ArticleSurgical endoscopy2026
Robotic liver resection learning curves stratified by IWATE criteria.
Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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11 authors.
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Abstract
backgroundCharacterizing the learning curve of robotic hepatectomy is important to facilitate safe adoption; however, variability in case complexity can confound the interpretation of non-stratified learning curve analyses. This study evaluated learning curves for robotic hepatectomy stratified according to procedural difficulty.
methodsWe included consecutive robotic hepatectomies performed by a single surgical team at two institutions between 2017 and 2025. Cases were classified as low-, intermediate-, advanced-, and expert-level based on the IWATE criteria. Cumulative sum (CUSUM) analysis of operative time was performed to define the learning curve for each complexity level.
resultsAmong a total of 174 cases (31 low-, 70 intermediate-, 34 advanced-, and 39 expert-level), the median age was 63 years, and 57% of patients were female. Mean operative time correlated with case complexity, ranging from 165 min in low-level cases to 368 min in expert-level cases (p<0.0001). Throughout the study period, lower-complexity resections continued to be performed while progressively more complex procedures were incorporated. CUSUM analysis demonstrated distinct competency thresholds: 14 cases for low-difficulty procedures and 29 cases for intermediate-level cases. The learning curves were shorter for advanced- and expert-level resections: 17 and 23 cases, respectively. The overall non-stratified learning curve stabilized at 89 cases. The 90-day major morbidity rate was 4%, and overall complication rates were comparable between the pre- and post-competency cohorts.
conclusionsIWATE-stratified analysis identified distinct learning curves across procedural complexity levels. The shorter competency thresholds observed for higher-complexity resections suggest that experience gained during lower-complexity procedures accelerates proficiency in complex resections, resulting in overlapping rather than sequential learning curves. These metrics should be interpreted as estimates derived from a real-world practice with an evolving case mix, rather than successive milestones, and may provide a practical framework for implementing robotic hepatectomy.
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