The international journal of medical robotics + computer assisted surgery : MRCAS · 2026
Learning Curve of Robotic Atrial Septal Defect Repair: A Multidimensional CUSUM Analysis of 284 Consecutive Cases.
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Apaydın Zinar, Can İsa, Duman Zihni Mert, Yazıcı Batuhan, Timur Barış, Güler Salih, Kadiroğulları Ersin
Аннотация
OBJECTIVE: To characterise the learning curve of robotic atrial septal defect (ASD) repair using a multidimensional cumulative sum (CUSUM) framework.
METHODS: In this retrospective study, 284 consecutive adult patients undergoing robotic ASD repair were analysed. CUSUM analysis was applied to operative time, cardiopulmonary bypass time, cross-clamp time, intensive care unit (ICU) stay, and hospital stay. Learning phases were identified according to inflection points of the CUSUM curves.
RESULTS: Four distinct learning phases were observed. Technical proficiency was achieved at Case 158, marked by significant reductions in operative (186.8 ± 43.3 vs. 133.7 ± 27.7 min), bypass, and cross-clamp times (p < 0.001). ICU stay and transfusion requirements progressively declined, while mortality remained low across all phases. Notably, postoperative care optimisation lagged behind technical mastery.
CONCLUSIONS: Multidimensional CUSUM analysis provides an objective, performance-based assessment of robotic ASD learning. Competency acquisition required substantial case volume, underscoring the need for structured training and longitudinal performance monitoring in robotic cardiac surgery.
METHODS: In this retrospective study, 284 consecutive adult patients undergoing robotic ASD repair were analysed. CUSUM analysis was applied to operative time, cardiopulmonary bypass time, cross-clamp time, intensive care unit (ICU) stay, and hospital stay. Learning phases were identified according to inflection points of the CUSUM curves.
RESULTS: Four distinct learning phases were observed. Technical proficiency was achieved at Case 158, marked by significant reductions in operative (186.8 ± 43.3 vs. 133.7 ± 27.7 min), bypass, and cross-clamp times (p < 0.001). ICU stay and transfusion requirements progressively declined, while mortality remained low across all phases. Notably, postoperative care optimisation lagged behind technical mastery.
CONCLUSIONS: Multidimensional CUSUM analysis provides an objective, performance-based assessment of robotic ASD learning. Competency acquisition required substantial case volume, underscoring the need for structured training and longitudinal performance monitoring in robotic cardiac surgery.