Development of a patient and institutional-based model for estimation of operative times for robot-assisted radical cystectomy: results from the International Robotic Cystectomy Consortium

Ahmed A. Hussein, Paul R. May, Youssef E. Ahmed, Matthias Saar, Carl J. Wijburg, Lee Richstone, Andrew Wagner, Timothy Wilson, Bertram Yuh, Joan P. Redorta, Prokar Dasgupta, Omar Kawa, Mohammad S. Khan, Mani Menon, James O. Peabody, Abolfazl Hosseini, Franco Gaboardi, Giovannalberto Pini, Francis Schanne, Alexandre MottrieKoon Ho Rha, Ashok Hemal, Michael Stockle, John Kelly, Wei S. Tan, Thomas J. Maatman, Vassilis Poulakis, Jihad Kaouk, Abdullah E. Canda, Mevlana D. Balbay, Peter Wiklund, Khurshid A. Guru

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)695-701
Number of pages7
JournalBJU International
Volume120
Issue number5
DOIs
Publication statusPublished - 2017 Nov

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Cystectomy
Robotics
Operative Time
Operating Rooms
Decision Trees
Urinary Diversion
Lymph Node Excision
Quality Control
Body Mass Index
Radiation
Drug Therapy

All Science Journal Classification (ASJC) codes

  • Urology

Cite this

Hussein, Ahmed A. ; May, Paul R. ; Ahmed, Youssef E. ; Saar, Matthias ; Wijburg, Carl J. ; Richstone, Lee ; Wagner, Andrew ; Wilson, Timothy ; Yuh, Bertram ; Redorta, Joan P. ; Dasgupta, Prokar ; Kawa, Omar ; Khan, Mohammad S. ; Menon, Mani ; Peabody, James O. ; Hosseini, Abolfazl ; Gaboardi, Franco ; Pini, Giovannalberto ; Schanne, Francis ; Mottrie, Alexandre ; Rha, Koon Ho ; Hemal, Ashok ; Stockle, Michael ; Kelly, John ; Tan, Wei S. ; Maatman, Thomas J. ; Poulakis, Vassilis ; Kaouk, Jihad ; Canda, Abdullah E. ; Balbay, Mevlana D. ; Wiklund, Peter ; Guru, Khurshid A. / Development of a patient and institutional-based model for estimation of operative times for robot-assisted radical cystectomy : results from the International Robotic Cystectomy Consortium. In: BJU International. 2017 ; Vol. 120, No. 5. pp. 695-701.
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title = "Development of a patient and institutional-based model for estimation of operative times for robot-assisted radical cystectomy: results from the International Robotic Cystectomy Consortium",
abstract = "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.",
author = "Hussein, {Ahmed A.} and May, {Paul R.} and Ahmed, {Youssef E.} and Matthias Saar and Wijburg, {Carl J.} and Lee Richstone and Andrew Wagner and Timothy Wilson and Bertram Yuh and Redorta, {Joan P.} and Prokar Dasgupta and Omar Kawa and Khan, {Mohammad S.} and Mani Menon and Peabody, {James O.} and Abolfazl Hosseini and Franco Gaboardi and Giovannalberto Pini and Francis Schanne and Alexandre Mottrie and Rha, {Koon Ho} and Ashok Hemal and Michael Stockle and John Kelly and Tan, {Wei S.} and Maatman, {Thomas J.} and Vassilis Poulakis and Jihad Kaouk and Canda, {Abdullah E.} and Balbay, {Mevlana D.} and Peter Wiklund and Guru, {Khurshid A.}",
year = "2017",
month = "11",
doi = "10.1111/bju.13934",
language = "English",
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pages = "695--701",
journal = "BJU International",
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Hussein, AA, May, PR, Ahmed, YE, Saar, M, Wijburg, CJ, Richstone, L, Wagner, A, Wilson, T, Yuh, B, Redorta, JP, Dasgupta, P, Kawa, O, Khan, MS, Menon, M, Peabody, JO, Hosseini, A, Gaboardi, F, Pini, G, Schanne, F, Mottrie, A, Rha, KH, Hemal, A, Stockle, M, Kelly, J, Tan, WS, Maatman, TJ, Poulakis, V, Kaouk, J, Canda, AE, Balbay, MD, Wiklund, P & Guru, KA 2017, 'Development of a patient and institutional-based model for estimation of operative times for robot-assisted radical cystectomy: results from the International Robotic Cystectomy Consortium', BJU International, vol. 120, no. 5, pp. 695-701. https://doi.org/10.1111/bju.13934

Development of a patient and institutional-based model for estimation of operative times for robot-assisted radical cystectomy : results from the International Robotic Cystectomy Consortium. / Hussein, Ahmed A.; May, Paul R.; Ahmed, Youssef E.; Saar, Matthias; Wijburg, Carl J.; Richstone, Lee; Wagner, Andrew; Wilson, Timothy; Yuh, Bertram; Redorta, Joan P.; Dasgupta, Prokar; Kawa, Omar; Khan, Mohammad S.; Menon, Mani; Peabody, James O.; Hosseini, Abolfazl; Gaboardi, Franco; Pini, Giovannalberto; Schanne, Francis; Mottrie, Alexandre; Rha, Koon Ho; Hemal, Ashok; Stockle, Michael; Kelly, John; Tan, Wei S.; Maatman, Thomas J.; Poulakis, Vassilis; Kaouk, Jihad; Canda, Abdullah E.; Balbay, Mevlana D.; Wiklund, Peter; Guru, Khurshid A.

In: BJU International, Vol. 120, No. 5, 11.2017, p. 695-701.

Research output: Contribution to journalArticle

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.

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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