Integrating clinical and image-based parameters for prediction of early post-prostatectomy incontinence recovery: simplified nomogram approach

Purpose: This study aimed to develop a novel model that combines both clinical and image-based parameters to predict early recovery of urinary incontinence after robotic-assisted radical prostatectomy (RARP) more easily and precisely.

Materials and methods: We retrospectively enrolled data from patients who underwent RARP performed by a single surgeon. Clinical parameters were collected through medical chart review. All patients received cystography one week after RARP to evaluate the anastomosis healing condition. All cystography images were analyzed by a single radiologist who was blinded to the clinical status of the patients. Multivariate analysis was performed to select significant predictors for early post-prostatectomy incontinence (PPI) recovery, defined as being pad-free within four weeks after surgery.

Results: A total of 293 patients were enrolled in this study. Among them, 26.7% experienced immediate dryness after surgery, while 47.6% achieved being pad-free within one month. The overall continence rate was over 90% six months after surgery. In univariate analysis, factors associated with early PPI recovery were BMI, T stage, NVB preservation, surgical margin status, downward bladder neck, and bladder neck angle on cystography. BMI, NVB preservation, and downward bladder neck remained significant in multivariate analysis (p-values = 0.041, 0.027, and 0.023, respectively). A nomogram model was established based on these three predictors.

Conclusion: This is the first model to combine preoperative clinical factors, peri-surgical factors, and postoperative image-based factors to predict PPI recovery after RARP. This model can assist clinicians in taking optimal actions for PPI and also reduce patient anxiety.