PURPOSE: To determine the duration of antimuscarinic therapy for overactive bladder syndrome (OAB) appropriate for assessment of the efficacy of treatment, and to evaluate the possible predictive factors for response to therapy. METHODS: All OAB patients who visited a urology outpatient clinic of a tertiary referral center and who were prescribed 5 mg of solifenacin or 4 mg of tolterodine extended release capsules daily were enrolled in the study. Patients were asked to continue therapy for 6 months. All enrolled patients completed the patient perception of bladder condition, overactive bladder symptom score (OABSS), and the modified Indevus Urgency Severity Scale questionnaires. All patients underwent uroflowmetry. RESULTS: A total of 164 patients were enrolled and 125 patients (76%) had at least one follow-up visit. The mean follow-up interval was 1 month (range, 0.5-6 months). Sixty-two patients (49.6%; 95% confidence interval [CI], 40.7-58.5) responded to antimuscarinic treatment. The median time for the onset of response was 3 months (95% CI, 1-6). Multivariate Cox proportional-hazards model revealed that elevated baseline OABSS was an independent predictor of responsiveness to therapy. Receiver operating characteristic (ROC) curve analysis revealed an optimal OABSS cutoff value of > or =7, with an area under the ROC curve of 0.79 (95% CI, 0.70-0.88; sensitivity, 91.9%; specificity, 60.7%). CONCLUSIONS: The median time for a therapeutic response was 3 months, and OABSS was the only predictor for responsiveness. These findings may serve as a guideline when prescribing antimuscarinic treatment for OAB patients.
Ambulatory Care Facilities ; Capsules ; Cholinergic Antagonists ; Follow-Up Studies ; Humans ; ROC Curve ; Sensitivity and Specificity ; Solifenacin Succinate ; Tertiary Care Centers ; Tolterodine Tartrate ; Treatment Outcome ; Urinary Bladder ; Urinary Bladder, Overactive* ; Urology

PURPOSE: To identify factors predicting the presence of overactive bladder syndrome (OAB)-wet, compared with OAB-dry. METHODS: Between September 2007 and September 2013, the medical records of 623 women with OAB who completed a 3-day bladder diary and underwent urodynamic studies in a medical center were retrospectively reviewed. OAB-wet was diagnosed in patients who complained of at least one episode of urgency incontinence in the previous month; otherwise, OAB-dry was diagnosed. Multivariable logistic regression analysis was used to predict the presence of OAB-wet. RESULTS: Age (odds ratio [OR], 1.05; P<0.001), maximal flow rate (Qmax) (OR,1.06; P<0.001), voided volume (OR, 0.996; P=0.001), detrusor pressure at maximal flow rate (PdetQmax) (OR, 1.02; P=0.003), urgency episodes (OR, 1.04; P<0.001) and urodynamic stress incontinence (OR,1.78; P=0.01) were independent predictors for the presence of OAB-wet vs. OAB-dry. If we use bladder contractility index as a variable for multivariable logistic regression analysis, bladder contractility index (OR, 1.012; P<0.001) become an independent predictor for OAB-wet. CONCLUSIONS: A smaller bladder capacity and more frequent urgency episodes were predictors of OAB-wet, and the above findings indicate that OAB-wet and OAB-dry might be a continuum of OAB. Old age, high Qmax, high PdetQmax and urodynamic stress incontinence were also predictors for OAB-wet, and the above results reveal that OAB-wet and OAB-dry have partially different clinical and urodynamic features. Further studies might be performed to elucidate whether different treatment strategies between OAB-dry and OAB-wet can improve treatment efficacy.
Female ; Humans ; Logistic Models ; Medical Records ; Retrospective Studies ; Treatment Outcome ; Urinary Bladder ; Urinary Bladder, Overactive ; Urodynamics

Purpose: The Q-tip test is used to measure urethral hypermobility and can predict surgical outcomes. However, certain factors may affect the reliability of this test. Our aim was to identify independent clinical and urodynamic predictors of the results of the Q-tip test. Methods: Between January 2014 and June 2019, 176 consecutive women with lower urinary tract symptoms who underwent the Q-tip test and urodynamic studies were included in this retrospective study. Results: Multivariable regression analysis revealed that age (regression coefficient, -0.55), point Ba (regression coefficient, 4.1), urodynamic stress incontinence (regression coefficient, 9.9), maximum flow rate (Qmax) (regression coefficient, 0.13), pressure transmission ratio (PTR) at maximum urethral pressure (MUP) (regression coefficient, -0.14), and the score on the fifth question of the Incontinence Impact Questionnaire (IIQQ5; “Has urine leakage affected your participation in social activities outside your home?”; regression coefficient, -4.1) were independent predictors of the Q-tip angle, with a constant of 87.0. The following Spearman rank correlation coefficients were found between the Q-tip angle and the following variables: age, -0.38; point Ba, 0.34; urodynamic stress incontinence, 0.32; Qmax, 0.28; PTR at MUP, -0.28; and IIQQ5, -0.23. A receiver operating characteristic curve (ROC) analysis for the prediction of urodynamic stress incontinence found that the optimum cutoff for PTR at MUP was <81%, with an area under the ROC curve of 0.70. Conclusions Age, point Ba, urodynamic stress incontinence, Qmax, PTR at MUP, and IIQQ5 were independent predictors of the Q-tip angle. However, none of these could be used as effective surrogates for the Q-tip test due to their lack of a sufficient correlation.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.