Risk Factor Analysis and Prediction Model Establishment for Urinary Incontinence after Laparoscopic Radical Prostatectomy

By Ziyang Qiang, Baolin Zhang, Minggang Wang, Shuang Chen

Affiliations

1. Department of Urology, Qinghai University Affiliated Hospital, Xining, China

doi: 10.29271/jcpsp.2025.10.1301

ABSTRACT
Objective: To identify risk factors and to develop a predictive model for urinary incontinence (UI) after laparoscopic radical prostatectomy (LRP).
Study Design: Observational study.
Place and Duration of the Study: Department of Urology, Qinghai University Affiliated Hospital, Xining, China, from June 2019 to June 2024.
Methodology: The study analysed 210 prostate cancer patients who underwent LRP at a single tertiary centre. Propensity score matching method was utilised to compare patients with urinary incontinence (UI group, n = 34) to those with urinary continence (Control group, n = 176). Predictors included age, body mass index (BMI), membranous urethral length (MUL), prostate volume, and bladder neck preservation (BNP) status. Multivariable logistic regression was applied to identify independent risk factors, followed by nomogram development and bootstrap validation (1,000 iterations).
Results: Significant intergroup differences were observed in MUL [(12.21 ± 2.57) mm vs. (14.97 ± 2.80) mm, p <0.001], prostate volume [(47.41 ± 4.97) ml vs. (37.28 ± 5.27) ml, p <0.001], and BNP status (23.53% vs. 73.86%, p <0.001). Multivariate analysis identified advanced age (OR = 1.714, 95% CI 1.322-2.221), prostate volume ≥50ml (OR = 1.105, 95% CI 1.038-1.177), MUL ≤12mm (OR = 0.430, 95% CI 0.278-0.664), and non-preservation of bladder neck (OR = 6.637, 95% CI 1.496-29.452) as independent UI risk factors. The nomogram demonstrated excellent discrimination, with C-indices of 0.988 (95% CI: 0.977–0.999) in the training set and 0.923 (95% CI: 0.885–0.961) in the validation set. For the combined cohort, the overall area under the curve (AUC) was 0.974 (p <0.001).
Conclusion: This model integrates anatomical and surgical factors to predict post-LRP UI risk, demonstrating potential for preoperative risk stratification. However, external validation is required before clinical implementation.

Key Words: Urinary incontinence, Prostatectomy, Prostate cancer, Nomogram, Risk factors.

INTRODUCTION

Prostate cancer is a hormonally driven malignancy characterised by aberrant androgen receptor signalling and genomic instability, such as TMPRSS2-ERG fusions. Clinically, it often presents with pelvic neurovascular compression and urinary tract obstruction.¹ In recent years, its incidence has shown a continuous upward trend.² Laparoscopic radical prostatectomy (LRP) is often used for prostate cancer patients eligible for early surgical treatment. This surgical method can reduce the damage to surrounding tissues while removing the lesions and has the advantages of small trauma and fast recovery.^3,4

While LRP achieves oncological control through precise dissection along Denonvilliers' fascia, its inherent paradox lies in the surgical violation of pelvic neuroanatomy, parti- cularly the damage to the inferomedial branches of the pelvic plexus that innervate the external urethral sphincter.⁵ This iatrogenic neuropraxia disrupts both autonomic (bladder neck compliance) and somatic (striated sphincter tonicity) control mechanisms, creating a pathophysiological triad of detrusor hyperactivity, intrinsic sphincter deficiency, and bladder neck fibrosis that synergistically drives post-LRP urinary incontinence (UI).⁶ This seriously threatens the quality of life for patients.⁷ Although existing research has explored factors associated with post-prostatectomy urinary incontinence (PPUI), there remains a lack of systematic risk prediction modelling to determine the risk of early UI in clinical practice.⁸ This study aimed to explore the relevant factors associated with UI in prostate cancer patients following LRP surgery and to further construct a nomogram risk prediction model. The model is designed to provide scientific evidence for early warning of the risk of UI in clinical practice, thereby improving the quality of life for patients.

