Variation in positive surgical margin status following radical prostatectomy for pT2 prostate cancer

Document Type

Conference Proceeding

Publication Date

2019

Publication Title

Eur Urol Suppl

Abstract

Introduction & Objectives: Positive surgical margin (PSM)following radical prostatectomy for pT2 prostate cancer is considered a surgical quality metric. We evaluated patient, institutional, surgical approach and cancer-specific factors associated with PSM variability. Materials & Methods: A total of 45,426 men from 1,152 institutions with pT2 prostate cancer and known margin status following radical prostatectomy were identified using the National Cancer Database (2010-2015). Patient demographics and comorbidity, socioeconomic status, geographical and institutional information, cancer-specific variables and type of surgical approach were extracted. Multilevel hierarchical mixed effects logistic regression model was performed to determine the factors associated with a risk of PSM and their contribution to a PSM status. Results: Median PSM rate of 8.5% (IQR: 5.2-13.0%, range: 0-100%). Robotic (OR: 0.90, 95% CI: 0.83-0.99)and laparoscopic (OR: 0.74, 95% CI: 0.64-0.90)surgical approach, academic institution (OR: 0.87, 95% CI: 0.76-1.00), high institution surgical volume (>297 cases [OR: 0.83, 95% CI: 0.70-0.99)and East North Central USA (OR: 0.71, 95% CI: 0.52-0.96)were independently associated with a lower PSM. Black men (OR: 1.13, 95% CI: 1.01-1.26)and adverse cancer specific features (PSA 10-20, PSA >20, cT3 stage, Gleason 7, 8, 9-10; all p>0.01)were independently associated with a higher PSM. The overall multilevel hierarchical logistic regression model accounts for 24.9% of PSM variation. Patient-specific, institution-specific and cancer-specific factors accounted for 9.1%, 15.6% and 61.1% of the variation within the overall regression model respectively. [Figure presented]Conclusions: Cancer-specific factors account for 15.2% of PSM variation with the remaining 84.8% of PSM variation due to patient, institution and other factors not accounted for in the model. Non cancer-specific factors represent potentially addressable factors which are important for policy makers in their efforts to improve patient outcome.

Volume

18

Issue

1

First Page

e294

Last Page

e295

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