The EMPaCT Classifier: A Validated Tool to Predict Postoperative Prostate Cancer-related Death Using Competing-risk Analysis

Published on Jan 16, 2017in European urology focus4.827
· DOI :10.1016/J.EUF.2016.12.008
Lorenzo Tosco13
Estimated H-index: 13
(Katholieke Universiteit Leuven),
Annouschka Laenen23
Estimated H-index: 23
(Katholieke Universiteit Leuven)
+ 23 AuthorsSteven Joniau73
Estimated H-index: 73
(Katholieke Universiteit Leuven)
Abstract Background Accurate prediction of survival after radical prostatectomy (RP) is important for making decisions regarding multimodal therapies. There is a lack of tools to predict prostate cancer–related death (PCRD) in patients with high-risk features. Objective To develop and validate a prognostic model that predicts PCRD combining pathologic features and using competing-risks analysis. Design, setting, and participants This was a retrospective multi-institutional observational cohort study of 5876 patients affected by high-risk prostate cancer. Patients were treated using RP and pelvic lymph node dissection (PLND) in a multimodal setting, with median follow-up of 49 mo. Outcome measurements and statistical analysis For PCRD prediction, a multivariate model with correction for competing risks was constructed to evaluate pathologic high-risk features (pT3b–4, Gleason score ≥8, and pN1) as predictors of mortality. All possible associations of the predictors were combined, and then subgroups with similar risk of PCRD were collapsed to obtain a simplified model encoding subgroups with significantly differing risk. Eightfold cross-validation of the model was performed. Results and limitations After applying exclusion criteria, 2823 subjects were identified. pT3b–4, Gleason score ≥8, and pN1 were all independent predictors of PCRD. The simplified model included the following prognostic groups: good prognosis, pN0 with 0–1 additional predictors; intermediate prognosis, pN1 with 0–1 additional predictors; poor prognosis, any pN with two additional predictors. The cross-validation yielded excellent median model accuracy of 88%. The retrospective design and the short follow-up could limit our findings. Conclusions We developed and validated a novel and easy-to-use prognostic instrument to predict PCRD after RP+PLND. This model may allow clinicians to correctly counsel patients regarding the intensity of follow-up and to tailor adjuvant treatments. Patient summary Prediction of mortality after primary surgery for prostate cancer is important for subsequent treatment plans. We present an accurate postoperative model to predict cancer mortality after radical prostatectomy for high-risk prostate cancer.
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