title: Prediction of clinical progression after radical prostatectomy in a nationwide population-based cohort.
abstract: OBJECTIVE
  The aim of this study was to create a model for predicting progression-free survival after radical prostatectomy for localized prostate cancer.
  
  
  METHODS
  The risk of biochemical recurrence (BCR) was modelled in a cohort of 3452 men aged 70 years or younger who were primarily treated with radical prostatectomy after being diagnosed between 2003 and 2006 with localized prostate cancer [clinical stage T1c-T2, Gleason score 5-10, N0/NX, M0/MX, prostate-specific antigen (PSA) < 20 ng/ml]. The cohort was split into two: one cohort for model development (n = 3452) and one for validation (n = 1762). BCR was defined as two increasing PSA values of at least 0.2 ng/ml, initiation of secondary therapy, distant metastases or death from prostate cancer. Multivariable Cox proportional hazard regression was applied, predictive performance was assessed using the bootstrap resampling technique to calculate the c index, and calibration of the model was evaluated by comparing predicted and observed Kaplan-Meier 1 year BCR.
  
  
  RESULTS
  The overall 5 year progression-free survival was 83% after a median follow-up time of 6.8 years in the development cohort and 7.3 years in the validation cohort. The final model included T stage, PSA level, primary and secondary Gleason grade, and number of positive and negative biopsies. The c index for discrimination between high and low risk of recurrence was 0.68. The probability of progression-free survival ranged from 22% to 97% over the range of risk scores in the study population.
  
  
  CONCLUSIONS
  This model is based on nationwide population-based data and can be used with a fair predictive accuracy to guide decisions on clinical follow-up after prostatectomy. An online calculator for convenient clinical use of the model is available at www.npcr.se/nomogram.
authors.affiliation: a Department of Urology , Skåne University Hospital , Malmö , Sweden ;|b Department of Translational Medicine, Division of Urological Cancers , Lund University , Sweden ;
authors.forename: Anders
authors.initials: A
authors.lastname: Bjartell
authors.affiliation: c Unit of Biostatistics, Institute of Environmental Medicine , Karolinska Institutet , Stockholm , Sweden ;
authors.forename: Matteo
authors.initials: M
authors.lastname: Bottai
authors.affiliation: d Department of Cardiothoracic Surgery , Sahlgrenska University Hospital , Gothenburg , Sweden ;
authors.forename: Josefin
authors.initials: J
authors.lastname: Persson
authors.affiliation: b Department of Translational Medicine, Division of Urological Cancers , Lund University , Sweden ;|e Department of Urology , Cambridge University Hospitals , Cambridge , UK ;
authors.forename: Ola
authors.initials: O
authors.lastname: Bratt
authors.affiliation: f Department of Urology, Institute of Clinical Sciences , Sahlgrenska Academy, University of Gothenburg , Sweden ;
authors.forename: Jan-Erik
authors.initials: JE
authors.lastname: Damber
authors.affiliation: g Department of Surgical Sciences , Uppsala University , Uppsala , Sweden ;|h Department of Surgical and Perioperative Sciences, Urology and Andrology , Umeå University , Umeå , Sweden ;
authors.forename: Pär
authors.initials: P
authors.lastname: Stattin
authors.affiliation: i Department of Medicine Solna, Karolinska Institutet, and Department of Urology , Karolinska University Hospital , Stockholm , Sweden.
authors.forename: Olof
authors.initials: O
authors.lastname: Akre
mesh_terms: D000328:Adult; D000368:Aged; D018450:Disease Progression; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000453:epidemiology; D011379:Prognosis; D011468:Prostatectomy / Q000379:methods; D011471:Prostatic Neoplasms / Q000601:surgery*; D013548:Sweden
keywords: Biochemical recurrence; nomogram; predictive models; prostate cancer; radical prostatectomy