Estimating acute urinary retention risk post prostate high dose-rate (HDR) brachytherapy: A clinical-based recursive partitioning analysis

Bladder Cancer

Radiother Oncol. 2020 Sep 20:S0167-8140(20)30798-2. doi: 10.1016/j.radonc.2020.09.023. Online ahead of print.


PURPOSE: To determine factors associated with need for post-procedural catheterization in prostate cancer patients treated with 15 Gy high dose-rate brachytherapy boost (HDR-BT).

MATERIAL AND METHODS: Patients treated with 15 Gy HDR-BT followed by EBRT were retrospectively evaluated for development of urinary retention and hematuria requiring catheterization in the first 30 days post procedure. Clinical characteristics and treatment details were obtained and used as independent variables under study. Univariable and multivariable logistic regression analysis were used to determine predictors of post brachytherapy complications and a classification tree for risk of urinary retention was created using recursive partitioning analysis (RPA).

RESULTS: A total of 425 patients treated with 15 Gy HDR-BT were included in this analysis. 27 patients (6.3%) required catheter placement due to acute urinary retention and thirteen other patients (3%) developed hematuria requiring urinary catheter insertion ± continuous bladder irrigation. Number of needles, prostate volume and prior use of ADT, alpha-blockers or 5α-reductase inhibitors were statistically associated with urinary retention in the univariable logistic regression analysis. In multivariable analysis, prostate volume, previous use of alpha-blocker, and use of ADT remained significant. In the RPA, populations were identified in which the rate of urinary retention ranged from 2% to 50% depending on presence of one or more of these risk factors.

CONCLUSION: The overall rate of acute urinary complications post HDR brachytherapy is low, but the individual risk of urinary retention can increase depending on the number of risk factors present. A more patient-directed retention risk estimation can be performed by using the classification risk tree presented here.