Objective: This study aimed to evaluate the clinical outcomes and cost-effectiveness of dual-energy X-ray absorptiometry (DXA) for osteoporosis screening. Materials and Methods: Eligible patients who had and had not undergone DXA screening were identified from among those aged 50 years or older at Kaohsiung Veterans General Hospital, Taiwan. Age, sex, screening year (index year), and Charlson comorbidity index of the DXA and non-DXA groups were matched using inverse probability of treatment weighting (IPTW) for propensity score analysis. For cost-effectiveness analysis, a societal perspective, 1-year cycle length, 20-year time horizon, and discount rate of 2% per year for both effectiveness and costs were adopted in the incremental cost-effectiveness (ICER) model. Results: The outcome analysis included 10337 patients (female:male, 63.8%:36.2%) who were screened for osteoporosis in southern Taiwan between January 1, 2012, and December 31, 2021. The DXA group had significantly better outcomes than the non-DXA group in terms of fragility fractures (7.6% vs. 12.5%, P < 0.001) and mortality (0.6% vs. 4.3%, P < 0.001). The DXA screening strategy gained an ICER of US$ -2794 per quality-adjusted life year (QALY) relative to the non-DXA at the willingness-to-pay threshold of US$ 33004 (Taiwan’s per capita gross domestic product). The ICER after stratifying by ages of 50–59, 60–69, 70–79, and ≥ 80 years were US$ -17815, US$ -26862, US$ -28981, and US$ -34816 per QALY, respectively. Conclusion Using DXA to screen adults aged 50 years or older for osteoporosis resulted in a reduced incidence of fragility fractures, lower mortality rate, and reduced total costs. Screening for osteoporosis is a cost-saving strategy and its effectiveness increases with age. However, caution is needed when generalizing these cost-effectiveness results to all older populations because the study population consisted mainly of women.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.

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.