Learning Curves and Complexity Evolution in Robotic Liver Surgery

OBJECTIVE: To evaluate the institutional learning curve of robotic liver resection (RLR), its association with surgical complexity and outcomes in an international multicenter cohort. SUMMARY OF BACKGROUND DATA: RLR is increasingly adopted worldwide, but the dynamics of the learning process and its impact on surgical outcomes remain poorly defined. METHODS: This international, multicenter, retrospective cohort study included consecutive patients who underwent RLR between 06/2013 and 12/2024 across 22 expert centers. Based on the first 150 cases at each center, three phases were used to define the learning curve: operative time for competency, major complications for proficiency, and textbook outcome for mastery. Risk-adjusted generalized additive models were used to assess the learning process. Benchmark performance was compared to international reference values. RESULTS: Among 2,331 patients, the most common indications were hepatocellular carcinoma (36%), benign lesions (23%) and colorectal liver metastases (21%). Operative time decreased after achieving competency (case #62), from 240 to 220 minutes (P<0.001). Major morbidity reached a plateau during the proficiency phase (cases #63-138; 9% vs. 11%, P=0.144), while progression to mastery (cases #139-150) was associated with a higher rate of textbook outcome (76% vs. 87%, P=0.021). In contrast, conversion (5.1% vs. 4.9% vs. 2%), bile leak (4.0% vs. 5.3% vs. 3.8%), and 90-day mortality (0.7% vs. 0.9% vs. 0.9%) remained low and stable across all learning phases. Importantly, benchmark criteria for operative time, hospital stay, and textbook outcome were met across all complexity groups, including during the early learning phase. CONCLUSIONS: Robotic liver surgery shows progressive outcome improvement with early achievement of benchmark-level results and stable performance across increasing complexity. These findings indicate that, in experienced centers, RLR can be safely implemented and integrated into clinical practice, supporting its key role in modern hepatobiliary surgery.

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Publication Details

Journal
Annals of Surgery
Published
2026-06-17
DOI
https://doi.org/10.1097/sla.0000000000007123
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
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article
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article

Learning Curves and Complexity Evolution in Robotic Liver Surgery

Davide Ghinolfi, Gabriel Fridolin Hess, Daisuke Fukumori, A Andreou et al.
Annals of Surgery
Hepatocellular Carcinoma Treatment and Prognosis
article

Learning Curves and Complexity Evolution in Robotic Liver Surgery

Davide Ghinolfi, Gabriel Fridolin Hess, Daisuke Fukumori, A Andreou, Taiga Wakabayashi, Daniel Seehofer, Céline De Meyere, Haluk Morgül, Fabrizio Di Benedetto, L. Bernardi, Caroline Berchtold, Mirhasan Rahimli, Adrian T. Billeter, E Balzano, Sanjay Pandanaboyana, Giuseppe Ettore, Brian K. P. Goh, Alessandra Cristaudi, Emrullah Birgin, Iswanto Sucandy, Philip C. Müller, Cristiano Guidetti, Stefan Gilg, RS Croner, Christoph Kuemmerli, Asmus Heumann, Roberta Odorizzi, Ignacio Sánchez Esquer, Go Wakabayashi, Beat P. Müller, Noa L. E. Aegerter, Nuh N. Rahbari, Y Dong, P Starlinger, M D’hondt, Felix Nickel, P Von Kroge, F Ratti, Florian Primavesi, Martina Nebbia, Victor Lopez-Lopez, Ricardo Robles-Campos, Ye-Xin Koh, Charles Chung-Wei Lin, Steve White, Christoph Tschuor, Stefano Ferretti, Stefan Stättner, Fabian Haak
article en

Abstract

OBJECTIVE: To evaluate the institutional learning curve of robotic liver resection (RLR), its association with surgical complexity and outcomes in an international multicenter cohort. SUMMARY OF BACKGROUND DATA: RLR is increasingly adopted worldwide, but the dynamics of the learning process and its impact on surgical outcomes remain poorly defined. METHODS: This international, multicenter, retrospective cohort study included consecutive patients who underwent RLR between 06/2013 and 12/2024 across 22 expert centers. Based on the first 150 cases at each center, three phases were used to define the learning curve: operative time for competency, major complications for proficiency, and textbook outcome for mastery. Risk-adjusted generalized additive models were used to assess the learning process. Benchmark performance was compared to international reference values. RESULTS: Among 2,331 patients, the most common indications were hepatocellular carcinoma (36%), benign lesions (23%) and colorectal liver metastases (21%). Operative time decreased after achieving competency (case #62), from 240 to 220 minutes (P<0.001). Major morbidity reached a plateau during the proficiency phase (cases #63-138; 9% vs. 11%, P=0.144), while progression to mastery (cases #139-150) was associated with a higher rate of textbook outcome (76% vs. 87%, P=0.021). In contrast, conversion (5.1% vs. 4.9% vs. 2%), bile leak (4.0% vs. 5.3% vs. 3.8%), and 90-day mortality (0.7% vs. 0.9% vs. 0.9%) remained low and stable across all learning phases. Importantly, benchmark criteria for operative time, hospital stay, and textbook outcome were met across all complexity groups, including during the early learning phase. CONCLUSIONS: Robotic liver surgery shows progressive outcome improvement with early achievement of benchmark-level results and stable performance across increasing complexity. These findings indicate that, in experienced centers, RLR can be safely implemented and integrated into clinical practice, supporting its key role in modern hepatobiliary surgery.

Annals of Surgery
University of Modena and Reggio Emilia (IT), Karolinska University Hospital (SE), Innsbruck Medical University (AT), Vita-Salute San Raffaele University (IT), Universität Hamburg (DE), Universität Innsbruck (AT), Singapore General Hospital (SG), AZ Groeninge (BE), Freeman Hospital (GB), University Hospital of Basel (CH), Copenhagen University Hospital (DK), Rigshospitalet (DK), Karolinska Institutet (SE), San Raffaele University of Rome (IT), Ente Ospedaliero Cantonale (CH), Klinikum Magdeburg (DE), University Medical Center Hamburg-Eppendorf (DE), Kepler Universitätsklinikum (AT), Chang Bing Show Chwan Memorial Hospital (TW), Mayo Clinic in Arizona (US), University Hospital Leipzig (DE), AdventHealth Tampa (US), Unfallkrankenhaus Salzburg (AT), Instituto Murciano de Investigación Biosanitaria (ES), Hakodate Central General Hospital (JP), Carlo Forlanini Hospital (IT), University Hospital Ulm (DE), Azienda Ospedaliera Universitaria Pisana (IT)
Openalex Percentile: Top 8%
Hepatocellular Carcinoma Treatment and Prognosis
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