There are a variety of surgical management strategies to help surgeons repair pelvic organ prolapse (POP). Surgical treatment for POP includes native tissue repair, augmentation with mesh, and minimally invasive surgeries. Currently, laparoscopic or robotic techniques for POP repair are increasing in popularity and continuing to evolve. The aim of this review is to present an up-to-date review of surgical techniques used for POP repair and to discuss ways to optimize surgical outcomes.

We aimed to investigate the association of obesity with nocturia using a nationally representative sample of adults from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2012. A total of 14,135 participants were included in this study. We performed a multivariate logistic regression analysis to find the odds ratio (OR) of obesity for nocturia. Furthermore, the OR of BMI for nocturia was analyzed using restricted cubic splines (RCS) with five knots. We conducted subgroup analysis according to age, sex, hypertension, and diabetes mellitus (DM) and further analysis with 1:1 matching data with propensity score. The participants who had body mass index (BMI) above 30 kg/m2 had a significantly higher OR for nocturia (OR, 1.39; 95% CI, 1.28-1.50) than those without obesity. RCS showed a dose-dependent relationship between BMI and OR for nocturia. Subgroup analysis by age, sex, hypertension, and DM showed similar results. Further analysis with 1:1 matching data showed a significant association of obesity with the prevalence of nocturia (OR, 1.25; 95% CI, 1.10-1.41). This study reported that obesity was significant association with the prevalence of nocturia with dose-dependent manner, regardless of age, sex, hypertension, and DM after taking major confounding factors into account.

Bang S, et al. Investig Clin Urol. 2022 May;63(3):301-308. https://doi.org/10.4111/icu.20210434

Abdominal sacrocolpopexy is the gold-standard treatment for apical compartment prolapse, as it is more effective and durable than the transvaginal approach. In the current era of minimally invasive surgery, laparoscopic sacrocolpopexy techniques have been described, but have not gained popularity due to their complexity and steep learning curves. To overcome this problem, robotic sacrocolpopexy was introduced, and has shown equivalent outcomes and safety compared to open and laparoscopic sacrocolpopexy based on findings that have been accumulated over 15 years.

The management of postprostatectomy urinary incontinence (PPI) is still challenging for urologists. In recent decades, various kinds of male sling system have been developed and introduced; however, they have not yet shown as good a result as that of artificial urinary sphincter (AUS). However, a male sling is still in an important position because patients have a high demand for sling implantation, and it can allow the avoidance of the use of mechanical devices like AUS. Recently, the male sling has been widely used in mild-to-moderate PPI patients; however, there are no studies that compare individual devices. Thus, it is hard to directly compare the success rate of operation, and it is impossible to judge which sling system is more excellent. It is expected that many sling options will be available in addition to AUS in the near future with the technological development of various male slings and the accumulation of long-term surgical outcomes. In that in patients with PPI, sling implantation is an option that must be explained rather than an option that need not be explained to them, this review would share the latest outcomes and complications.

Purpose: This study aimed to develop and evaluate machine learning models, specifically CatBoost and extreme gradient boosting (XGBoost), for diagnosing lower urinary tract symptoms (LUTS) in male patients. The objective is to differentiate between bladder outlet obstruction (BOO) and detrusor underactivity (DUA) using a comprehensive dataset that includes patient-reported outcomes, uroflowmetry measurements, and ultrasound-derived features.Methods: The dataset used in this study was collected from male patients aged 40 and older who presented with LUTS and sought treatment at the urology department of Samsung Medical Center. We developed and trained CatBoost and XGBoost models using this dataset. These models incorporated features like prostate size, voiding parameters, and responses from questionnaires. Their performance was assessed using standard metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC).Results: The results indicated that the CatBoost models displayed greater sensitivity, rendering them effective for initial screenings by accurately identifying true positive cases. Conversely, the XGBoost models showed higher specificity and precision, making them more suitable for confirming diagnoses and reducing false positives. In terms of overall performance for both BOO and DUA, XGBoost surpassed CatBoost, achieving an AUROC of 0.826 and 0.819, respectively.Conclusions: Integrating these machine learning models into the diagnostic workflow for LUTS can significantly enhance clinical decision-making by offering noninvasive, cost-effective, and patient-friendly diagnostic alternatives. The combined application of CatBoost and XGBoost models has the potential to improve diagnostic accuracy and provide customized treatment plans for patients, ultimately leading to better clinical outcomes.