Purpose: Interstitial cystitis/bladder pain syndrome (IC/BPS) is mysterious and difficult to diagnose without cystoscopic hydrodistention. This study aimed to explore non-invasive and highly reliable urine biomarkers to identify Hunner’s IC (HIC) and different non-Hunner’s IC (NHIC) subtypes. Methods: In total, 422 women with and without clinically diagnosed IC/BPS (n = 376 and 46, respectively) were retrospectively enrolled. Patients were diagnosed with HIC or NHIC by cystoscopic hydrodistention under anesthesia. Then, the maximal bladder capacity (MBC) and glomerulation grade were determined. Thirteen urine inflammatory cytokines, chemokines, and oxidative stress biomarkers based on the previously reported predictors of IC/BPS were assayed using commercial microsphere kits. The dataset was randomly divided into training (70%) and test (30%) sets for model construction and validation using logistic regression and stepwise variable selection techniques. To construct the predictive models, univariate analysis was performed to evaluate the discriminative power of each urinary biomarker, measured by the area under the curve (AUC). Biomarkers with AUC values < 0.6 were excluded from further modeling. Multivariate logistic regression was then employed, with variables selected through stepwise forward selection based on log-likelihood criteria. For dichotomization, cutoff values were determined using quartile ranges from the control group. The final model’s performance was assessed using AUC, accuracy, sensitivity, and specificity in both training and test sets. Results: By setting the screening criterion to AUC ≥ 0.60, the potential urinary biomarkers for identifying IC/BPS cases were eotaxin, monocyte chemoattractant protein-1, tumor necrosis factor-alpha (TNF-α), 8-hydroxy-2′-deoxyguanosine (8-OHdG), and 8-isoprostane. Those for identifying HIC from the IC/BPS cohort were interleukin (IL)-6, IL-8, interferon γ-inducible protein 10 (IP-10), and regulated on activation, normal T-cell expressed and secreted (RANTES). A diagnostic algorithm using a cluster of urinary biomarkers included TNF-α ≥ 0.95 pg/mL or 8-OHDG ≥ 22.34 pg/mL and 8-isoprastane ≥ 22.34 pg/mL for identifying IC/BPS from the overall cohort; for identifying HIC from the IC/BPS cohort, the urinary IP-10 ≥ 3.74 pg/mL or IP-10 ≥ 19.94 pg/mL was added. Conclusions: Using a cluster of urinary biomarkers such as TNF-α or 8-OHdG and 8-isoprostane can identify IC/BPS from a study cohort, and adding the urinary IP-10 can distinguish HIC from IC/BPS cases.

Background: Diagnosing underlying causes of nonneurogenic male lower urinary tract symptoms associated with bladder outlet obstruction (BOO) is challenging. Video-urodynamic studies (VUDS) and pressure-flow studies (PFS) are both invasive diagnostic methods for BOO. VUDS can more precisely differentiate etiologies of male BOO, such as benign prostatic obstruction, primary bladder neck obstruction, and dysfunctional voiding, potentially outperforming PFS. Objective: These examinations’ invasive nature highlights the need for developing noninvasive predictive models to facilitate BOO diagnosis and reduce the necessity for invasive procedures. Methods: We conducted a retrospective study with a cohort of men with medication-refractory, nonneurogenic lower urinary tract symptoms suspected of BOO who underwent VUDS from 2001 to 2022. In total, 2 BOO predictive models were developed—1 based on the International Continence Society’s definition (International Continence Society–defined bladder outlet obstruction; ICS-BOO) and the other on video-urodynamic studies–diagnosed bladder outlet obstruction (VBOO). The patient cohort was randomly split into training and test sets for analysis. A total of 6 machine learning algorithms, including logistic regression, were used for model development. During