Patient reported outcome measures concerning urinary incontinence after robot assisted radical prostatectomy: development and validation of an online prediction model using clinical parameters, lower urinary tract symptoms and surgical experience

With the increasing public awareness on prostate cancer and opportunistic screening methods, men are being diagnosed with localized prostate cancer at an earlier stage of disease. At present, prostate cancer is the most frequently diagnosed malignancy in men of 50 years and older. Radical prostatectomy (RP) is the most important surgical curative approach in prostate cancer patients. One of the negative consequences next to erectile Dysfunction of the surgical procedure is postoperative prostatectomy incontinence (PPI) [ 1]. PPI has a substantial negative effect on the overall health-relatively quality of life (HRQoL) and satisfaction of patients who underwent this surgical procedure [ 2].

In a systematic review of more than 8000 men who underwent robotic, laparoscopic or open RP, rates of PPI ranged from 4 to 31%.[ 2]. The literature concerning the predictive value of different pre-operative variables for PPI shows conflicting results. PPI is influenced by a wide set of pre-operative clinical variables, anatomic patient characteristics, per-operative surgical techniques, surgeon experience, as well as by the definition of urinary incontinence, the methodology to assess its impact and by the methods used to collect and report these data [ 2– 5]. In these former studies on PPI, increased age and higher body mass index (BMI), higher comorbidity index, severity of lower urinary tract symptoms (LUTS) and larger prostate volumes were the most relevant pre-operative predictors of PPI after RARP [ 2].

There are, however, only few risk formulas, risk tables and nomograms predicting PPI after robot assisted radical prostatectomy (RARP) [ 6]. Existing models are hampered by low level of statistical power due to small sample size or by the absence of important predictive factors for PPI into the model such as surgical technique and surgical experience. By our knowledge, no online prediction model of PPI yet exists. We aimed to evaluate the weight of clinical, as well as different surgical variables to predict PPI after RARP. We developed and validated an online tool that may assist clinicians and their patients in adequate counseling of the risk for PPI after RARP.

The median follow-up time for the whole patient series was 36 months (range 12–108). The response rate for returning the EPIC26 questionnaire at one year of follow-up was 85.2% (1856 of 2178 patients). A total of 322 patients were not evaluable for analysis due to not returning the questionnaire, or because these patients were lost to follow-up. In total, 2.3% (42/1856) of patients returned their PROMs questionnaire, but were not assessable for analysis due to several other reasons (such as no answer to the questions, unreadable PROMS). This led to 1814 patients being evaluable for analysis. Pre-operative parameters of evaluated patients are listed in Table 1.

Postoperative urinary incontinence is one of the most bothersome side-effects of robot-assisted radical prostatectomy (RARP). If adequate prediction of PPI is possible, it may be a helpful tool in the counseling of patients in their choice of treatment after a diagnosis of prostate cancer. Also, patients at high risk for PPI could be withheld surgery, or if surgery remains mandatory, specific surgical techniques could be used to maximize functional outcomes [ 9]. This is one of the few studies that is aimed to develop an online prediction model for PPI after RARP that may be used by both clinicians and patients before treatment. Beside well-known risk factors for PPI as are described in the urological literature such as (older) age and the severity of lower urinary tract symptoms, this study used a set of less commonly used predictive factors for PPI such as the extent of nerve-sparing surgery and surgical experience.

The present study reports on a large consecutive cohort of patients with a relatively long median follow-up of 36 months (range 12–108) compared to similar studies on this subject. The continence rates were defined by patient reporting outcome measures (PROMS) using validated questionnaires that were handed over to patients at set and well-defined time point after surgery. Here, a properly used definition of urine continence was used as is recommended by the European Association of Urology, i.e. the use of zero pads or the use of only one safety pad per day. One of the major strengths of the present study is the high response rate of men returning the PROMS questionnaires (i.e., 85.2%), the large size of the study cohort (i.e., 1814 patients), and the important primary endpoint (i.e., PPI at one year after RALP).

Herein, a wide series of clinical, and surgical variables, i.e. older age, higher BMI, higher prostatic volume, higher ASA-score, the severity of LUTS, absence of nerve-sparing surgery and less surgical experience were all statistically significant risk factors for poor outcome on univariate analysis when PPI was evaluated (Table 2). In multivariable logistic regression analysis, we found pre-operative severity of LUTS, higher age, the extent of nerve sparing surgery and surgeon experience to be significant independent predictors of post-operative continence status (Table 3). ASA, BMI and prostate volume were not significantly associated with continence. Based on these data, an online prediction model for PPI status was developed. At internal validation, our online prediction model performed well in predicting PPI with poor discrimination and good calibration (Fig.  2). The online prediction mode is available online and free accessible at www.​collette.​nl/​calcu.

There are only few studies predicting PPI using validated nomograms. AUC values ranged from 0.67 to 0.71. Nomograms have the highest accuracy and best discriminating characteristics among the various prediction tools, as they are constructed through multivariable models. By our knowledge, no online prediction model of PPI exists.

