Numerical investigation of urethra flow characteristics in benign prostatic hyperplasia

Abstract

Background and objective: Conventional practice includes a limited depiction of urethral pressure and flows based on fragmented gross clinical observations. However, with technological advancements in simulations, computational fluid dynamics (CFD) can provide an alternative approach to predict the bladder pressure with a concordant quantitative flow field in the urethra. Thus, this study aims to comprehensively analyze the urine flow characteristics in various urethra models using simulations. Methods: Three-dimensional urethra models were constructed for seven specific subjects based on clinical radiographs. Simulations with Reynolds averaged Navier-Stokes model were performed to quantitatively investigate the urine flow under various volume flow rate of voided urine. Results: Under benign prostatic hyperplasia, the spindle shape of the prostatic urethra (PRU) generates wake flow. The wake flow was also observed in several regions downstream of the PRU, depending on the urethra shape. This wake flow resulted in total pressure loss and urinary tract dysfunction. When comparing pre- and post-operative urethra models, the bladder pressure decreased by 14.98% in P04 and 4.67% in P06. Thus, we identified variability between surgical results of patients. The bladder pressure according to the volume flow rate of voided urine was investigated using simulations and the theoretical consideration based on hydrodynamics. In theoretical consideration, the bladder pressure was expressed as a second-order polynomial for volume flow rate. These results concur with the simulation results. Conclusion: Numerical simulation can describe the urine flow field in the urethra, providing the possibility to predict the bladder pressure without requiring painful, invasive interventions, such as cystoscopy. Furthermore, effective treatments to improve urination function can be formulated to be patient-specific, by detecting causes and problem regions based on quantitative analysis and predicting post-surgical outcomes. © 2022