model development, we first performed development validation using repeated 5-fold cross-validation on the training set and then test validation to assess the model’s performance on an independent test set. Both models were implemented as paper-based nomograms and integrated into a web-based artificial intelligence prediction tool to aid clinical decision-making. Results: Among 307 patients, 26.7% (n=82) met the ICS-BOO criteria, while 82.1% (n=252) were diagnosed with VBOO. The ICS-BOO prediction model had a mean area under the receiver operating characteristic curve (AUC) of 0.74 (SD 0.09) and mean accuracy of 0.76 (SD 0.04) in development validation and AUC and accuracy of 0.86 and 0.77, respectively, in test validation. The VBOO prediction model yielded a mean AUC of 0.71 (SD 0.06) and mean accuracy of 0.77 (SD 0.06) internally, with AUC and accuracy of 0.72 and 0.76, respectively, externally. When both models’ predictions are applied to the same patient, their combined insights can significantly enhance clinical decision-making and simplify the diagnostic pathway. By the dual-model prediction approach, if both models positively predict BOO, suggesting all cases actually resulted from medication-refractory primary bladder neck obstruction or benign prostatic obstruction, surgical intervention may be considered. Thus, VUDS might be unnecessary for 100 (32.6%) patients. Conversely, when ICS-BOO predictions are negative but VBOO predictions are positive, indicating varied etiology, VUDS rather than PFS is advised for precise diagnosis and guiding subsequent therapy, accurately identifying 51.1% (47/92) of patients for VUDS. Conclusions: The 2 machine learning models predicting ICS-BOO and VBOO, based on 6 noninvasive clinical parameters, demonstrate commendable discrimination performance. Using the dual-model prediction approach, when both models predict positively, VUDS may be avoided, assisting in male BOO diagnosis and reducing the need for such invasive procedures.

Purpose: The current study aimed to investigate the clinical comorbidities and urodynamic characteristics of a large cohort of women with dysfunctional voiding (DV) validated on a videourodynamic study (VUDS). Methods: Women who presented with VUDS-confirmed DV from 1998 to 2022 were retrospectively analyzed. Data on clinical symptoms, VUDS findings, and medical comorbidities including medical illness and previous surgical history were recorded and examined. Patients with DV were subgrouped according to age, presence of medical comorbidity, and different urodynamic parameters. The urodynamic parameters and treatment outcomes among the different subgroups were examined. Results: In total, 216 women were retrospectively analyzed. Among them, 188 (88.3%) presented with storage symptoms and 130 (61.0%) with voiding symptoms. Regarding outcomes, 48 (22.2%) patients had successful treatment outcomes; 76 (35.2%), improved outcomes; and 92 (42.6%), failed outcomes. Then, 150 (69.0%) patients presented with urodynamic DO. Patients with terminal DO experienced a significantly higher incidence of hypertension (56.8%), diabetes mellitus (37.9%), and latent central nervous system diseases (38.9%) than those with non-DO or phasic DO. Patients with phasic DO had a significantly higher detrusor pressure (Pdet) and bladder outlet obstruction index than those with non-DO and terminal DO. Patients with hypertension or those with a Pdet ≥ 35 cmH₂O had high rates of successful treatment outcomes. Conclusions: DV is significantly associated with older age and a higher incidence of central nervous system diseases, hypertension, and diabetes mellitus in women. Patients with phasic DO had a high Pdet and BOO, and patients with hypertension or those with Pdet ≥35 cm H₂O who received urethral sphincter treatment had a better treatment outcome.

