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Learning Curve of Robotic Atrial Septal Defect Repair: A Multidimensional CUSUM Analysis of 284 Consecutive Cases
Zinar Apaydın1, İsa Can1, Zihni Mert Duman2
1Department of Cardiovascular Surgery, Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Trainning and Research Hospital, İstanbul, Turkey.
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.
