A novel algorithm for the non-invasive detection of bladder outlet obstruction in men with lower urinary tract symptoms

OBJECTIVE: To determine the ability of bladder wall thickness (BWT) in combination with non-invasive variables to distinguish patients with bladder outlet obstruction (BOO). PATIENTS AND METHODS: Patients completed the International Prostate Symptom Score (IPSS) questionnaire and prostate size was measured by transrectal ultrasonography (US). Pressure-flow studies were performed to determine the urodynamic diagnosis. BWT was measured at 250-mL bladder filling using transabdominal US. Recursive partition analysis (RPA) recursively partitions data for relating independent variable(s) to a dependent variable creating a tree of partitions. It finds a set of cuts of the dependent variable(s) that best predict the independent variable, by searching all possible cuts until the desired fit is reached. RPA was used to test the ability of the combined data of BWT, maximum urinary flow rate (Q(max)), post-void residual urine volume (PVR), IPSS, and prostate size to predict BOO. RESULTS: In all, 72 patients were included in the final analysis. The median BWT, voided volumes, PVR, mean Q(max), and IPSS were significantly higher in patients who had an Abrams/Griffiths (A/G) number of >40 (55 patients) compared to those with an A/G number of ≤40 (17 patients). RPA revealed that the combination of BWT and Q(max) gave a correct classification in 61 of the 72 patients (85%), with 92% sensitivity and 65% specificity, 87% positive predictive value, and 76% negative predictive value (NPV) for BOO (area under the curve 0.85). The positive diagnostic likelihood ratio of this reclassification fit was 2.6. CONCLUSIONS: It was possible to combine BWT with Q(max) to create a new algorithm that could be used as a screening tool for BOO in men with lower urinary tract symptoms.