Int Neurourol J.  2014 Dec;18(4):198-205. 10.5213/inj.2014.18.4.198.

Factors Influencing Nonabsolute Indications for Surgery in Patients With Lower Urinary Tract Symptoms Suggestive of Benign Prostatic Hyperplasia: Analysis Using Causal Bayesian Networks

• Kim M
• Ramirez L
• Yoo C
• Choo M
• Paick JS
• Oh SJ

• KMID: 2232733
• DOI: http://doi.org/10.5213/inj.2014.18.4.198

Abstract

PURPOSE
To identify the factors affecting the surgical decisions of experienced physicians when treating patients with lower urinary tract symptoms that are suggestive of benign prostatic hyperplasia (LUTS/BPH).
METHODS
Patients with LUTS/BPH treated by two physicians between October 2004 and August 2013 were included in this study. The causal Bayesian network (CBN) model was used to analyze factors influencing the surgical decisions of physicians and the actual performance of surgery. The accuracies of the established CBN models were verified using linear regression (LR) analysis.
RESULTS
A total of 1,108 patients with LUTS/BPH were analyzed. The mean age and total prostate volume (TPV) were 66.2 (+/-7.3, standard deviation) years and 47.3 (+/-25.4) mL, respectively. Of the total 1,108 patients, 603 (54.4%) were treated by physician A and 505 (45.6%) were treated by physician B. Although surgery was recommended to 699 patients (63.1%), 589 (53.2%) actually underwent surgery. Our CBN model showed that the TPV (R=0.432), treating physician (R=0.370), bladder outlet obstruction (BOO) on urodynamic study (UDS) (R=0.324), and International Prostate Symptom Score (IPSS) question 3 (intermittency; R=0.141) were the factors directly influencing the surgical decision. The transition zone volume (R=0.396), treating physician (R=0.340), and BOO (R=0.300) directly affected the performance of surgery. Compared to the LR model, the area under the receiver operating characteristic curve of the CBN surgical decision model was slightly compromised (0.803 vs. 0.847, P<0.001), whereas that of the actual performance of surgery model was similar (0.801 vs. 0.820, P=0.063) to the LR model.
CONCLUSIONS
The TPV, treating physician, BOO on UDS, and the IPSS item of intermittency were factors that directly influenced decision-making in physicians treating patients with LUTS/BPH.

Keyword

Bayes Theorem; Decision Support Techniques; Decision Making, Computer-Assisted; Prostatic Hyperplasia; Urodynamics

MeSH Terms

Bayes Theorem
Decision Making, Computer-Assisted
Decision Support Techniques
Humans
Linear Models
Lower Urinary Tract Symptoms*
Prostate
Prostatic Hyperplasia*
ROC Curve
Urinary Bladder Neck Obstruction
Urodynamics