TY - JOUR
T1 - Non-invasive clinical parameters for the prediction of urodynamic bladder outlet obstruction
T2 - Analysis using causal Bayesian networks
AU - Kim, Myong
AU - Cheeti, Abhilash
AU - Yoo, Changwon
AU - Choo, Minsoo
AU - Paick, Jae Seung
AU - Oh, Seung June
N1 - Publisher Copyright:
© 2014 Kim et al.
PY - 2014/11/14
Y1 - 2014/11/14
N2 - 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 (67.3, SD) years, 48.5 (625.9) ml, and 17.9 (67.9), respectively. The mean BOO index was 35.1 (625.2) 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.
AB - 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 (67.3, SD) years, 48.5 (625.9) ml, and 17.9 (67.9), respectively. The mean BOO index was 35.1 (625.2) 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.
UR - http://www.scopus.com/inward/record.url?scp=84911914718&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0113131
DO - 10.1371/journal.pone.0113131
M3 - Article
C2 - 25397903
AN - SCOPUS:84911914718
SN - 1932-6203
VL - 9
JO - PLoS ONE
JF - PLoS ONE
IS - 11
M1 - e113131
ER -