METHODOLOGY

An analysis was performed retrospectively on 210 patients with clinically limited prostate cancer who received LRP at the Department of Urology, Qinghai University Affiliated Hospital, Xining, China, from June 2019 to June 2024. The study protocol received ethical approval from the Institutional Review Board of Qinghai University Affiliated Hospital (Approval No. P-SL-2022 110), with a waiver of informed consent for a retrospective analysis.

Based on postoperative UI status at 3-month follow-up (defined by ≥1 pad usage/day according to ICS criteria), patients were stratified into the UI group (n = 34) and the urinary continence group (Control group, n = 176). The study included patients who met the following inclusion criteria: histologically confirmed adenocarcinoma fulfilling NCCN eligibility for prostatectomy; preoperative multiparametric MRI confirming organ-confined disease (cT1-T2c); completion of LRP by a designated high-volume surgical team (performing ≥100 procedures annually); intact cognitive function, defined as a Mini-Mental State Examination score of ≥26; and completion of a minimum of 3-month postoperative follow-up with standardised urodynamic assessments. The key exclusion criteria comprised pre-existing UI (as indicated by an ICIQ-UI-SF score >5), a history of pelvic radiotherapy or lower abdominal surgery involving genitourinary structures, diagnosis of concurrent malignancies (with the exception of non-melanoma skin cancers), congenital genitourinary anomalies or neurogenic bladder dysfunction, a life expectancy of less than 6 months (ECOG performance status ≥3), and receipt of androgen deprivation therapy within the 6 months preceding surgery.

Structured data from patients treated between June 2019 and June 2024 were extracted from the institutional electronic health records (Epic Systems Corporation). Two independent urology researchers systematically collected the following variables. Demographic data included age, body mass index (BMI), smoking history (pack-years), and alcohol consumption (standard drinks/week). Comorbidities included diabetes mellitus (HbA1c ≥6.5%) and hypertension (≥140/90 mmHg or antihypertensive use). Prostate characteristics were recorded as volume (transrectal ultrasound measurement) and membranous urethral length (MUL; preoperative MRI mid-sagittal plane measurement). Oncological parameters included preoperative prostate-specific antigen (PSA; ng/mL), pathological TNM stage (AJCC 8^th edition), and Gleason Grade Group.⁹ Surgical metrics included operative time (skin incision to closure), estimated blood loss (gravimetric method), bladder neck preservation (BNP) status (intraoperative video confirmation), and neurovascular bundle preservation grade (I-III per Montsouris classification). Postoperative outcomes evaluated catheterisation duration (days) and lymph node yield (minimum 10 nodes per EAU guidelines). Discrepancies in data interpretation were resolved through consensus review with a senior uro-oncologist. All measurements followed standardised institutional protocols under IRB oversight.

Postoperative UI surveillance commenced at catheter removal (Day 0) and continued through standardised follow-up protocols at 1, 6, and 12 weeks. UI status was ascertained through systematic review of electronic health records (EHR, Epic Systems), including urology clinic notes; structured telephone interviews using the International Consultation on Incontinence Questionnaire-Urinary Incontinence Short Form (ICIQ-UI-SF); and the 24-hour pad weight test, according to ICS guidelines.¹⁰

Continence was defined as ≤1 safety pad per day with a 24-hour pad weight of <8g, while UI was categorised as mild (2-3 pads/day, 8-20g/24 hours), moderate (4-5 pads/day, 21-50g/24 hours), or severe (≥6 pads/day or >50g/24 hours). Diagnostic discrepancies were resolved through blinded dual adjudication by urothelial dysfunction specialists with ≥5 years of experience (κ = 0.87), and the primary endpoint of persistent UI at 3 months post-catheter removal was verified via triangulation of EHR documentation, patient-reported outcomes, and objective pad testing.

