susan van Dieren
- Moritz Schmelzle
Institutions
- University of Jordan (JO)
- University of Verona (IT)
- University of Modena and Reggio Emilia (IT)
- Karolinska University Hospital (SE)
- University of Padua (IT)
- Vita-Salute San Raffaele University (IT)
- University of Pittsburgh (US)
- AZ Groeninge (BE)
- Southampton General Hospital (GB)
- Newcastle upon Tyne Hospitals NHS Foundation Trust (GB)
- University Hospital Coventry (GB)
- Carolinas Medical Center (US)
- Chinese PLA General Hospital (CN)
- Medizinische Hochschule Hannover (DE)
- Moscow Clinical Scientific Center (RU)
- IRCCS Azienda Ospedliero-Universitaria di Bologna Policlinico di Sant'Orsola (IT)
- Amsterdam Neuroscience (NL)
- University of California San Francisco Medical Center (US)
- St. Vincent's University Hospital (IE)
- IRCCS Ospedale San Raffaele (IT)
- AdventHealth Tampa (US)
- National Cancer Centre Singapore (SG)
- University Hospital Magdeburg (DE)
- Ospedale Generale Regionale Francesco Miulli (IT)
- Hospital Universitario Virgen de la Arrixaca (ES)
- Cancer Center Amsterdam (NL)
- Amsterdam University of Applied Sciences (NL)
- University of Amsterdam (NL)
- University of Bologna (IT)
- Otto-von-Guericke-Universität Magdeburg (DE)
Publication Details
- Journal
- Journal of the American College of Surgeons
- Published
- 2026-04-16
- DOI
- https://doi.org/10.1097/xcs.0000000000001978
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00