Comparison of success rates, learning curves, and inter-subject performance variability of robot-assisted and manual ultrasound-guided nerve block needle guidance in simulation.
BACKGROUND
This study focuses on a recently developed robotic nerve block system and its impact on learning regional anaesthesia skills. We compared success rates, learning curves, performance times, and inter-subject performance variability of robot-assisted vs manual ultrasound (US)-guided nerve block needle guidance. The hypothesis of this study is that robot assistance will result in faster skill acquisition than manual needle guidance.


METHODS
Five co-authors with different experience with nerve blocks and the robotic system performed both manual and robot-assisted, US-guided nerve blocks on two different nerves of a nerve phantom. Ten trials were performed for each of the four procedures. Time taken to move from a shared starting position till the needle was inserted into the target nerve was defined as the performance time. A successful block was defined as the insertion of the needle into the target nerve. Average performance times were compared using analysis of variance. P<0.05 was considered significant. Data presented as mean (standard deviation).


RESULTS
All blocks were successful. There were significant differences in performance times between co-authors to perform the manual blocks, either superficial (P=0.001) or profound (P=0.0001); no statistical difference between co-authors was noted for the robot-assisted blocks. Linear regression indicated that the average decrease in time between consecutive trials for robot-assisted blocks of 1.8 (1.6) s was significantly (P=0.007) greater than the decrease for manual blocks of 0.3 (0.3) s.


CONCLUSIONS
Robot assistance of nerve blocks allows for faster learning of needle guidance over manual positioning and reduces inter-subject performance variability.
Division of Experimental Surgery, McGill University, Montreal, Quebec, Canada.
J
J
Morse
Department of Anaesthesia, University of Pisa, Pisa, Italy.
N
N
Terrasini
Division of Experimental Surgery, McGill University, Montreal, Quebec, Canada.
M
M
Wehbe
Division of Experimental Surgery, McGill University, Montreal, Quebec, Canada.
C
C
Philippona
Department of Anaesthesia, University of Pisa, Pisa, Italy.
C
C
Zaouter
Division of Experimental Surgery, McGill University, Montreal, Quebec, Canada Arnold and Blema Steinberg McGill Medical Simulation Centre, McGill University, Montreal, Quebec, Canada.
S
S
Cyr
Division of Experimental Surgery, McGill University, Montreal, Quebec, Canada Arnold and Blema Steinberg McGill Medical Simulation Centre, McGill University, Montreal, Quebec, Canada thomas.hemmerling@mcgill.ca.
T M
TM
Hemmerling
D000779:Anesthetics, Local / Q000008:administration & dosage; D002983:Clinical Competence / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003194:Computer-Assisted Instruction; D006801:Humans; D059032:Learning Curve; D009407:Nerve Block / Q000379:methods* / Q000592:standards; D019047:Phantoms, Imaging; D012371:Robotics / Q000379:methods* / Q000592:standards; D018084:Ultrasonography, Interventional / Q000379:methods*
D000779:Anesthetics, Local
learning curves; medical robotics; regional anaesthesia; robot assistance; robotic anaesthesia; simulation