PD6-12 DIMINISHING RETURNS OF ROBOTIC DIFFUSION: COMPLICATIONS FOLLOWING ROBOT-ASSISTED RADICAL PROSTATECTOMY
Jesse D. Sammon, Firas Abdollah, Dane Klett, Daniel Pucheril, Quoc‐Dien Trinh, Mani Menon
- Year
- 2015
- Citations
- 3
Abstract
You have accessJournal of UrologyProstate Cancer: Epidemiology & Natural History II1 Apr 2015PD6-12 DIMINISHING RETURNS OF ROBOTIC DIFFUSION: COMPLICATIONS FOLLOWING ROBOT-ASSISTED RADICAL PROSTATECTOMY Jesse Sammon, Firas Abdollah, Dane Klett, Daniel Pucheril, Akshay Sood, Quoc-Dien Trinh, and Mani Menon Jesse SammonJesse Sammon More articles by this author , Firas AbdollahFiras Abdollah More articles by this author , Dane KlettDane Klett More articles by this author , Daniel PucherilDaniel Pucheril More articles by this author , Akshay SoodAkshay Sood More articles by this author , Quoc-Dien TrinhQuoc-Dien Trinh More articles by this author , and Mani MenonMani Menon More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.807AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES In the United States, robotic-assisted radical prostatectomy (RARP) has supplanted open radical retropubic prostatectomy (ORP) as the most common approach for the operative management of prostate cancer. Evidence suggests patients are pursuing robotic surgery due to its perceived benefits without fully considering the implications of hospital and provider volume characteristics. Outcomes at high volume centers with high volume surgeons, are exceedingly difficult to generalize to the medical community as a whole. Therefore, we examine the extent of robotic diffusion for RARP, and explore the effects of wider robotic adoption on prostatectomy complication rates. METHODS Patients undergoing RARP for prostate cancer, 2009–2011, were extracted from the Nationwide Inpatient Sample (NIS). Each hospital volume quartile represents the range of annual hospital volume of one-fourth of patients undergoing RARP (Very-low: 1–54, Low: 55–102, High: 103–257, Very High: 258–869). Occurrence of complications was included. The independent effect of hospital volume quartile was assessed with ordinal logistic regression fitted with generalized estimating equations. Adjustment was made for age, race, comorbidity, and insurance status, as well as hospital region, location, bed size and academic status. RESULTS The number of sampled hospitals performing RARP remained stable over the study period. Median hospital volume fell sharply after 2009 (Fig. 1). Patients treated at very-low volume institutions experienced a complication rate of 14.2%, while those treated at very-high volume institutions experienced a complication rate of 7.5%. In multivariable analyses, patients treated at very-high volume hospitals were half as likely to experience a complication as compared to those treated at very-low volume hospitals (OR: 0.546; 95% CI: 0.393-0.758). CONCLUSIONS The migration of patients away from very-high volume institutions likely reflects the effect of current pay-for performance models in U.S. healthcare, in which institutions that have invested millions of dollars in robotic technology are disincentivized to refer patients to higher volume institutions. This perverse disincentive is likely jeopardizing patient care, and a renewed focus on the benefits of centralization of care is warranted. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e150 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jesse Sammon More articles by this author Firas Abdollah More articles by this author Dane Klett More articles by this author Daniel Pucheril More articles by this author Akshay Sood More articles by this author Quoc-Dien Trinh More articles by this author Mani Menon More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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