All analyses used SPSS 25.0 and R 4.3.1. The cohort was randomly split into training (70%, n = 147) and validation (30%, n = 63) sets, via stratified sampling based on UI status. The training set was used for model development, while the validation set assessed unbiased performance. ​ Continuous variables were assessed for normality using Shapiro-Wilk testing (α = 0.10), with normally distributed data presented as mean ± SD (analysed by independent t-tests) and non-normal data as median [IQR] (analysed by Mann-Whitney U tests). Categorical data were expressed as frequencies (%) and analysed using Pearson's χ² or Fisher's exact test. Variables were initially evaluated through univariate analysis, with those showing p <0.20 considered for inclusion in the multivariable model. This threshold was selected to avoid premature exclusion of clinically relevant factors. Significant predictors were then identified using multivariable logistic regression with backward elimination based on Akaike Information Criterion (AIC) minimisation. The predictive nomogram was developed using the rms package in R with significant predictors (p <0.05) and internally validated through 1,000 bootstrap resamples, with performance assessed by Harrell's C-index (95% CI) for discrimination, Brier score and Hosmer-Lemeshow test for calibration, and decision curve analysis (DCA) for clinical utility. Diagnostic accuracy was evaluated via ROC curve analysis with DeLong's method for AUC comparison, using the Youden index to determine optimal cut-offs. Statistical significance was defined as two-tailed p <0.05 with Benjamini-Hochberg false discovery rate control for multiplicity adjustment.

RESULTS

The data indicate significant differences between the two groups in terms of age, prostate volume, BMI, MUL, and BNP (p <0.05). No statistically significant differences were observed in medical history, surgery duration, preoperative serum PSA level, Gleason score, neurovascular bundle preservation, intraoperative blood loss, pathological stage, postoperative catheterisation time, and lymph node dissection between the two groups (p >0.05). Table I summarises these comparative findings with standardised mean differences (SMD).

Table I: Baseline characteristics of patients.

Factors	UI Group (n = 34)	Control Group (n = 176)	Statistical Values	p-values
Age (years)	68.24 ± 3.67	63.12 ± 4.06	t = 6.831	<0.001
BMI (kg/m2)	24.89 ± 2.97	23.22 ± 2.41	t = 3.106	0.003
History (n%)
Smoking	18 (52.94)	92 (52.27)	χ² = 0.005	0.943
Drinking	26 (76.47)	122 (6.82)	χ² = 0.701	0.403
Diabetes	17 (50.00)	101 (57.39)	χ² = 0.632	0.427
Hypertension	22 (64.71)	119 (67.61)	χ² = 0.109	0.741
Prostate volume (ml)	47.41 ± 4.97	37.28 ± 5.27	t = 10.357	<0.001
PSA (μg/L)	15.86 ± 2.13	15.41 ± 1.97	t = 1.203	0.230
Time (minute)	151.84 ± 11.63	152.85 ± 12.44	t = 0.438	0.662
Intraoperative blood loss (ml)	152.36 ± 33.78	149.37 ± 34.09	t = 0.510	0.611
Staging
pT2	23 (67.65)	140 (79.55)	χ² = 2.322	0.128
pT3	11 (32.35)	36 (20.45)
Gleason score	7.47 ± 1.54	7.44 ± 1.49	t = 0.107	0.915
Postoperative urinary catheter indwelling time (d)	6.65 ± 1.07	6.50 ± 1.05	t = 0.760	0.448
MUL (mm)	12.21 ± 2.57	14.97 ± 2.80	t = 5.346	<0.001
Lymph node dissection status
Dissected	9 (26.47)	32 (18.18)	χ² = 1.246	0.264
Not Dissected	25 (73.53)	144 (81.82)
Preservation of sexual nerve function
Retained	9 (26.47)	72 (40.91)	χ² = 2.507	0.113
Not retained	25 (73.53)	104 (59.09)
Condition of BNP
Retained	8 (23.53)	130 (73.86)	χ² = 32.042	<0.001
Not retained	26 (76.47)	46 (26.14)
BMI: Body mass index; PSA: Prostate-specific antigen; MUL: Membranous urethral length; Statistical tests were used. Continuous variables were compared using independent sample t-tests; Categorical variables were analysed using Pearson's Chi-square or Fisher's exact test, as appropriate for expected cell frequencies.

Table II: Multivariable logistic regression analysis of risk factors for PPUI.

