Literature DB >> 17868719

Validity of the CAPRA score to predict biochemical recurrence-free survival after radical prostatectomy. Results from a european multicenter survey of 1,296 patients.

Matthias May1, Nina Knoll, Michael Siegsmund, Dirk Fahlenkamp, Horst Vogler, Bernd Hoschke, Oliver Gralla.   

Abstract

PURPOSE: The CAPRA (Cancer of the Prostate Risk Assessment) score from the University of California, San Francisco provides a new statistical model to predict recurrence-free survival and pathological tumor stage after radical prostatectomy. It was originally developed using data from the CaPSURE (Cancer of the Prostate Strategic Urologic Research Endeavor) registry. To calculate the score, which ranges from 0 to 10, 5 clinical variables are needed, ie prostate specific antigen, Gleason sum, clinical tumor grade, percentage of positive biopsies and patient age. To date, the only external validation of the CAPRA score has been conducted using the SEARCH (Shared Equal Access Regional Cancer Hospital) database. The present study uses a German database to contribute to existing validation work and to test transferability of the CAPRA score to a sample that differs fundamentally from the SEARCH sample in terms of clinical features.
MATERIALS AND METHODS: Data from 1,296 German patients after radical prostatectomy were used for validation. Mean followup was 56.5 (+/-35.4) months. Accuracy of prediction of recurrence-free survival and pathological tumor stage with the CAPRA score was analyzed using Kaplan-Meier analysis, proportional hazards regression, logistic regression and graphic representation.
RESULTS: For the external validation of the CAPRA score, the underlying clinical variables of our study group were unfavorable compared to the original cohort from the CaPSURE data set. The recurrence-free survival rate decreased after 3 and 5 years from 100% to 97%, respectively, in the CAPRA score 0 to 1 group, and from 44% to 31%, respectively, in the CAPRA score of 7 or higher group. The hazard ratios of a biochemical recurrence per 1-group increase were 1.50 (95% CI 1.43-1.56) for the CAPRA sum score, 1.62 (95% CI 1.53-1.71) for the 7-group CAPRA score and 3.52 (95% CI 3.00-4.12) for the 3-group CAPRA score. Concordance indices between 0.78 and 0.81 suggested good predictive accuracy. Of the 5 CAPRA constituents 4 independently predicted recurrence-free survival, ie prostate specific antigen, Gleason sum, cT stage and percent of positive biopsies. Positive margins occurred in 13.1% of patients with a CAPRA score of 0 to 1 vs 62% of patients with a score of 7 to 10 (p <0.001). Organ confined tumors were present in 97.7% of patients with a CAPRA score of 0 to 1 vs 19.3% of those with a score of 7 to 10 (p <0.001).
CONCLUSIONS: Despite different clinical features in the present patient cohort and the CaPSURE data set, the accuracy of the CAPRA nomogram in predicting recurrence-free survival was high. These results underscore the effectiveness and the clinical applicability of the CAPRA score which, in addition to patient counseling, may also be used for risk stratification in clinical studies.

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Year:  2007        PMID: 17868719     DOI: 10.1016/j.juro.2007.07.043

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  29 in total

Review 1.  Predictive and prognostic models in radical prostatectomy candidates: a critical analysis of the literature.

Authors:  Giovanni Lughezzani; Alberto Briganti; Pierre I Karakiewicz; Michael W Kattan; Francesco Montorsi; Shahrokh F Shariat; Andrew J Vickers
Journal:  Eur Urol       Date:  2010-08-06       Impact factor: 20.096

2.  Prediction of biochemical recurrence following radical prostatectomy in men with prostate cancer by diffusion-weighted magnetic resonance imaging: initial results.

Authors:  Sung Yoon Park; Chan Kyo Kim; Byung Kwan Park; Hyun Moo Lee; Kyung Soo Lee
Journal:  Eur Radiol       Date:  2010-11-03       Impact factor: 5.315

3.  The CAPRA-S score: A straightforward tool for improved prediction of outcomes after radical prostatectomy.

Authors:  Matthew R Cooperberg; Joan F Hilton; Peter R Carroll
Journal:  Cancer       Date:  2011-06-03       Impact factor: 6.860

4.  Obesity at Diagnosis and Prostate Cancer Prognosis and Recurrence Risk Following Primary Treatment by Radical Prostatectomy.

Authors:  Crystal S Langlais; Janet E Cowan; John Neuhaus; Stacey A Kenfield; Erin L Van Blarigan; Jeanette M Broering; Matthew R Cooperberg; Peter Carroll; June M Chan
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2019-08-28       Impact factor: 4.254

Review 5.  The status of surgery in the management of high-risk prostate cancer.

Authors:  Christian Bach; Sailaja Pisipati; Datesh Daneshwar; Mark Wright; Edward Rowe; David Gillatt; Raj Persad; Anthony Koupparis
Journal:  Nat Rev Urol       Date:  2014-05-13       Impact factor: 14.432

Review 6.  Pharmacotherapeutic management of locally advanced prostate cancer: current status.

Authors:  Jarad M Martin; Stephane Supiot; Dominik R Berthold
Journal:  Drugs       Date:  2011-05-28       Impact factor: 9.546

Review 7.  Critical review of prostate cancer predictive tools.

Authors:  Shahrokh F Shariat; Michael W Kattan; Andrew J Vickers; Pierre I Karakiewicz; Peter T Scardino
Journal:  Future Oncol       Date:  2009-12       Impact factor: 3.404

8.  Prostate cancer risk assessment: choosing the sharpest tool in the shed.

Authors:  Matthew R Cooperberg
Journal:  Cancer       Date:  2008-12-01       Impact factor: 6.860

Review 9.  Reporting performance of prognostic models in cancer: a review.

Authors:  Susan Mallett; Patrick Royston; Rachel Waters; Susan Dutton; Douglas G Altman
Journal:  BMC Med       Date:  2010-03-30       Impact factor: 8.775

10.  Multiple cores of Gleason score 6 correlate with favourable findings at radical prostatectomy.

Authors:  Carla L Ellis; Patrick C Walsh; Alan W Partin; Jonathan I Epstein
Journal:  BJU Int       Date:  2013-01-25       Impact factor: 5.588

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