PURPOSE: To understand the role of case complexity in the learning curve for robotic colorectal surgery. MATERIALS AND METHODS: Sixty-two patients who underwent robot-assisted colorectal surgery were retrospectively reviewed. Each case was assigned a category of complexity ranging from I to IV. Overall, groups and categories of segmental colectomy, rectopexy, and proctectomy for cancer were analyzed according to case volume. Forty-eight patients who underwent similar laparoscopic cases during the same period were also reviewed for comparison. RESULTS: Level I complexity cases were identified in 30% of the first 15 cases compared to 3% after the first 15 cases (P < .01). Level IV complexity cases were identified in 10% of the first 15 cases and 34% after 15 cases (P = .03). Mean operative time for the overall group was 426 minutes (range 178-766, standard deviation [SD] = 152) in the first 15 cases and 373 minutes (range 190-593, SD = 109) after more than 15 cases (P = NS). Mean operative time for rectal cancer procedures decreased from 518 minutes (range 425-752, SD = 88) to 410 minutes (range 220-593, SD = 98) after 15 cases (P = .02). Mean operative time for rectopexy decreased from 361 minutes (range 276-520, SD = 85) to 258 minutes (range 215-318, SD = 34) after 15 cases (P = .03). Overall complications were reduced after 15 cases (6.3%) compared with the first 15 cases (27%) (P = .04). When comparing laparoscopic and open cases, laparoscopic cases were associated with a significant shorter operative time (P = < .00001) as well as overall cost (P = < .00001). CONCLUSION: Complex robotic colorectal surgery can be performed early in the experience, with reduced operative time. Overall complications are reduced after 15 robotic cases. This study shows that improvement in robotic surgery operating time and surgical outcomes occur along with application of the technology to more difficult cases, not as a function of choosing less complex cases.
PURPOSE: To understand the role of case complexity in the learning curve for robotic colorectal surgery. MATERIALS AND METHODS: Sixty-two patients who underwent robot-assisted colorectal surgery were retrospectively reviewed. Each case was assigned a category of complexity ranging from I to IV. Overall, groups and categories of segmental colectomy, rectopexy, and proctectomy for cancer were analyzed according to case volume. Forty-eight patients who underwent similar laparoscopic cases during the same period were also reviewed for comparison. RESULTS: Level I complexity cases were identified in 30% of the first 15 cases compared to 3% after the first 15 cases (P < .01). Level IV complexity cases were identified in 10% of the first 15 cases and 34% after 15 cases (P = .03). Mean operative time for the overall group was 426 minutes (range 178-766, standard deviation [SD] = 152) in the first 15 cases and 373 minutes (range 190-593, SD = 109) after more than 15 cases (P = NS). Mean operative time for rectal cancer procedures decreased from 518 minutes (range 425-752, SD = 88) to 410 minutes (range 220-593, SD = 98) after 15 cases (P = .02). Mean operative time for rectopexy decreased from 361 minutes (range 276-520, SD = 85) to 258 minutes (range 215-318, SD = 34) after 15 cases (P = .03). Overall complications were reduced after 15 cases (6.3%) compared with the first 15 cases (27%) (P = .04). When comparing laparoscopic and open cases, laparoscopic cases were associated with a significant shorter operative time (P = < .00001) as well as overall cost (P = < .00001). CONCLUSION: Complex robotic colorectal surgery can be performed early in the experience, with reduced operative time. Overall complications are reduced after 15 robotic cases. This study shows that improvement in robotic surgery operating time and surgical outcomes occur along with application of the technology to more difficult cases, not as a function of choosing less complex cases.
Entities:
Keywords:
case complexity; colorectal surgery; learning curve; robotic surgery
Authors: Julie Flynn; Jose T Larach; Joseph C H Kong; Satish K Warrier; Alexander Heriot Journal: Int J Colorectal Dis Date: 2021-02-20 Impact factor: 2.571
Authors: Catharina Müller; Johannes Laengle; Stefan Riss; Michael Bergmann; Thomas Bachleitner-Hofmann Journal: Front Oncol Date: 2021-02-16 Impact factor: 6.244
Authors: Morcos A Awad; Jarrod Buzalewski; Cooper Anderson; James T Dove; Ashley Soloski; Nicole E Sharp; Bogdan Protyniak; Mohsen M Shabahang Journal: JSLS Date: 2020 Oct-Dec Impact factor: 2.172