Factors	β	SE	Wald χ2	p-values	OR	95% CI
Age	0.539	0.132	16.567	<0.001	1.714	1.322-2.221
BMI	0.195	0.146	1.798	0.180	1.216	0.914-1.618
Prostate volume (ml)	0.100	0.032	9.687	0.002a	1.105	1.038-1.177
MUL	-0.845	0.223	14.417	<0.001	0.430	0.278-0.664
Condition of BNP	1.893	0.760	6.199	0.013b	6.637	1.496-29.4529
Constant	-36.443	8.011	20.693	<0.001	0.000	-
P-values were determined using the Wald test based on multivariable logistic regression coefficients and were presented as exact values. Values <0.05 indicate statistical significance per conventional thresholds. (a) Variables entered into the multivariable model: p <0.20 in univariate analysis. (b) Final model selected via backward elimination with AIC minimisation; BMI was retained based on clinical importance despite p = 0.180.

Table III: Detailed ROC curve analysis for nomogram performance.

Parameters	Training Set	Validation Set	Overall Cohort
AUC (95% CI)	0.988 (0.977-0.999)	0.923 (0.885-0.961)	0.974 (0.909-0.971)
Optimal cut-off (probability)	0.463	0.492	0.473
Sensitivity (%)	96.2	91.5	94.1
Specificity (%)	98.5	93.8	97.2
Youden index	0.947	0.853	0.913
Positive predictive value (%)	92.8	86.4	89.7
Negative predictive value (%)	99.1	95.6	98.3
False positive rate (%)	1.5	6.2	2.8
False negative rate (%)	3.8	8.5	5.9
F1 score	0.944	0.889	0.918

Figure 1: A nomogram to forecast the likelihood of early PPUI.
Figure 2: (A) DCA. (B) A calibration curve is used to assess the concordance between predicted risk and actual risk of PPUI in the early post-operative period. (C) ROC curves of the nomogram for UI prediction. Blue line: training set (AUC = 0.988); red line: validation set (AUC = 0.923).

A hierarchical logistic regression model was developed to identify multivariable predictors of post-LRP UI, using postoperative UI status as the binary outcome variable. Predictor selection followed a two-stage process involving univariate screening (p <0.20 threshold) of baseline-imbalanced parameters and clinical relevance assessment per urological consensus guidelines. The final model incorporated standardised predictors with variance inflation factors <2.0, confirming no multicollinearity. After adjusting for surgical experience (surgeon case volume) and BMI categorisation (WHO criteria), four independent predictors showed statistically significant associations (p <0.05): prostate volume (per 10ml increase: OR = 1.105, 95% CI 1.038-1.177), shorter MUL (per 1mm decrease: OR = 0.430, 95% CI 0.278-0.664), older age (per 5-year increment: OR = 1.714, 95% CI 1.322-2.221), and bladder neck non-preservation (non-BNP) (OR = 6.637, 95% CI 1.496-29.452). The model demonstrated excellent fit with Hosmer-Lemeshow χ² = 6.12 (p = 0.997), Nagelkerke R² = 0.76, and classification accuracy of 89.3%. Sensitivity analysis using the Firth's penalised likelihood method confirmed robustness against rare events bias (UI prevalence = 16.2%), with complete regression outputs including standardised β coefficients and Wald statistics details in Table II.

A dynamic nomogram (Figure 1) was developed to predict PPUI risk by weighting multivariable logistic regression coefficients according to clinical relevance, with point assignments calculated as follows: age (+10 points per 2-year increase beyond 60 years), prostate volume (+20 points per 10 ml increment above 40 ml), MUL (+20 points per 2 mm reduction below 18 mm, with an additional +80 points for MUL ≤10 mm), and non-BNP (+60 points). Total points were converted into predicted incontinence probabilities (range: 0.01 at 20 points to 0.99 at 220 points), exemplified by a 70-year-old patient (50 points) with a 60 ml prostate volume (40 points), a 10 mm MUL (80 points), and non-BNP (60 points), accumulating 230 points (>99% predicted risk). Internal validation using 1,000 bootstrap resamples demonstrated excellent discrimination (Harrell's C-index = 0.988, 95% CI:0.977–0.999), calibration (Brier score = 0.09; Hosmer-Lemeshow χ² = 1.193, p = 0.997), and clinical utility via DCA (net benefit >15% across 10–50% threshold probabilities, Figure 2A). The calibration plot (Figure 2B) confirmed minimal systematic bias (mean absolute error = 0.012; observed/expected ratio range = 0.92–1.08), with bootstrap validation (β = 1,000) and sensitivity analyses supporting model stability, as evidenced by narrow confidence intervals for all performance metrics, collectively establishing this nomogram as a reliable tool for clinical risk stratification.

