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Non-invasive clinical parameters for the prediction of urodynamic bladder outlet obstruction: analysis using causal Bayesian networks.

Kim M, Cheeti A, Yoo C, Choo M, Paick JS, Oh SJ - PLoS ONE (2014)

Bottom Line: The LR model showed a similar accuracy (77.0%).However, the area under the receiver operating characteristic curve of the CBN model was statistically smaller than that of the LR model (0.772 vs. 0.798, p = 0.020).Our study demonstrated that TPV, Qmax, and PVR are independent predictors of urodynamic BOO.

View Article: PubMed Central - PubMed

Affiliation: Department of Urology, Seoul National University Hospital, Seoul, Korea.

ABSTRACT

Purpose: To identify non-invasive clinical parameters to predict urodynamic bladder outlet obstruction (BOO) in patients with benign prostatic hyperplasia (BPH) using causal Bayesian networks (CBN).

Subjects and methods: From October 2004 to August 2013, 1,381 eligible BPH patients with complete data were selected for analysis. The following clinical variables were considered: age, total prostate volume (TPV), transition zone volume (TZV), prostate specific antigen (PSA), maximum flow rate (Qmax), and post-void residual volume (PVR) on uroflowmetry, and International Prostate Symptom Score (IPSS). Among these variables, the independent predictors of BOO were selected using the CBN model. The predictive performance of the CBN model using the selected variables was verified through a logistic regression (LR) model with the same dataset.

Results: Mean age, TPV, and IPSS were 6.2 (±7.3, SD) years, 48.5 (±25.9) ml, and 17.9 (±7.9), respectively. The mean BOO index was 35.1 (±25.2) and 477 patients (34.5%) had urodynamic BOO (BOO index ≥40). By using the CBN model, we identified TPV, Qmax, and PVR as independent predictors of BOO. With these three variables, the BOO prediction accuracy was 73.5%. The LR model showed a similar accuracy (77.0%). However, the area under the receiver operating characteristic curve of the CBN model was statistically smaller than that of the LR model (0.772 vs. 0.798, p = 0.020).

Conclusions: Our study demonstrated that TPV, Qmax, and PVR are independent predictors of urodynamic BOO.

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Causal Bayesian network model for bladder outlet obstruction.TPV, total prostate volume; TZV, transitional zone volume; PSA, prostatic specific antigen; BOO, bladder outlet obstruction; Qmax, maximum flow rate; PVR, post-void residual volume; IPSS, International Prostate Symptom Score.
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pone-0113131-g001: Causal Bayesian network model for bladder outlet obstruction.TPV, total prostate volume; TZV, transitional zone volume; PSA, prostatic specific antigen; BOO, bladder outlet obstruction; Qmax, maximum flow rate; PVR, post-void residual volume; IPSS, International Prostate Symptom Score.

Mentions: Based on the BPH patient data, the best network structure was selected/learned using the CBN model (Fig. 1). TPV, Qmax, and PVR exhibited direct relationships with BOO. Therefore, those three variables were selected as non-invasive independent predictors of BOO. The correlation coefficient was the highest for TPV (R = 0.391 p<0.001), followed by Qmax (R = −0.253, p<0.001) and PVR (R = 0.214, p<0.001).


Non-invasive clinical parameters for the prediction of urodynamic bladder outlet obstruction: analysis using causal Bayesian networks.

Kim M, Cheeti A, Yoo C, Choo M, Paick JS, Oh SJ - PLoS ONE (2014)

Causal Bayesian network model for bladder outlet obstruction.TPV, total prostate volume; TZV, transitional zone volume; PSA, prostatic specific antigen; BOO, bladder outlet obstruction; Qmax, maximum flow rate; PVR, post-void residual volume; IPSS, International Prostate Symptom Score.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4232562&req=5

pone-0113131-g001: Causal Bayesian network model for bladder outlet obstruction.TPV, total prostate volume; TZV, transitional zone volume; PSA, prostatic specific antigen; BOO, bladder outlet obstruction; Qmax, maximum flow rate; PVR, post-void residual volume; IPSS, International Prostate Symptom Score.
Mentions: Based on the BPH patient data, the best network structure was selected/learned using the CBN model (Fig. 1). TPV, Qmax, and PVR exhibited direct relationships with BOO. Therefore, those three variables were selected as non-invasive independent predictors of BOO. The correlation coefficient was the highest for TPV (R = 0.391 p<0.001), followed by Qmax (R = −0.253, p<0.001) and PVR (R = 0.214, p<0.001).

Bottom Line: The LR model showed a similar accuracy (77.0%).However, the area under the receiver operating characteristic curve of the CBN model was statistically smaller than that of the LR model (0.772 vs. 0.798, p = 0.020).Our study demonstrated that TPV, Qmax, and PVR are independent predictors of urodynamic BOO.

View Article: PubMed Central - PubMed

Affiliation: Department of Urology, Seoul National University Hospital, Seoul, Korea.

ABSTRACT

Purpose: To identify non-invasive clinical parameters to predict urodynamic bladder outlet obstruction (BOO) in patients with benign prostatic hyperplasia (BPH) using causal Bayesian networks (CBN).

Subjects and methods: From October 2004 to August 2013, 1,381 eligible BPH patients with complete data were selected for analysis. The following clinical variables were considered: age, total prostate volume (TPV), transition zone volume (TZV), prostate specific antigen (PSA), maximum flow rate (Qmax), and post-void residual volume (PVR) on uroflowmetry, and International Prostate Symptom Score (IPSS). Among these variables, the independent predictors of BOO were selected using the CBN model. The predictive performance of the CBN model using the selected variables was verified through a logistic regression (LR) model with the same dataset.

Results: Mean age, TPV, and IPSS were 6.2 (±7.3, SD) years, 48.5 (±25.9) ml, and 17.9 (±7.9), respectively. The mean BOO index was 35.1 (±25.2) and 477 patients (34.5%) had urodynamic BOO (BOO index ≥40). By using the CBN model, we identified TPV, Qmax, and PVR as independent predictors of BOO. With these three variables, the BOO prediction accuracy was 73.5%. The LR model showed a similar accuracy (77.0%). However, the area under the receiver operating characteristic curve of the CBN model was statistically smaller than that of the LR model (0.772 vs. 0.798, p = 0.020).

Conclusions: Our study demonstrated that TPV, Qmax, and PVR are independent predictors of urodynamic BOO.

Show MeSH
Related in: MedlinePlus