The current study aimed to develop predictive models based on noninvasive clinical parameters to facilitate the early identification and stratification of patients with suspected bladder outlet dysfunction (BOD), thereby reducing the need for invasive diagnostic procedures.

This retrospective study included 307 male patients with lower urinary tract symptoms (LUTS) refractory to medical therapy who were enrolled between January 2001 and May 2022. To assess the predictive performance of the model in an independent cohort, the dataset was randomly divided into the training set (70%) for model development and the test set (30%) for external validation. A two-stage modeling approach was adopted: Stage 1 involved detecting BOD, and stage 2 focused on identifying specific BOD subtypes. Backward stepwise logistic regression was conducted for model derivation, with internal validation performed using 5-fold cross-validation repeated 20 times. Clinical nomograms and a clinical decision-making framework were constructed based on the final modeling results.

In stage 1, the derived BOD model for detecting suspected BOD incorporated maximum flow rate, voided volume, intravesical prostatic protrusion (IPP), and prostatic urethral angle as predictors. In stage 2, the derived benign prostatic obstruction (BPO) model included post void residual, total prostate volume, and IPP as predictors (Table 1). Both internal and external validations had good discriminative ability for the derived BOD and BPO models. However, the BOD model (0.47) had a relatively low specificity, and the BPO model (0.58) had a lower sensitivity. Thus, these findings should be considered when applying the models in clinical practice (Figure 1).

The study results, for the first time, provide a reasonable two-stage model for predicting the presence of BOD and identifying BPO in male patients with LUTS and small to moderate prostate volume. For detecting BPO, the model achieved an accuracy rate of 82%, without the need for invasive urodynamic study, thereby supporting the use of the two-stage model decision-making framework. We also constructed nomogram to broadly screening BOD by the combination of Qmax, voided volume, IPP, and PUA, a total score of ≥107 yielded the probability of 0.78 to identify BOD of 0.78. Subsequently, by combining PVR, TPV, and IPP, a total score of ≥39 yielded the probability of 0.35 to discriminate BPO. With two-stage model decision-making framework, male patients with LUTS can be screened for BPO and appropriately treated. The other BOD subtypes may need videourodynamic study to clarify the underlying pathophysiology and treated.

This stepwise strategy, which applies the high-sensitivity BOD model for initial screening, followed by the high-specificity BPO model for subtype confirmation, effectively reduced the need for videourodynamic study by limiting it to patients with non-BPO phenotypes requiring further evaluation. The results of this study revealed that using the clinical non-invasive parameters to create models can only yield a low sensitivity and low specificity for identifying BPO and the other BOD subtype. In patients with LUTS and small to moderate prostate volume, invasive video urodynamic study is still necessary when invasive treatment modality is recommended.