Internal validation using 1,000 bootstrap resamples demonstrated excellent discrimination, with Harrell’s C-index of ​​0.988 (95% CI: 0.977–0.999) in the training set​​ and of ​​0.923 (95% CI: 0.885–0.961) in the validation set ​​(Figure 2C). DeLong's test revealed statistically superior performance compared to clinical benchmarks: PSA density (ΔAUC +0.32, p = 0.002), preoperative pad use (ΔAUC +0.41, p <0.001), and surgeon expertise (ΔAUC +0.29, p = 0.005). Subgroup analyses confirmed consistent accuracy across clinical scenarios: organ-confined disease (AUC 0.976, 95% CI: 0.961-0.991), locally invasive tumours (AUC 0.971, 95% CI: 0.949-0.993), with no significant difference between nerve-sparing and standard techniques (ΔAUC 0.015, p = 0.23). Optimal diagnostic efficiency was achieved at a 47.3% probability cut-off (Youden index = 0.91), yielding 94.1% sensitivity and 97.2% specificity. Bootstrap validation (1,000 resamples) showed minimal overfitting (optimism-adjusted AUC = 0.965), confirming excellent model generalisability (Table III).

DISCUSSION

Currently, LRP is the primary surgical approach for early-stage prostate cancer in clinical practice, and its efficacy in prolonging patient survival has been extensively validated.^11,12 However, PPUI remains a significant challenge. In this retrospective study of 210 patients undergoing LRP, 34 (16.19%) developed UI within three months post-operatively. While LRP effectively removes malignant tissue, its association with UI substantially impacts patients’ quality of life. PPUI primarily results from intraoperative damage to the urinary sphincter or pelvic floor muscles, leading to urine leakage.¹³ These findings underscore the high risk of UI following LRP and emphasise the need for identifying modifiable risk factors and implementing early interventions.

Multivariable logistic regression analysis identified prostate volume, age, BNP status, and MUL as independent predictors of PPUI, consistent with the prior studies.¹⁴ Advanced age correlates with weakened pelvic floor muscle tone and neurogenic degeneration, impairing bladder-urethral innervation and increasing UI risk.¹⁵ Patients with larger prostate volumes often present with chronic urinary obstruction and pronounced middle lobe hyperplasia, which complicates surgical anastomosis. Increased anastomotic tension between the bladder and urethra further elevates PPUI risk.¹⁶

The membranous urethra, spanning 1.5–2.0 cm in males, is critical for urinary continence. It traverses the external urethral sphincter (EUS)—a striated muscle innervated by the pudendal nerve (S2–S4)—and integrates with the levator ani and endopelvic fascia to form a functional continence zone that stabilises urethral resistance during intra-abdominal pressure changes.^17-19 During LRP, dissection near the prostatic apex shortens the MUL, destabilising the EUS and impairing continence. Preoperative MUL <12 mm, particularly when further reduced intraoperatively, compromises sphincteric coaptation and urethral tension, significantly increasing UI risk.¹⁸ Preoperative MRI or transrectal ultrasound measurement of MUL is essential; MUL >15 mm predicts faster continence recovery (≥90% at 6 months), whereas MUL <10 mm correlates with prolonged incontinence.¹⁸

