Learning Curve of Robotic Atrial Septal Defect Repair: A Multidimensional CUSUM Analysis of 284 Consecutive Cases.

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.