title: Predicting Prostate Cancer Recurrence After Radical Prostatectomy.
abstract: Prostate cancer prognosis is variable, and management decisions involve balancing patients' risks of recurrence and recurrence-free death. Moreover, the roles of body mass index (BMI) and race in risk of recurrence are controversial [1,2]. To address these issues, we developed and cross-validated RAPS (Risks After Prostate Surgery), a personal prediction model for biochemical recurrence (BCR) within 10 years of radical prostatectomy (RP) that includes BMI and race as possible predictors, and recurrence-free death as a competing risk.
  
  
  
  RAPS uses a patient's risk factors at surgery to assign him a recurrence probability based on statistical learning methods applied to a cohort of 1,276 patients undergoing RP at the University of Pennsylvania. We compared the performance of RAPS to that of an existing model with respect to calibration (by comparing observed and predicted outcomes), and discrimination (using the area under the receiver operating characteristic curve (AUC)).
  
  
  
  RAPS' cross-validated BCR predictions provided better calibration than those of an existing model that underestimated patients' risks. Discrimination was similar for the two models, with BCR AUCs of 0.793, 95% confidence interval (0.766-0.820) for RAPS, and 0.780 (0.745-0.815) for the existing model. RAPS' most important BCR predictors were tumor grade, preoperative prostate-specific antigen (PSA) level and BMI; race was less important [3]. RAPS' predictions can be obtained online at https://predict.shinyapps.io/raps.
  
  
  
  RAPS' cross-validated BCR predictions were better calibrated than those of an existing model, and BMI information contributed substantially to these predictions. RAPS predictions for recurrence-free death were limited by lack of co-morbidity data; however the model provides a simple framework for extension to include such data. Its use and extension should facilitate decision strategies for post-RP prostate cancer management. Prostate 77:291-298, 2017. © 2016 Wiley Periodicals, Inc.
authors: Precision Health Economics, Austin, Texas.
authors: Abra
authors: A
authors: Jeffers
authors: Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, California.
authors: Vanessa
authors: V
authors: Sochat
authors: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio.
authors: Michael W
authors: MW
authors: Kattan
authors: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio.
authors: Changhong
authors: C
authors: Yu
authors: Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California.
authors: Erin
authors: E
authors: Melcon
authors: Department of Urology, University of Pennsylvania, Philadelphia, Pennsylvania.
authors: Kosj
authors: K
authors: Yamoah
authors: Department of Epidemiology, Harvard University School of Public Health, Boston, Massachusetts.
authors: Timothy R
authors: TR
authors: Rebbeck
authors: Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California.
authors: Alice S
authors: AS
authors: Whittemore
mesh_terms: D000368:Aged; D015331:Cohort Studies; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000097:blood / Q000175:diagnosis*; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000097:blood; D011468:Prostatectomy / Q000639:trends*; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000601:surgery*; D012372:ROC Curve
chemical_list: D017430:Prostate-Specific Antigen
keywords: body mass index; calibration discrimination; prediction model; prostate cancer recurrence