Fellowship training influences learning curves for laparoscopic sleeve gastrectomy

Abstract

Background Laparoscopic sleeve gastrectomy (LSG) is the most commonly performed bariatric surgical procedure. Little is known about how surgeon training background influences the learning curve of this procedure. We examined operating times (OT), weight loss outcomes, and 30-day complications between surgeons with and without fellowship training in LSG. We hypothesize that post-residency training specific to LSG influences learning curves. Methods Surgeons from a single institution were split into two groups: those who had not completed fellowship training in LSG (NF, n = 3), and those who had completed LSG specific training in fellowship (SGF, n = 3). OTs, BMI changes at 1 year, and 30-day readmissions, reoperations, and complications were extracted for the first 100 LSG cases of each surgeon. Data were analyzed in bins of 20 cases. Comparisons were made between cohorts within a bin and between adjacent bins of the same surgeon cohort. Logistic regression analyses were performed of OT and weight loss outcomes. Results SGF surgeons showed no difference in OTs over their first 100 cases. NF surgeons had statistically significant increased OTs compared to SGF surgeons during their first 60 cases and progressively shortened OTs during that interval (109 min to 78 min, p < 0.001 for NF surgeons vs. 73 min to 69 min, SGF surgeons). NF surgeons had a significantly steeper slope for improvement in OT over case number. There was no correlation between case number and weight loss outcomes in either group, and no differences in 30-day outcomes between groups. Conclusion Surgeons who trained to perform LSG in fellowship demonstrate faster and consistent OR times on their initial independent LSG cases compared to surgeons who did not, with no correlation between case number and weight loss outcomes or safety profiles for either group. This suggests that learning curves for LSG are achieved during formal case-specific fellowship training.