@article {145750, 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.}, journal = {BJU international}, volume = {120}, year = {2017}, note = {n/a}, month = {2017 Nov}, pages = {695-701}, 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.

}, keywords = {cystectomy, Humans, Models, Theoretical, Operative Time, Personnel Staffing and Scheduling, quality control, Retrospective Studies, Robotic Surgical Procedures}, issn = {1464-410X}, doi = {10.1111/bju.13934}, author = {Hussein, Ahmed A and May, Paul R and Ahmed, Youssef E and Saar, Matthias and Wijburg, Carl J and Richstone, Lee and Wagner, Andrew and Wilson, Timothy and Yuh, Bertram and Redorta, Joan P and Dasgupta, Prokar and Kawa, Omar and Khan, Mohammad S and Menon, Mani and Peabody, James O and Hosseini, Abolfazl and Gaboardi, Franco and Pini, Giovannalberto and Schanne, Francis and Mottrie, Alexandre and Rha, Koon-Ho and Hemal, Ashok and Stockle, Michael and Kelly, John and Tan, Wei S and Maatman, Thomas J and Poulakis, Vassilis and Kaouk, Jihad and Canda, Abdullah E and Balbay, Mevlana D and Wiklund, Peter and Guru, Khurshid A} }