The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The CAPRA‐S score: A straightforward tool for improved prediction of outcomes after radical prostatectomy
Open accessAbstract
BACKGROUND: The authors previously developed and validated the Cancer of the Prostate Risk Assessment (CAPRA) score to predict prostate cancer recurrence based on pretreatment clinical data. They aimed to develop a similar postsurgical score with improved accuracy via incorporation of pathologic data. METHODS: A total of 3837 prostatectomy patients in the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE™) national disease registry were analyzed. Cox regression was used to determine the predictive power of preoperative prostate‐specific antigen (PSA), pathologic Gleason score (pGS), surgical margins (SM), extracapsular extension (ECE), seminal vesicle invasion (SVI), and lymph node invasion (LNI). Points were assigned based on the relative weights of these variables in predicting recurrence. The new postsurgical score (CAPRA‐S) was tested and compared with a commonly cited nomogram with proportional hazards analysis, concordance (c) index, calibration plots, and decision‐curve analysis. RESULTS: Recurrence appeared in 16.8% of the men; actuarial progression‐free probability at 5 years was 78.0%. The CAPRA‐S was determined by adding up to 3 points for PSA, up to 3 points for pGS, 1 point each for ECE and LNI, and 2 points each for SM and SVI. The hazard ratio for each point increase in CAPRA‐S score was 1.54 (95% confidence interval, 1.49‐1.59), indicating a 2.4‐fold increase in risk for each 2‐point increase in score. The CAPRA‐S c‐index was 0.77, substantially higher than 0.66 for the pretreatment CAPRA score and comparable to 0.76 for the nomogram. The CAPRA‐S score performed better in both calibration and decision curve analyses. CONCLUSIONS: The CAPRA‐S offers good discriminatory accuracy, calibration, and ease of calculation for clinical and research settings. Cancer 2011;. © 2011 American Cancer Society.
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A Proportional Hazards Model for the Subdistribution of a Competing Risk
Fine, J. P., & Gray, R. J. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10474144 · Cited by 14,290
Jemal, A., Siegel, R., Xu, J., & Ward, E. (2010) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.20073 · Cited by 12,596
Cited by
Vale, C. L., Fisher, D., Kneebone, A., Parker, C., Pearse, M., Richaud, P., Sargos, P., Sydes, M. R., Brawley, C., Brihoum, M., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)31952-8 · Cited by 404
Mehra, R., Shi, Y., Udager, A. M., Prensner, J. R., Sahu, A., Iyer, M. K., Siddiqui, J., Cao, X., Wei, J., Jiang, H., et al. (2014) · Neoplasia · doi:10.1016/j.neo.2014.11.006 · Cited by 95
Metadata
- Authors
- Cooperberg, M. R., Hilton, J. F., & Carroll, P. R.
- Venue
- Cancer
- Publisher
- Wiley
- Published
- 2011-06-03
- Volume
- 117
- Issue
- 22
- Pages
- 5039-5046
- ISSN
- 0008-543X (ISSN-L)
- 1097-0142
- OpenAlex
- W2041120035
- Cited by
- 457
- References
- 2
- FWCI
- 16.83
- Type
- journal article
- Language
- en
- Open access
- bronze
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