Objective: Involuntary admission to psychiatric inpatient care can protect both patients with severe mental illnesses and individuals around them. This study analyzed annual healthcare costs per person for involuntary psychiatric admission and examined categories of mental disorders and other factors associated with mortality. Methods: This retrospective cohort study collected 1 million randomly sampled beneficiaries from the National Health Insurance Database for 2002–2013. It identified and matched 181 patients with involuntary psychiatric admissions (research group) with 724 patients with voluntary psychiatric admissions (control group) through 1:4 propensity-score matching for sex, age, comorbidities, mental disorder category, and index year of diagnosis. Results: Mean life expectancy of patients with involuntary psychiatric admissions was 33.13 years less than the general population. Average annual healthcare costs per person for involuntary psychiatric admissions were 3.94 times higher compared with voluntary admissions. The general linear model demonstrated that average annual medical costs per person per compulsory hospitalization were 5.8 times that of voluntary hospitalization. Survival analysis using the Cox proportional hazards model found no significant association between type of psychiatric admission (involuntary or voluntary) and death. Conclusion This study revealed no significant difference in mortality between involuntary and voluntary psychiatric admissions, indicating involuntary treatment’s effectiveness.

Spinal dural arteriovenous fistula (SDAVF) is the most common spinal vascular malformation, however it is still rare and underdiagnosed. Magnetic resonance imaging findings such as spinal cord edema and dilated and tortuous perimedullary veins play a pivotal role in the confirmation of the diagnosis. However, spinal angiography remains the gold standard in the diagnosis of SDAVF. Classic angiographic findings of SDAVF are early filling of radicular veins, delayed venous return, and an extensive network of dilated perimedullary venous plexus. A series of angiograms of SDAVF at different locations along the spinal column, and mimics of serpentine perimedullary venous plexus on MR images, are demonstrated. Thorough knowledge of SDAVF aids correct diagnosis and prevents irreversible complications.
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Central Nervous System Vascular Malformations/*diagnosis/epidemiology/etiology ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Spinal Cord Diseases/diagnosis ; Spine/radiography

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.