Prediction of Incontinence after Robot-Assisted Radical Prostatectomy: Development and Validation of a 24-Month Incontinence Nomogram

Pinkhasov, Ruben M.; Lee, Timothy J.; Huang, Rogerio; Berkley, Bonnie; Pinkhasov, Alexandr; Dodge, Nicole; Loecher, Matthew S.; James, Gaybrielle; Pop, Elena; Attwood, Kristopher; Mohler, James L.

doi:10.3390/cancers14071644

Cited by 14 publications

(12 citation statements)

References 39 publications

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“…Many papers have predictive factors for recovery of urinary continence post prostatectomy, with old age, less nerve-sparing and high BMI resulting in worse outcomes [ 2 – 4 , 9 , 14 ]. Nomograms predicting recovery of urinary continence post-RALP have been designed at 4- and 12-weeks [ 31 ] and 6-, 12- and 24-month [ 32 ] follow-up points. Pre-operative predictive measures of continence such as mean urethral length (MUL) have been described since 2002 when Coakley et al .…”

Section: Discussionmentioning

confidence: 99%

Systematic review on urinary continence rates after robot-assisted laparoscopic radical prostatectomy

Geraghty,

Keane,

Davis

2024

Ir J Med Sci

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Objective The objective was to investigate the current evidence and discern urinary continence rates post robot-assisted laparoscopic radical prostatectomy (RALP). Methods A systematic review of the literature was carried out, searching the Embase, Scopus and PubMed databases between 1 January 2000 and 1 May 2020. The search terms “Robotic prostatectomy AND continence” were employed. Articles were selected in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Statistical analysis was performed using the programme R; cumulative analysis of percentage of men continent was calculated. Results A total of 3101 abstracts and 50 full text articles were assessed, with 22 publications included (n = 2813 patients). There were 21 randomised controlled trials and one partly randomised controlled trial with four publications comparing RALP to other prostate cancer treatments. Thirteen studies explored different RALP techniques, and five studies examined vesicourethral anastomosis (VUA). There were statistically significant improvements in early urinary continence rates in three studies analysing reconstructive techniques (83% vs 60%, p = 0.04; 26.5% vs 15.4%, p = 0.016; 77% vs 44.1%, p ≤ 0.001). Long-term continence rates were not significantly improved across all studies assessing reconstruction. One study comparing RALP vs laparoscopic radical prostatectomy (LRP) demonstrated a statistically significant improvement in continence at 3 months (80% vs 73.3%, p < 0.001); 6 months (83.3% vs 81.4%, p < 0.001); 12 months (95% vs 83.3%, p < 0.001) and 24 months (96.7% vs 85%, p < 0.001). Early continence was less favourable for RALP when compared to brachytherapy (BT) patients at 3 months (86% vs 98.7%, p < 0.05) and 6 months (90.5% vs 98.7%, p < 0.05). Conclusion Early continence rates were improved across numerous techniques in RALP. These results were not translated into significantly improved long-term outcomes. Continence rates following RALP were favourable compared to LRP, similar to ORP and less favourable compared to BT. Our findings suggest that post-RALP continence can be further improved with alterations in robotic technique.

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Section: Discussionmentioning

confidence: 99%

Systematic review on urinary continence rates after robot-assisted laparoscopic radical prostatectomy

Geraghty,

Keane,

Davis

2024

Ir J Med Sci

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“…Their dataset includes clinical and demographic data of 680 men and their LR model achieved AUCs between 0.64 and 0.80 across 24 months. No overlap in predictors was found and the study lacked external validation ( 24 ).…”

Section: Discussionmentioning

confidence: 99%

A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer

et al. 2023

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IntroductionUrinary incontinence (UI) is a common side effect of prostate cancer treatment, but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown promising results in predicting outcomes, yet the lack of transparency in complex models known as “black-box” has made clinicians wary of relying on them in sensitive decisions. Therefore, finding a balance between accuracy and explainability is crucial for the implementation of ML models. The aim of this study was to employ three different ML classifiers to predict the probability of experiencing UI in men with localized prostate cancer 1-year and 2-year after treatment and compare their accuracy and explainability. MethodsWe used the ProZIB dataset from the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) which contained clinical, demographic, and PROM data of 964 patients from 65 Dutch hospitals. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms were applied to predict (in)continence after prostate cancer treatment. ResultsAll models have been externally validated according to the TRIPOD Type 3 guidelines and their performance was assessed by accuracy, sensitivity, specificity, and AUC. While all three models demonstrated similar performance, LR showed slightly better accuracy than RF and SVM in predicting the risk of UI one year after prostate cancer treatment, achieving an accuracy of 0.75, a sensitivity of 0.82, and an AUC of 0.79. All models for the 2-year outcome performed poorly in the validation set, with an accuracy of 0.6 for LR, 0.65 for RF, and 0.54 for SVM. ConclusionThe outcomes of our study demonstrate the promise of using non-black box models, such as LR, to assist clinicians in recognizing high-risk patients and making informed treatment choices. The coefficients of the LR model show the importance of each feature in predicting results, and the generated nomogram provides an accessible illustration of how each feature impacts the predicted outcome. Additionally, the model’s simplicity and interpretability make it a more appropriate option in scenarios where comprehending the model’s predictions is essential.

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“…The identification of the key factors (age, race, height and weight, lifestyle) that predict presentation of UI and recovery of urinary control after RP could be a very useful tool in clinical practice. 47 , 116 In this sense the development of easy-to-use nomograms allows the urologists to create a better therapeutic pathway for the continence recovery after RP. 47 , 116 …”

Section: Introductionmentioning

confidence: 99%

“… 47 , 116 In this sense the development of easy-to-use nomograms allows the urologists to create a better therapeutic pathway for the continence recovery after RP. 47 , 116 …”

Section: Introductionmentioning

confidence: 99%

Management of Urinary Incontinence Following Radical Prostatectomy: Challenges and Solutions

Castellan¹,

Ferretti²,

Litterio³

et al. 2023

TCRM

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Urinary incontinence is a common and debilitating problem in patients undergoing radical prostatectomy. Current methods developed to treat urinary incontinence include conservative treatments, such as lifestyle education, pelvic muscle floor training, pharmacotherapy, and surgical treatments, such as bulking agents use, artificial urinary sphincter implants, retrourethral transobturator slings, and adjustable male sling system. Pelvic floor muscle exercise is the most common management to improve the strength of striated muscles of the pelvic floor to try to recover the sphincter weakness. Antimuscarinic drugs, phosphodiesterase inhibitors, duloxetine, and a-adrenergic drugs have been proposed as medical treatments for urinary incontinence after radical prostatectomy. Development of new surgical techniques, new surgical tools and materials, such as male slings, has provided an improvement of outcomes after UI surgery. Such improvement is still ongoing, and the uptake of new devices might lead to even better outcomes after UI surgery.

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Prediction of Incontinence after Robot-Assisted Radical Prostatectomy: Development and Validation of a 24-Month Incontinence Nomogram

Cited by 14 publications

References 39 publications

Systematic review on urinary continence rates after robot-assisted laparoscopic radical prostatectomy

Systematic review on urinary continence rates after robot-assisted laparoscopic radical prostatectomy

A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer

Management of Urinary Incontinence Following Radical Prostatectomy: Challenges and Solutions

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