Arthroscopic rotator cuff repair: The learning curve
Authors: Dan Guttmann, Robert D. Graham, Megan J. MacLennan, James H. Lubowitz
References: Arthroscopy 2005 Apr;21(4):394-400.
Purpose: The purpose of this study was to answer the question: How many cases are required for a surgeon to become proficient in performing arthroscopic rotator cuff repair? We hypothesize that as surgical experienced is gained, learning can be quantitatively shown by a significant decrease in operative time. Type of Study: Prospective case series. Methods: Rotator cuff repair time (RCRT) in minutes (as well as other time components comprising total surgical time) was recorded for 100 consecutive patients having arthroscopic rotator cuff repair performed by a single surgeon beginning with his first case in private practice. Mean RCRTs for consecutive blocks of 10 cases were compared. Learning is graphically represented by plotting the RCRT by case number and generating a logarithmic trend curve. A best-fit linear equation (y = mx + b) allows comparison of the initial 10 cases with the subsequent 90 cases, where m, the slope, represents the rate of decrease in RCRT (learning). Results: Mean RCRT decreased significantly (P < .05) from the first block of 10 cases to the second block of 10 cases. There were no significant changes in mean RCRT when comparing other consecutive blocks of 10 cases. The slope of the line fitting the first block of 10 cases is −8.75; the slope (m) of the line fitting the subsequent 90 cases is −0.23. There is no significant difference in mean RCRT when cases are stratified by tear size. Conclusions: Graphic representation of RCRT by case number generates a learning curve whereby learning is quantitatively shown as a significant decrease in operative time as surgical experience is gained. Clinical Relevance: Qualification of the learning curve for arthroscopic rotator cuff repair provides a guide for orthopaedic surgeons contemplating the expected time line for acquiring proficiency in this technique.
RCRT in minutes by case number. Individual data points are connected by best-fit linear trend lines for the first block of 10 cases and the subsequent block of 90 cases.