OBJECTIVES: To define a uroflowmetry-based non-invasive predictive tool for the primary diagnosis of urethral stricture disease. METHODS: A total of 300 male patients (150 with urethral stricture disease and 150 with benign prostatic obstruction), treated surgically between 2005 and 2015, and 75 healthy males were included in this study. Patients were randomly assigned to one of two groups (75 benign prostatic obstruction patients and 75 urethral stricture disease patients in each group). A model, able to objectively differentiate between benign prostatic obstruction and urethral stricture disease, was created by use of original and hybrid uroflowmetry variables in logistic regression in group A (test group). Receiver operating characteristics curve analysis was used for validation in group B (validation group) and to calculate cut-off values to distinguish healthy individuals from benign prostatic obstruction as a result of urethral stricture disease. RESULTS: Receiver operating characteristics area under the curve values of the created model were 0.841 (0.777-0.905) and 0.807 (0.735-0.879) in groups A and B, respectively. Optimal cut-off values were 2.2847 and 0.1182 to distinguish healthy individuals versus benign prostatic obstruction and benign prostatic obstruction versus urethral stricture disease. CONCLUSIONS: A triphasic uroflowmetry-based model is able to objectively distinguish voiding patterns of healthy individuals, benign prostatic obstruction and urethral stricture disease. The probability of urethral stricture disease can be objectively calculated for each individual patient based on a non-invasive uroflowmetry test. Uroflowmetry pattern interpretation by use of statistical models could become a new standard.
RCT Entities:
OBJECTIVES: To define a uroflowmetry-based non-invasive predictive tool for the primary diagnosis of urethral stricture disease. METHODS: A total of 300 male patients (150 with urethral stricture disease and 150 with benign prostatic obstruction), treated surgically between 2005 and 2015, and 75 healthy males were included in this study. Patients were randomly assigned to one of two groups (75 benign prostatic obstructionpatients and 75 urethral stricture diseasepatients in each group). A model, able to objectively differentiate between benign prostatic obstruction and urethral stricture disease, was created by use of original and hybrid uroflowmetry variables in logistic regression in group A (test group). Receiver operating characteristics curve analysis was used for validation in group B (validation group) and to calculate cut-off values to distinguish healthy individuals from benign prostatic obstruction as a result of urethral stricture disease. RESULTS: Receiver operating characteristics area under the curve values of the created model were 0.841 (0.777-0.905) and 0.807 (0.735-0.879) in groups A and B, respectively. Optimal cut-off values were 2.2847 and 0.1182 to distinguish healthy individuals versus benign prostatic obstruction and benign prostatic obstruction versus urethral stricture disease. CONCLUSIONS: A triphasic uroflowmetry-based model is able to objectively distinguish voiding patterns of healthy individuals, benign prostatic obstruction and urethral stricture disease. The probability of urethral stricture disease can be objectively calculated for each individual patient based on a non-invasive uroflowmetry test. Uroflowmetry pattern interpretation by use of statistical models could become a new standard.