Learning Curve Analysis for Robotic-assisted Harvest of Deep Inferior Epigastric Perforator Flap

The deep inferior epigastric perforator (DIEP) flap is the preferred method for autologous breast reconstruction after mastectomy, but risks the development of hernia, bulge, and decreased core strength. Robotic harvest of DIEP vessels may limit abdominal wall morbidity through smaller fascial incisions and preservation of motor nerves. This study shows the expected learning curve (LC) for robotic harvest and compares the LC between a general surgeon (GS) and a plastic surgeon (PS). A retrospective cohort study was performed for patients who underwent bilateral robotic DIEP flap harvest from October 2021 to September 2022. We evaluated robotic pedicle dissection time (DT) and compared the times between GS and PS. We calculated LC for each surgeon using the cumulative sum (CUSUM) method, $$ . The LC was identified as the peak of the CUSUM graph. Forty-four flap dissections were recorded during the collection period: 27 by the PS and 17 by the GS. There was no significant difference in DT between the GS and the PS (P = 0.366), and both surgeons saw a decrease in DT over time. Using the CUSUM method, we see the peak of the plot at patient 9 for the PS and the peak of the plot at patient 5 for the GS, after which cumulative DT decreased. As robotic harvest of DIEP flaps becomes accepted, plastic surgeons who wish to incorporate it into their practice may achieve proficiency in their DT within 10 flap harvests and a similar DT compared with robotic-trained GSs.