Related Experiment Video
Updated: Apr 16, 2026
Utilizing a 3D Printed Laparoscopic Nissen Fundoplication Model to Shorten a Resident's Learning Curve
Published on: August 15, 2025
Learning curve evaluation using cumulative summation analysis-a clinical example of pediatric robot-assisted
Thomas P Cundy1, Nicholas E Gattas2, Alan D White2
1Department of Pediatric Surgery, Leeds General Infirmary, Leeds, United Kingdom; The Hamlyn Centre, Institute of Global Health Innovation, Imperial College London, London,United Kingdom.
Background:
The cumulative summation (CUSUM) method for learning curve analysis remains under-utilized in the surgical literature in general, and is described in only a small number of publications within the field of pediatric surgery. This study introduces the CUSUM analysis technique and applies it to evaluate the learning curve for pediatric robot-assisted laparoscopic pyeloplasty (RP).
Methods:
Clinical data were prospectively recorded for consecutive pediatric RP cases performed by a single-surgeon. CUSUM charts and tests were generated for set-up time, docking time, console time, operating time, total operating room time, and postoperative complications. Conversions and avoidable operating room delay were separately evaluated with respect to case experience. Comparisons between case experience and time-based outcomes were assessed using the Student's t-test and ANOVA for bi-phasic and multi-phasic learning curves respectively. Comparison between case experience and complication frequency was assessed using the Kruskal-Wallis test.
Results:
A total of 90 RP cases were evaluated. The learning curve transitioned beyond the learning phase at cases 10, 15, 42, 57, and 58 for set-up time, docking time, console time, operating time, and total operating room time respectively. All comparisons of mean operating times between the learning phase and subsequent phases were statistically significant (P=<0.001-0.01). No significant difference was observed between case experience and frequency of post-operative complications (P=0.125), although the CUSUM chart demonstrated a directional change in slope for the last 12 cases in which there were high proportions of re-do cases and patients <6 months of age.
Conclusions:
The CUSUM method has a valuable role for learning curve evaluation and outcome quality monitoring. In applying this statistical technique to the largest reported single surgeon series of pediatric RP, we demonstrate numerous distinctly shaped learning curves and well-defined learning phase transition points.
