Biostatistical modeling using traditional preoperative and pathological prognostic variables in the selection of men at high risk for disease recurrence after radical prostatectomy for prostate cancer.
PURPOSE: Biostatistical models predicting the risk of recurrence after radical prostatectomy for clinically localized prostate cancer are necessary. Identifying these high risk patients shortly after surgery, while tumor burden is minimal, makes them candidates for possible adjuvant therapy and/or investigational phase II clinical trials. This study builds on previously proposed models that predict the likelihood of early recurrence after radical prostatectomy. MATERIALS AND METHODS: In our analysis we evaluate age, race, prostatic acid phosphatase and nuclear grade with the established prognostic variables of pretreatment prostate specific antigen, postoperative Gleason sum and pathological stage. RESULTS: After multivariable Cox regression analysis using only statistically significant variables that predicted recurrence we developed an equation that calculates the relative risk of recurrence (Rr) as: Rr = exp[(0.51 x Race) + (0.12 x PSAST) + (0.25 x Postop Gleason sum) + (0.89 x Organ Conf.). These cases are then categorized into 3 distinct risk groups of relative risk of recurrence of low (< 10.0), intermediate (10.0 to 30.0) and high (> 30.0). Kaplan-Meier survival analysis of these 3 risk groups reveals that each category has significantly different risks of recurrence (p < 0.05). This model is validated with an independent cohort of radical prostatectomy patients treated at a different medical center by multiple primary surgeons. CONCLUSIONS: This model suggests that race, preoperative prostate specific antigen, postoperative Gleason sum and pathological stage are important independent prognosticators of recurrence after radical prostatectomy for clinically localized prostate cancer. Race should be considered in future models that attempt to predict the likelihood of recurrence after surgery.
Bauer, JJ; Connelly, RR; Seterhenn, IA; Deausen, J; Srivastava, S; McLeod, DG; Moul, JW
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