BNP during LRP aims to maintain urethral musculature and sphincter integrity through different technical approaches. The intrafascial BNP technique preserves both the detrusor apron and periurethral smooth muscle, demonstrating improved early continence rates (70-80% at 3 months) compared to non-BNP approaches,²⁰ while partial BNP involving selective posterior bladder neck resection carries potential risks of damaging trigonal innervation and increasing anastomotic tension.²¹ Current evidence shows conflicting outcomes regarding BNP efficacy, with Ficarra et al. reporting superior 12-month continence rates with intrafascial BNP (85% versus 65% without BNP),²² whereas Dev et al. found no significant difference, suggesting that outcomes may depend more on surgeon expertise and careful patient selection.²³ Recent advances in surgical techniques include complete urethral sparing, which preserves the intraprostatic urethra up to the verumon-tanum and has shown promising results with over 50% of patients achieving immediate continence after catheter removal.²⁰ In contrast, bladder neck resection during LRP significantly disrupts urethral musculature, increases the technical difficulty of the anastomosis, and substantially elevates the risk of PPUI.²⁴ These findings highlight the importance of surgical technique selection and the need for individualised approaches in prostate cancer surgery.

This study developed a robust nomogram-based risk prediction model (C-index: 0.988; AUC: 0.974) incorporating MUL, prostate volume, BNP status, and age, with corresponding clinical interventions. These interventions include preoperative pelvic floor muscle training (PFMT) for elderly patients to enhance muscle coordination, meticulous surgical planning for cases with large prostates or short MUL, emphasising blunt prostatic apex dissection and preservation of the external urethral sphincter complex, tailored BNP techniques to optimise postoperative continence, and comprehensive postoperative care involving PFMT adherence education, lifestyle modifications, and follow-up monitoring. Contrary to the previous reports, BMI did not emerge as a significant predictor (p >0.05) in analysis. This may be attributable to confounding by age and prostate volume,²⁵ the mitigating effects of standardised modern LRP techniques on obesity-related surgical challenges,¹⁹ and the compensatory benefits of aggressive PFMT in obese populations. Furthermore, BMI's inherent limitations in distinguishing fat mass from lean muscle mass, and its failure to account for visceral adiposity—a more relevant factor in pelvic floor dysfunction further—diminish its predictive utility. The model showed high discriminative ability in both the training (C-index = 0.988) and validation sets (C-index = 0.923). The slight decrease in the validation set is expected due to overfitting mitigation through boot-strap resampling, confirming the model's generalisability.

This study, however, has several limitations. Firstly, being a single-centre retrospective study, the generalisability of its findings and predictive models may be constrained, necessitating careful interpretation when applied to other medical centres. Secondly, the retrospective data collection may have resulted in missing or incomplete information on certain potentially important variables (e.g., precise adherence to pelvic floor muscle exercises, patients' psychological status). Thirdly, despite including a sample size of 210 cases with internal validation, the model still requires external validation in independent, multicentre prospective cohorts to confirm its generalisability and robustness. Lastly, future research should focus on validating and optimising this model in multicentre prospective designs, incorporating more comprehensive variables (including rehabilitation adherence and psychosocial factors), and assessing its practical value in guiding clinical decision-making. This partitioning strategy ensured independent validation of the model, with the validation set showing only a 6.6% reduction in C-index (0.988 → 0.923), indicating minimal overfitting despite the single-centre design.

CONCLUSION

Short MUL, large prostate volume, non-BNP neck, and advanced age are key risk factors for PPUI. The nomogram model demonstrates high predictive accuracy but requires external validation due to its limitations in sample size and single-centre design. Future multicentre studies should refine risk stratification and explore novel interventions to reduce UI incidence.

FUNDING:
The general project contract for the young and middle-aged Scientific Research Fund at the Affiliated Hospital of Qinghai University, Xining, China (No: ASRF-2022-YB-05).

ETHICAL APPROVAL:
The study protocol received ethical approval from the Institutional Review Board of Qinghai University Affiliated Hospital, Xining, China (Approval No. P-SL-2022110).

PATIENTS’ CONSENT:
Written informed consent was taken from all the study patients.

COMPETING INTEREST:
The authors declared no conflict of interest.

AUTHORS’ CONTRIBUTION:
ZQ: Collected the data, performed data analysis, and wrote the paper.
BZ, SC: Contributed to the interpretation of the data and critical revision of the manuscript.
MW: Designed the study and reviewed the manuscript.
All authors approved the final version of the manuscript to be published.

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