OBJECTIVES: To develop a methodology for predicting operative times for robot-assisted radical prostatectomy (RARP) using preoperative patient, disease, procedural, and surgeon variables to facilitate operating room (OR) scheduling. METHODS: The model included preoperative metrics: body mass index (BMI), American Society of Anesthesiologists score, clinical stage, National Comprehensive Cancer Network risk, prostate weight, nerve-sparing status, extent and laterality of lymph node dissection, and operating surgeon (six surgeons were included in the study). A binary decision tree was fit using a conditional inference tree method to predict operative times. The variables most associated with operative time were determined using permutation tests. Data were split at the value of the variable that results in the largest difference in mean for surgical time across the split. This process was repeated recursively on the resultant data. RESULTS: A total of 1709 RARPs were included. The variable most strongly associated with operative time was the surgeon (surgeons 2 and 4-102 minutes shorter than surgeons 1, 3, 5, and 6, p < 0.001). Among surgeons 2 and 4, BMI had the strongest association with surgical time (p < 0.001). Among patients operated by surgeons 1, 3, 5, and 6, RARP time was again most strongly associated with the surgeon performing RARP. Surgeons 1, 3, and 6 were on average 76 minutes faster than surgeon 5 (p < 0.001). The regression tree output in the form of box plots showed operative time median and ranges according to patient, disease, procedural, and surgeon metrics. CONCLUSION: We developed a methodology that can predict operative times for RARP based on patient, disease and surgeon variables. This methodology can be utilized for quality control, facilitate OR scheduling, and maximize OR efficiency.
OBJECTIVES: To develop a methodology for predicting operative times for robot-assisted radical prostatectomy (RARP) using preoperative patient, disease, procedural, and surgeon variables to facilitate operating room (OR) scheduling. METHODS: The model included preoperative metrics: body mass index (BMI), American Society of Anesthesiologists score, clinical stage, National Comprehensive Cancer Network risk, prostate weight, nerve-sparing status, extent and laterality of lymph node dissection, and operating surgeon (six surgeons were included in the study). A binary decision tree was fit using a conditional inference tree method to predict operative times. The variables most associated with operative time were determined using permutation tests. Data were split at the value of the variable that results in the largest difference in mean for surgical time across the split. This process was repeated recursively on the resultant data. RESULTS: A total of 1709 RARPs were included. The variable most strongly associated with operative time was the surgeon (surgeons 2 and 4-102 minutes shorter than surgeons 1, 3, 5, and 6, p < 0.001). Among surgeons 2 and 4, BMI had the strongest association with surgical time (p < 0.001). Among patients operated by surgeons 1, 3, 5, and 6, RARP time was again most strongly associated with the surgeon performing RARP. Surgeons 1, 3, and 6 were on average 76 minutes faster than surgeon 5 (p < 0.001). The regression tree output in the form of box plots showed operative time median and ranges according to patient, disease, procedural, and surgeon metrics. CONCLUSION: We developed a methodology that can predict operative times for RARP based on patient, disease and surgeon variables. This methodology can be utilized for quality control, facilitate OR scheduling, and maximize OR efficiency.
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