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NY Prostate Cancer Conference - A. Vickers - Session 7: Should surgeon specific factors be incorporated in prediction modeling?
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NY Prostate Cancer Conference - A. Vickers - Session 7: Should surgeon specific factors be incorporated in prediction modeling?

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NY Prostate Cancer Conference - A. Vickers - Session 7: Should surgeon specific factors be incorporated in prediction modeling? NY Prostate Cancer Conference - A. Vickers - Session 7: Should surgeon specific factors be incorporated in prediction modeling? Presentation Transcript

  • Should surgeon specific factors be incorporated in prediction modeling?
    • Andrew J. Vickers
    • Associate Attending Research Methodologist
    • Memorial Sloan-Kettering Cancer Center
  • Does a patient ’s chance of cure depend on the surgeon?
    • Oncologic surgery is highly skilled
    • It is plausible that the outcome of surgery depends on the surgeon
      • More experienced surgeons better than less experienced surgeons?
      • Some surgeons better than others with equivalent levels of experience?
  • Complications after radical prostatectomy Volume -------------- Outcome Low Medium High Very High Postoperative complications 32% 31% 30% 26% Urinary complications 28% 26% 27% 20%
  •  
  • The learning curve for open radical prostatectomy
  • Organ confined Non-organ confined
  •  
  • Patient characteristics laparoscopic learning curve Prior cases (surgeon experience) before incident case < 50 50-99 100-249 250-1000 p n 793 611 946 2352 PSA (ng / ml) 6.9 (5.0, 10.0) 6.8 (5.0, 9.8) 7 (5.1, 10.3) 5.9 (4.3, 8.5) 0.11 Age at RP 64 (59, 68) 64 (59, 68) 63 (58, 68) 61 (56, 66) 0.036 Path. Gleason 0.4 ≤ 6 365 (46%) 255 (42%) 439 (46%) 1024 (44%) 7 375 (47%) 311 (51%) 423 (45%) 1180 (50%) ≥ 8 53 (7%) 45 (7%) 84 (9%) 148 (6%) Non-organ confined 247 (31%) 205 (34%) 304 (32%) 612 (26%) 0.3
  • Principal analysis
    • After adjustment for stage, grade, PSA, highly significant relationship between surgeon experience & cancer recurrence (p=0.005)
    • Risk of recurrence at five years:
      • 17% for surgeon with 10 prior cases
      • 9% for surgeon with 750 prior cases
    • Absolute risk difference of 8.0% NNT 13
  • Open RP Laparoscopic RP
  • What about variation of functional outcomes?
    • 1,333 patients treated with radical prostatectomy at MSKCC 1999 - 2007
    • Evaluated for urinary and erectile function one year after surgery
        • Urinary function: no pads
        • Erectile function: full erections sufficient for sexual activity
  •  
  •  
  • Surgeon experience affects predictiveness Locally advanced disease Gleason 8 Gleason 7 PSA AUC 0.750 for patients treated by surgeons with <50 cases 0.849 for patients treated by surgeons with >500 cases
  • Surgeon specific factors and prediction modelling
    • Already a problem!
    • Kattan nomogram based on patients treated by a leading surgeon at a major academic center
  • Application of models created in academic centers
    • An issue of calibration not discrimination
    • Patients with organ confined disease will do better no matter who they see
    • Absolute risk of recurrence varies ten fold by experience:
      • Inexperienced surgeon: ~15% risk
      • Experienced surgeon: ~1% risk
  • CAPRA vs. Stephenson
  •  
  • Prediction models and surgeon specific factors
    • So….
    • Should surgeon be a variable in prediction models?
  • Nomogram including surgeon experience
  • Nomogram including surgeon experience: AUC 0.812 vs. 0.811
  • Radical prostatectomy outcomes collaboration
  • Inclusion of surgeon factors in prediction models
    • My best guess?:
      • High vs. low volume surgeon
    • Will it help prediction?
      • I don’t know
    • Will it help education?
      • Certainly!