TY - JOUR
T1 - Development of a patient and institutional-based model for estimation of operative times for robot-assisted radical cystectomy
T2 - results from the International Robotic Cystectomy Consortium
AU - Hussein, Ahmed A.
AU - May, Paul R.
AU - Ahmed, Youssef E.
AU - Saar, Matthias
AU - Wijburg, Carl J.
AU - Richstone, Lee
AU - Wagner, Andrew
AU - Wilson, Timothy
AU - Yuh, Bertram
AU - Redorta, Joan P.
AU - Dasgupta, Prokar
AU - Kawa, Omar
AU - Khan, Mohammad S.
AU - Menon, Mani
AU - Peabody, James O.
AU - Hosseini, Abolfazl
AU - Gaboardi, Franco
AU - Pini, Giovannalberto
AU - Schanne, Francis
AU - Mottrie, Alexandre
AU - Rha, Koon Ho
AU - Hemal, Ashok
AU - Stockle, Michael
AU - Kelly, John
AU - Tan, Wei S.
AU - Maatman, Thomas J.
AU - Poulakis, Vassilis
AU - Kaouk, Jihad
AU - Canda, Abdullah E.
AU - Balbay, Mevlana D.
AU - Wiklund, Peter
AU - Guru, Khurshid A.
N1 - Funding Information:
Jihad Kaouk, speaker for Healthtronics and device testing consultant for Intuitive Surgical. Carl J. Wijburg, Proctor for Intuitive Surgical. Alexandre Mottrie, Proctor for Intuitive Surgical. Peter Wiklund, Research grant from Intuitive Surgical.
Funding Information:
Vattikuti Foundation Collective Quality Initiative and Roswell Park Cancer Institute Alliance Foundation.
Publisher Copyright:
© 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd
PY - 2017/11
Y1 - 2017/11
N2 - Objectives: To design a methodology to predict operative times for robot-assisted radical cystectomy (RARC) based on variation in institutional, patient, and disease characteristics to help in operating room scheduling and quality control. Patients and Methods: The model included preoperative variables and therefore can be used for prediction of surgical times: institutional volume, age, gender, body mass index, American Society of Anesthesiologists score, history of prior surgery and radiation, clinical stage, neoadjuvant chemotherapy, type, technique of diversion, and the extent of lymph node dissection. A conditional inference tree method was used to fit a binary decision tree predicting operative time. Permutation tests were performed to determine the variables having the strongest association with surgical time. The data were split at the value of this variable resulting in the largest difference in means for the surgical time across the split. This process was repeated recursively on the resultant data sets until the permutation tests showed no significant association with operative time. Results: In all, 2 134 procedures were included. The variable most strongly associated with surgical time was type of diversion, with ileal conduits being 70 min shorter (P < 0.001). Amongst patients who received neobladders, the type of lymph node dissection was also strongly associated with surgical time. Amongst ileal conduit patients, institutional surgeon volume (>66 RARCs) was important, with those with a higher volume being 55 min shorter (P < 0.001). The regression tree output was in the form of box plots that show the median and ranges of surgical times according to the patient, disease, and institutional characteristics. Conclusion: We developed a method to estimate operative times for RARC based on patient, disease, and institutional metrics that can help operating room scheduling for RARC.
AB - Objectives: To design a methodology to predict operative times for robot-assisted radical cystectomy (RARC) based on variation in institutional, patient, and disease characteristics to help in operating room scheduling and quality control. Patients and Methods: The model included preoperative variables and therefore can be used for prediction of surgical times: institutional volume, age, gender, body mass index, American Society of Anesthesiologists score, history of prior surgery and radiation, clinical stage, neoadjuvant chemotherapy, type, technique of diversion, and the extent of lymph node dissection. A conditional inference tree method was used to fit a binary decision tree predicting operative time. Permutation tests were performed to determine the variables having the strongest association with surgical time. The data were split at the value of this variable resulting in the largest difference in means for the surgical time across the split. This process was repeated recursively on the resultant data sets until the permutation tests showed no significant association with operative time. Results: In all, 2 134 procedures were included. The variable most strongly associated with surgical time was type of diversion, with ileal conduits being 70 min shorter (P < 0.001). Amongst patients who received neobladders, the type of lymph node dissection was also strongly associated with surgical time. Amongst ileal conduit patients, institutional surgeon volume (>66 RARCs) was important, with those with a higher volume being 55 min shorter (P < 0.001). The regression tree output was in the form of box plots that show the median and ranges of surgical times according to the patient, disease, and institutional characteristics. Conclusion: We developed a method to estimate operative times for RARC based on patient, disease, and institutional metrics that can help operating room scheduling for RARC.
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U2 - 10.1111/bju.13934
DO - 10.1111/bju.13934
M3 - Article
C2 - 28620985
AN - SCOPUS:85023643407
SN - 1464-4096
VL - 120
SP - 695
EP - 701
JO - BJU International
JF - BJU International
IS - 5
ER -