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Cumulative summation analysis of learning curve for robotic-assisted hiatal hernia repairs
Emily L Lin1, Agustin Sibona2, Jiahao Peng3
1Department of General Surgery, Loma Linda University Health, Loma Linda, CA, USA. elin@llu.edu.
Background:
Robotic-assisted laparoscopic surgery (RALS) is evolving as an important surgical approach in the field of general surgery. We aimed to evaluate the learning curve for RALS procedures involving repair of hiatal hernias.
Methods:
A series of robotic-assisted hiatal hernia (HH) repairs were performed between 2013 and 2017 by a surgeon at a single institution. Data were entered into a retrospective database. Patient demographics and intraoperative parameters including console time (CT), surgery time (ST), and total operative time (OT) were examined and abstracted for learning curve analysis using the cumulative sum (CUSUM) method. Assessment of perioperative and post-operative outcomes were calculated using descriptive statistics.
Results:
The average age of the patients was 57.4 years, average BMI was 29.9 kg/m2, median American Society of Anesthesiologists (ASA) classification was 2, and average Charlson Comorbidity Index (CCI) score was 2.8. The series had a mean CT of 132.6 min, mean ST of 145.1 min, and mean OT of 197.4 min. The CUSUM learning curve for CT was best approximated as a third-order polynomial consisting of three unique phases: the initial training phase (case 1-40), the improvement phase (case 41-85), and the mastery phase (case 86 onwards). There was no significant difference in perioperative complications between the phases. Short-term clinical outcomes were comparable with national standards and did not correlate significantly with operative experience.
Conclusions:
The three phases identified with CUSUM analysis represented characteristic stages of the learning curve for robotic hiatal hernia procedures. Our data suggest the training phase is achieved after 40 cases and a high level of mastery is achieved after approximately 85 cases. Thus, the CUSUM method serves as a useful tool for objectively evaluating practical skills for surgeons and can ultimately help establish milestones that assess surgical competency during robotic surgery training.
