BACKGROUND: Initial treatment options for low-risk clinically localized prostate cancer (PCa) include radical prostatectomy (RP) or observation. OBJECTIVE: To examine cancer-specific mortality (CSM) after accounting for other-cause mortality (OCM) in PCa patients treated with either RP or observation. DESIGN, SETTING, AND PARTICIPANTS: Using the Surveillance Epidemiology and End Results Medicare-linked database, a total of 44 694 patients ≥65 yr with localized (T1/2) PCa were identified (1992-2005). INTERVENTION: RP and observation. MEASUREMENTS: Propensity-score matching was used to adjust for potential selection biases associated with treatment type. The matched cohort was randomly divided into the development and validation sets. Competing-risks regression models were fitted and a competing-risks nomogram was developed and externally validated. RESULTS AND LIMITATIONS: Overall, 22,244 (49.8%) patients were treated with RP versus 22450 (50.2%) with observation. Propensity score-matched analyses derived 11,669 matched pairs. In the development cohort, the 10-yr CSM rate was 2.8% (2.3-3.5%) for RP versus 5.8% (5.0-6.6%) for observation (absolute risk reduction: 3.0%; relative risk reduction: 0.5%; p<0.001). In multivariable analyses, the CSM hazard ratio for RP was 0.48 (0.38-0.59) relative to observation (p<0.001). The competing-risks nomogram discrimination was 73% and 69% for prediction of CSM and OCM, respectively, in external validation. The nature of observational data may have introduced a selection bias. CONCLUSIONS: On average RP reduces the risk of CSM by half in patients aged ≥65 yr, relative to observation. The individualized protective effect of RP relative to observation may be quantified with our nomogram. Crown
BACKGROUND: Initial treatment options for low-risk clinically localized prostate cancer (PCa) include radical prostatectomy (RP) or observation. OBJECTIVE: To examine cancer-specific mortality (CSM) after accounting for other-cause mortality (OCM) in PCa patients treated with either RP or observation. DESIGN, SETTING, AND PARTICIPANTS: Using the Surveillance Epidemiology and End Results Medicare-linked database, a total of 44 694 patients ≥65 yr with localized (T1/2) PCa were identified (1992-2005). INTERVENTION: RP and observation. MEASUREMENTS: Propensity-score matching was used to adjust for potential selection biases associated with treatment type. The matched cohort was randomly divided into the development and validation sets. Competing-risks regression models were fitted and a competing-risks nomogram was developed and externally validated. RESULTS AND LIMITATIONS: Overall, 22,244 (49.8%) patients were treated with RP versus 22450 (50.2%) with observation. Propensity score-matched analyses derived 11,669 matched pairs. In the development cohort, the 10-yr CSM rate was 2.8% (2.3-3.5%) for RP versus 5.8% (5.0-6.6%) for observation (absolute risk reduction: 3.0%; relative risk reduction: 0.5%; p<0.001). In multivariable analyses, the CSM hazard ratio for RP was 0.48 (0.38-0.59) relative to observation (p<0.001). The competing-risks nomogram discrimination was 73% and 69% for prediction of CSM and OCM, respectively, in external validation. The nature of observational data may have introduced a selection bias. CONCLUSIONS: On average RP reduces the risk of CSM by half in patients aged ≥65 yr, relative to observation. The individualized protective effect of RP relative to observation may be quantified with our nomogram. Crown
Authors: Wael Y Khoder; Raphaela Waidelich; Michael Seitz; Armin J Becker; Alexander Buchner; Stefan Trittschler; Christian G Stief Journal: World J Urol Date: 2014-04-22 Impact factor: 4.226
Authors: Robin Wm Vernooij; Michelle Lancee; Anne Cleves; Philipp Dahm; Chris H Bangma; Katja Kh Aben Journal: Cochrane Database Syst Rev Date: 2020-06-04
Authors: Jens Hansen; Giorgio Gandaglia; Marco Bianchi; Maxine Sun; Michael Rink; Zhe Tian; Malek Meskawi; Quoc-Dien Trinh; Shahrokh F Shariat; Paul Perrotte; Felix K-H Chun; Markus Graefen; Pierre I Karakiewicz Journal: Can Urol Assoc J Date: 2014 Jan-Feb Impact factor: 1.862
Authors: A Sivaraman; G Ordaz Jurado; X Cathelineau; Eric Barret; P Dell'Oglio; S Joniau; M Bianchi; A Briganti; M Spahn; P Bastian; J Chun; P Chlosta; P Gontero; M Graefen; R Jeffrey Karnes; G Marchioro; B Tombal; L Tosco; H Henk van der Poel; R Sanchez-Salas Journal: World J Urol Date: 2016-02-20 Impact factor: 4.226
Authors: Christopher Sweeney; Mari Nakabayashi; Meredith Regan; Wanling Xie; Julia Hayes; Nancy Keating; Suhui Li; Tomas Philipson; Marc Buyse; Susan Halabi; Philip Kantoff; A Oliver Sartor; Howard Soule; Brandon Mahal Journal: J Natl Cancer Inst Date: 2015-09-25 Impact factor: 13.506
Authors: Michael A Liss; John Billimek; Kathryn Osann; Jane Cho; Ross Moskowitz; Adam Kaplan; Richard J Szabo; Sherrie H Kaplan; Sheldon Greenfield; Atreya Dash Journal: Cancer Date: 2013-04-25 Impact factor: 6.860