Predicting Prostatic Obstruction and Bladder Outlet Dysfunction in Men with Lower Urinary Tract Symptoms and Small-to-Moderate Prostate Volume Using Noninvasive Diagnostic Tools

Objective: 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. Materials and Methods: 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. 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 (PUA) as predictors. In stage 2, the derived benign prostatic obstruction (BPO) model included post-void residual (PVR), total prostate volume (TPV), and IPP as predictors. We also constructed nomogram to broadly screening BOD by the combination of maximum flow rate, 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. 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. Conclusions: 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.