Pudendal Nerve Terminal Motor Latency Testing: Assessing The Educational Learning Curve

Abstract

PURPOSE: Pudendal nerve terminal motor latency testing is useful as a diagnostic tool in fecal incontinence. It has also been used as a predictive factor in sphincteroplasty repairs. The technique is seldom taught and mastered in colorectal training programs. The purpose of this study was to assess a learning curve for teaching this procedure. METHODS: The student was a formally trained colorectal surgeon with no pudendal nerve terminal motor latency experience; the instructor has performed more than 3,000 pudendal nerve terminal motor latency studies. Fifty consecutive patients had manometry and pudendal nerve terminal motor latency testing. Both the student and instructor performed pudendal nerve terminal motor latency in a sequential fashion. Variables collected included pudendal nerve terminal motor latency, completion of test, time to complete test, and accuracy of the test. Variables were analyzed with paired t-test and chi-squared analysis. RESULTS: The study group included 41 female and 9 male patients. The average age of the patients was 53 years. Bowel complaints included constipation in 16 and incontinence in 34 patients. Data were analyzed in their entirety and at ten-patient intervals. The student tended to record longer latencies (P < 0.001). This led to false-positive rates of 23 and 21 percent, respectively, for left and right pudendal nerve terminal motor latency. On average, the time to complete the procedure was three times longer for the student than for the instructor (P < 0.001). At ten-patient intervals, the pudendal nerve terminal motor latency difference between the two groups disappeared at the 41-to-50–patient mark. CONCLUSION: Given the proper learning environment, pudendal nerve terminal motor latency testing can be mastered in a relatively short period. On the basis of this study, our estimation is 40 patients are required for a student to master this technique. The novice examiner can expect false-positive results early in the learning curve, and these should be validated accordingly.