A novel predictive model for noninvasively diagnosing bladder outlet obstruction in female patients based on clinical features and uroflowmetry parameters

Abstract

AbstractObjectiveTo develop and validate a simple prediction model to diagnose female bladder outlet obstruction (fBOO) because of the invasive nature of standard urodynamic studies (UDS) for diagnosing fBOO.MethodsWe retrospectively analyzed the data of 728 women who underwent UDS at Tongji Hospital between 2011 and 2021. The definition of fBOO was Pdet.Qmax − 2.2 × Qmax > 5 (BOOIf > 5). Independent predictive factors of fBOO were determined by multivariable logistic regression analysis. These predictive factors were incorporated into a predictive model to assess the risk of fBOO.ResultsOf the 728 patients, 249 (34.2%) were identified as having fBOO and these women were randomly assigned to two groups, a model development group and a model validation group. Multivariate logistic regression demonstrated that age, Qmax, flow time, and voiding efficiency were independent risk factors for fBOO. The predictive model of fBOO showed a satisfactory performance, with area under the curve being 0.811 (95% confidence interval [CI] 0.771–0.850, P < 0.001), which was confirmed to be 0.820 (95% CI 0.759–0.882, P < 0.001) with external validation. The calibration curve indicated that the predicted probability had an excellent correspondence to observed frequency. Decision curve analysis demonstrated a greater clinical net benefit compared with the strategies of treat all or treat none when the predicted risk was in a range of 3% and 75%.ConclusionA novel predictive model of fBOO was developed and validated based on clinical features and noninvasive test parameters in female patients with lower urinary tract symptoms. The model is a quick and easy‐to‐use tool to assess the risk of fBOO for urologists in their routine practice without an invasive UDS.