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

Yu Cheng; Taicheng Li; Xiaoyu Wu; Guanghui Du; Shengfei Xu

doi:10.1002/ijgo.15430

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

Int J Gynaecol Obstet. 2024 Feb 28. doi: 10.1002/ijgo.15430. Online ahead of print.

Authors

Yu Cheng^{1

2}, Taicheng Li¹, Xiaoyu Wu¹, Guanghui Du¹, Shengfei Xu¹

Affiliations

¹ Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
² Department of Urology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.

PMID: 38415782
DOI: 10.1002/ijgo.15430

Abstract

Objective: To 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.

Methods: We retrospectively analyzed the data of 728 women who underwent UDS at Tongji Hospital between 2011 and 2021. The definition of fBOO was P_det.Qmax - 2.2 × Q_max > 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.

Results: Of 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, Q_max , 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%.

Conclusion: A 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.

Keywords: female bladder outlet obstruction; lower urinary tract symptoms; noninvasive; prediction model; urodynamic study; uroflowmetry.