Purpose: This study was conducted to evaluate the efficacy of bladder outlet surgery in patients with detrusor underactivity (DU) and to identify factors associated with successful outcomes. Methods: We conducted a retrospective review of men diagnosed with DU in urodynamic studies who underwent bladder outlet surgery for lower urinary tract symptoms between May 2018 and April 2023. The International Prostate Symptom Score (IPSS) questionnaire, uroflowmetry (UFM), and multichannel urodynamic studies were administered. Successful treatment outcomes were defined as either an IPSS improvement of at least 50% or the regaining of spontaneous voiding in patients urethral catheterization prior to surgery. Results: The study included 93 male patients. Men diagnosed with significant or equivocal bladder outlet obstruction (BOO) experienced significant postoperative improvements in IPSS (from 20.6 to 6.0 and from 17.4 to 6.5, respectively), maximum urine flow rate (from 5.0 mL/sec to 14.4 mL/sec and from 8.8 mL/sec to 12.2 mL/sec, respectively) and voiding efficiency (from 48.8% to 86.0% and from 61.2% to 85.1%, respectively). However, in the group without obstruction, the improvements in IPSS and UFM results were not significant. The presence of detrusor overactivity (odds ratio [OR], 3.152; P=0.025) and preoperative urinary catheterization (OR, 2.756; P=0.040) were associated with favorable treatment outcomes. Conversely, an unobstructed bladder outlet was identified as a negative prognostic factor. Conclusions In men with DU accompanied by equivocal or significant BOO, surgical intervention to alleviate the obstruction may enhance the IPSS, quality of life, and UFM results. However, those with DU and an unobstructed bladder outlet face a comparatively high risk of treatment failure. Preoperative detrusor overactivity and urinary catheterization are associated with more favorable surgical outcomes. Consequently, active deobstructive surgery should be considered for patients with DU who are experiencing urinary retention.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.