Matsushita et al. included clinical parameters such as patient age, BMI, ASA score and multiparametric magnetic resonance imaging (mpMRI) measured membranous urethral length (MUL) to develop a predictive model for PPI. In this retrospective analysis of data from 2849 patients undergoing RARP using multivariable logistic regression analysis, an intuitive nomogram was developed that could be used to counsel patients on their risk of PPI after radical prostatectomy. However, one of the backsides of this study is that surgical experience and LUTS were not assessed in their predictive model. Also, PROMs were not used in data gathering [ 6].

Based on a consecutive series of 1168 robot-assisted and open retropubic radical prostatectomies, Jeong et al. developed and validated nomograms to predict early, intermediate and late recovery from urinary incontinence after surgery using a multivariate model. Age at surgery, MUL, and robot-assisted radical prostatectomy were significant for recovery of incontinence at 1, 3, and 12 months. Saving the neurovascular bundle (NVB) and prostate volume were significant only for recovery of incontinence at 12 months. Severity of LUTS and surgical experience were not included into the model [ 10]. Also, only a part of patients underwent pre-operative mpMRI so could not benefit from this prediction model.

In a study on functional outcomes after nerve-sparing radical prostatectomy in 1311 patients, a nomogram was developed based on age, BMI, erectile function, surgery type (open or robotic) and extent of nerve sparing type. PROMs were filled in during outpatient clinic. Pre-operative LUTS and surgical experience were not assessed in the nomogram [ 11]. Patients with urinary incontinence consisted mainly of individuals who were impotent before RP, elderly and/or overweight. A nomogram was based on 209 patients' age-adjusted Charlson’s comorbidity index, Erectile Function, prostate volume, nerve-sparing status and 24-h urine loss at 1 month after RARP to predict 1-year PPI. The study is hampered by low level of statistical power due to a small sample group [ 12].

The present study may be hampered by several pitfalls. It may well be that certain predictors for PPI were not included in the present analysis where these were reported to have prognostic impact in previous studies. These variables may be previous transurethral resection of the prostate (TURP) prior to RARP, or the presence of specific anatomical features such as the presence of a (large) middle prostatic lobe or posterior prostatic lobe. Most importantly, the variables that can, at present, be evaluated on pre-operative mpMRI were not used in the present predictive model. This holds true for the MUL and the inter-levator distance (ILD) which have recently shown to have prognostic impact for PPI [ 6, 10]. However, at the time that the present cohort of patients underwent RARP, mpMRI was not yet performed as a routine diagnostic imaging modality in the clear majority of patients. Also, MUL is known to be not properly defined yet by radiologist and is therefore prone for high interobserver variability. At last, patients who underwent oncological treatment due to progression of disease such as by salvage radiation therapy to the prostatic fossa or hormonal medication were censored for the primary outcome of PPI at the time treatment was initiated. It might well be that the urinary continence rate might have improved further by continued follow-up, whereas this was censored at the time of last follow-up. On the other hand, most patients will not have started salvage oncological treatment within 1 year after RARP.

Due to the complexity and challenging nature of radical prostatectomy (RP), it is likely that both short- and long-term outcomes of urinary incontinence strongly depend on the cumulative number of cases performed by the surgeon as well as by the hospital. It has been a subject of long debate to what extent this influences continence outcomes and what the threshold is for the number of cases that need to be treated to gain optimal outcomes. Trinh et al. performed a systematic review in which the association between hospital and surgeon volume and perioperative, oncologic, and functional outcomes after RP were assessed. From 45 studies reporting on this subject retrospectively, undeniable evidence suggests that increasing surgical volume improves outcomes. A similar study was performed by Wilt et al. In their systematic review, the association between hospital and surgeon volume, and patient outcomes after RP were assessed. The rate of late urinary complications was 2.4% lower (95% CI − 5, − 0.1) and the rate of long-term incontinence was 1.2% lower (95% CI − 2.5, − 0.1) for each 10 additional RP performed by the surgeon annually. So, higher provider volumes of surgery are associated with better outcomes after RP and an argument to implement this variable in future models predicting PPI after RARP [ 13, 14].

Using consecutive data from a large, well-defined cohort of patients who underwent RARP in a single robotic center, a predictive model for PPI at one year postoperatively was developed. The study reported a high response rate (85.2%), a long median follow-up time (36 months (range 12–108), and used validated PROMS to define the primary endpoint of PPI. On univariate analysis, all variables were associated with PPI. In multivariable logistic regression analysis, severity of LUTS, higher age, extent of nerve sparing surgery and surgeons experience proved to be independent significant predictors of PPI. ASA, BMI and prostate volume were not significantly associated to PPI. An intuitive online prediction model to predict PPI was build which includes the independent predictors for PPI one year after RARP. At internal validation, our online prediction model performed well in predicting post-operative incontinence with poor discrimination and good calibration.