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• W2001128566 abstract "No AccessJournal of UrologyAdult Urology: Outcomes/Epidemiology/Socioeconomics1 Oct 2005MULTI-INSTITUTIONAL VALIDATION STUDY OF NEURAL NETWORKS TO PREDICT DURATION OF STAY AFTER LAPAROSCOPIC RADICAL/SIMPLE OR PARTIAL NEPHRECTOMY SIJO J. PAREKATTIL, INDERBIR S. GILL, ERIK P. CASTLE, SCOTT V. BURGESS, MELISSA M. WALLS, RAJU THOMAS, UDAYA KUMAR, JODY A. PURIFOY, CHRISTOPHER S. NG, YOUNG KANG, GERHARD J. FUCHS, ERIK S. WEISE, HOWARD N. WINFIELD, COSTAS LALLAS, and PAUL E. ANDREWS SIJO J. PAREKATTILSIJO J. PAREKATTIL More articles by this author , INDERBIR S. GILLINDERBIR S. GILL More articles by this author , ERIK P. CASTLEERIK P. CASTLE More articles by this author , SCOTT V. BURGESSSCOTT V. BURGESS More articles by this author , MELISSA M. WALLSMELISSA M. WALLS More articles by this author , RAJU THOMASRAJU THOMAS More articles by this author , UDAYA KUMARUDAYA KUMAR More articles by this author , JODY A. PURIFOYJODY A. PURIFOY More articles by this author , CHRISTOPHER S. NGCHRISTOPHER S. NG More articles by this author , YOUNG KANGYOUNG KANG More articles by this author , GERHARD J. FUCHSGERHARD J. FUCHS More articles by this author , ERIK S. WEISEERIK S. WEISE More articles by this author , HOWARD N. WINFIELDHOWARD N. WINFIELD More articles by this author , COSTAS LALLASCOSTAS LALLAS More articles by this author , and PAUL E. ANDREWSPAUL E. ANDREWS More articles by this author View All Author Informationhttps://doi.org/10.1097/01.ju.0000173921.67597.e8AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract Purpose: We developed models to predict post-laparoscopic radical or simple nephrectomy (LapNx) and post-laparoscopic partial nephrectomy (LapPNx) hospital duration of stay (DOS). Materials and Methods: We performed a retrospective review (design group) of all 726 patients (July 1997 to April 2004) who underwent LapNx or LapPNx at the Cleveland Clinic Foundation (CCF). Preoperative findings were recorded. Neural network algorithms were designed to predict the DOS before surgery. The models were then tested on a separate 252 patients from 6 different institutions, namely Tulane University Medical School, University of Arkansas for Medical Sciences, Cedars-Sinai Medical Center, University of Iowa, Mayo Clinic at Scottsdale and CCF. Results: In the CCF design groups, the LapNx model accuracy was 73% to 74% and the LapPNx model 73% to 83%. Overall accuracy in the test groups at all 6 institutions was 72% (area under ROC 0.6 to 0.7) for the LapNx model and 52% to 81% (ROC 0.5 to 0.7) for the LapPNx model. Conclusions: The LapNx model provides 72% accuracy in predicting the DOS at all 6 institutions. The LapPNx model provided fair accuracy only at CCF and Tulane University Medical School. These models may streamline the delivery of care and continued testing will allow for further refinement. References 1 : Financial analysis of open versus laparoscopic radical nephrectomy and nephroureterectomy. J Urol2002; 167: 1757. Link, Google Scholar 2 : Financial analysis of needlescopic versus open adrenalectomy. J Urol1999; 162: 1264. Link, Google Scholar 3 : Cost comparison for laparoscopic nephrectomy and open nephrectomy: analysis of individual parameters. Urology2002; 59: 821. Google Scholar 4 : Cost containment in urology. Urology1995; 46: 14. Google Scholar 5 : Clinical care pathway for the management of ureteroneocystostomy in the pediatric urology population. J Urol1997; 158: 1221. Link, Google Scholar 6 : Artificial neural networks: opening the black box. Cancer2001; 91: 1615. Google Scholar 7 : On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med2000; 19: 541. Google Scholar 8 : A neurocomputational model for prostate carcinoma detection. Cancer2003; 98: 1849. Google Scholar 9 : Artificial neural networks in pediatric urology: prediction of sonographic outcome following pyeloplasty. J Urol1998; 160: 980. Link, Google Scholar 10 : Computational tools for the modern andrologist. J Androl1996; 17: 462. Google Scholar 11 : Epidemiology, Biostatistics, and Preventive Medicine.. Philadelphia: W. B. Saunders Co.1996: 172. chapt. 113. Google Scholar 12 : Using and Understanding Medical Statistics. Basel: Karger1996: 136. chapt. 111. Google Scholar From the Cleveland Clinic Foundation, Cleveland, Ohio, Tulane University Medical School, New Orleans, Louisiana, University of Arkansas for Medical Sciences, Little Rock, Arkansas, Cedars-Sinai Medical Center, Los Angeles, California, University of Iowa, Iowa City, Iowa, and Mayo Clinic, Scottsdale, Arizona© 2005 by American Urological Association, Inc.FiguresReferencesRelatedDetails Volume 174Issue 4 Part 1October 2005Page: 1380-1384 Advertisement Copyright & Permissions© 2005 by American Urological Association, Inc.Keywordsprognosisneural networkslaparoscopynephrectomyMetricsAuthor Information SIJO J. PAREKATTIL More articles by this author INDERBIR S. GILL More articles by this author ERIK P. CASTLE More articles by this author SCOTT V. BURGESS More articles by this author MELISSA M. WALLS More articles by this author RAJU THOMAS More articles by this author UDAYA KUMAR More articles by this author JODY A. PURIFOY More articles by this author CHRISTOPHER S. NG More articles by this author YOUNG KANG More articles by this author GERHARD J. FUCHS More articles by this author ERIK S. WEISE More articles by this author HOWARD N. WINFIELD More articles by this author COSTAS LALLAS More articles by this author PAUL E. ANDREWS More articles by this author Expand All Advertisement PDF downloadLoading ..." @default.
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