Spinal cord injury (SCI) often leads to neurogenic lower urinary tract dysfunction, causing dysuria and affecting patients’ well-being. This study aimed to evaluate the efficacy of a urethral sphincter botulinum toxin A (BoNT-A) injection in patients with SCI and dysuria. This was a retrospective study including 118 patients with SCI who underwent a urethral BoNT-A injection following a standardized protocol for refractory voiding dysfunction. The protocol involved injecting BoNT-A into the urethral sphincter under cystoscopic guidance. Patient demographics, bladder condition parameters, and treatment outcomes were analyzed. Logistic regression and receiver operating characteristic curve analyses were performed to identify predictors of treatment success. Of the 118 patients, 71 (60.1%) showed satisfactory treatment outcomes after the injection. Post-injection status, bladder management, and injection frequency varied significantly among patients with satisfactory and unsatisfactory treatment outcomes. Age, bladder compliance, intravesical pressure, and bladder contractility were indicators of satisfactory outcomes. The first sensation of bladder filling of ≤263 mL, intravesical pressure of ≤28, and bladder contractility index of ≥14 were highly correlated with satisfactory outcomes. A urethral sphincter BoNT-A injection shows promise in managing dysuria in patients with SCI. Understanding bladder condition parameters and patient demographics helps optimize patient selection for this intervention. Further studies are needed to validate these findings and refine treatment protocols.

BACKGROUND Diagnosing underlying causes of nonneurogenic male lower urinary tract symptoms associated with bladder outlet obstruction (BOO) is challenging. Video-urodynamic studies (VUDS) and pressure-flow studies (PFS) are both invasive diagnostic methods for BOO. VUDS can more precisely differentiate etiologies of male BOO, such as benign prostatic obstruction, primary bladder neck obstruction, and dysfunctional voiding, potentially outperforming PFS. OBJECTIVE These examinations’ invasive nature highlights the need for developing noninvasive predictive models to facilitate BOO diagnosis and reduce the necessity for invasive procedures. METHODS We conducted a retrospective study with a cohort of men with medication-refractory, nonneurogenic lower urinary tract symptoms suspected of BOO who underwent VUDS from 2001 to 2022. In total, 2 BOO predictive models were developed—1 based on the International Continence Society’s definition (International Continence Society–defined bladder outlet obstruction; ICS-BOO) and the other on video-urodynamic studies–diagnosed bladder outlet obstruction (VBOO). The patient cohort was randomly split into training and test sets for analysis. A total of 6 machine learning algorithms, including logistic regression, were used for model development. During model development, we first performed development validation using repeated 5-fold cross-validation on the training set and then test validation to assess the model’s performance on an independent test set. Both models were implemented as paper-based nomograms and integrated into a web-based artificial intelligence prediction tool to aid clinical decision-making. RESULTS Among 307 patients, 26.7% (n=82) met the ICS-BOO criteria, while 82.1% (n=252) were diagnosed with VBOO. The ICS-BOO prediction model had a mean area under the receiver operating characteristic curve (AUC) of 0.74 (SD 0.09) and mean accuracy of 0.76 (SD 0.04) in development validation and AUC and accuracy of 0.86 and 0.77, respectively, in test validation. The VBOO prediction model yielded a mean AUC of 0.71 (SD 0.06) and mean accuracy of 0.77 (SD 0.06) internally, with AUC and accuracy of 0.72 and 0.76, respectively, externally. When both models’ predictions are applied to the same patient, their combined insights can significantly enhance clinical decision-making and simplify the diagnostic pathway. By the dual-model prediction approach, if both models positively predict BOO, suggesting all cases actually resulted from medication-refractory primary bladder neck obstruction or benign prostatic obstruction, surgical intervention may be considered. Thus, VUDS might be unnecessary for 100 (32.6%) patients. Conversely, when ICS-BOO predictions are negative but VBOO predictions are positive, indicating varied etiology, VUDS rather than PFS is advised for precise diagnosis and guiding subsequent therapy, accurately identifying 51.1% (47/92) of patients for VUDS. CONCLUSIONS The 2 machine learning models predicting ICS-BOO and VBOO, based on 6 noninvasive clinical parameters, demonstrate commendable discrimination performance. Using the dual-model prediction approach, when both models predict positively, VUDS may be avoided, assisting in male BOO diagnosis and reducing the need for such invasive procedures. CLINICALTRIAL