Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

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.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Objectives: This study aimed to present the Asia-Pacific consensus on long-term and sequential therapy for osteoporosis, offering evidence-based recommendations for the effective management of this chronic condition.The primary focus is on achieving optimal fracture prevention through a comprehensive, individualized approach. Methods: A panel of experts convened to develop consensus statements by synthesizing the current literature and leveraging clinical expertise. The review encompassed long-term anti-osteoporosis medication goals, first-line treatments for individuals at very high fracture risk, and the strategic integration of anabolic and anti resorptive agents in sequential therapy approaches. Results: The panelists reached a consensus on 12 statements. Key recommendations included advocating for anabolic agents as the first-line treatment for individuals at very high fracture risk and transitioning to anti resorptive agents following the completion of anabolic therapy. Anabolic therapy remains an option for in dividuals experiencing new fractures or persistent high fracture risk despite antiresorptive treatment. In cases of inadequate response, the consensus recommended considering a switch to more potent medications. The consensus also addressed the management of medication-related complications, proposing alternatives instead of discontinuation of treatment. Conclusions This consensus provides a comprehensive, cost-effective strategy for fracture prevention with an emphasis on shared decision-making and the incorporation of country-specific case management systems, such as fracture liaison services. It serves as a valuable guide for healthcare professionals in the Asia-Pacific region, contributing to the ongoing evolution of osteoporosis management.

Background/Aims: Direct‐acting antivirals (DAAs) have been approved for hepatitis C virus (HCV) treatment in patients with end-stage renal disease (ESRD) on hemodialysis. Nevertheless, the complicated comedications and their potential drug-drug interactions (DDIs) with DAAs might limit clinical practice in this special population. Methods: The number, class, and characteristics of comedications and their potential DDIs with five DAA regimens were analyzed among HCV-viremic patients from 23 hemodialysis centers in Taiwan. Results: Of 2,015 hemodialysis patients screened in 2019, 169 patients seropositive for HCV RNA were enrolled (mean age, 65.6 years; median duration of hemodialysis, 5.8 years). All patients received at least one comedication (median number, 6; mean class number, 3.4). The most common comedication classes were ESRD-associated medications (94.1%), cardiovascular drugs (69.8%) and antidiabetic drugs (43.2%). ESRD-associated medications were excluded from DDI analysis. Sofosbuvir/velpatasvir/voxilaprevir had the highest frequency of potential contraindicated DDIs (red, 5.6%), followed by glecaprevir/pibrentasvir (4.0%), sofosbuvir/ledipasvir (1.3%), sofosbuvir/velpatasvir (1.3%), and elbasvir/grazoprevir (0.3%). For potentially significant DDIs (orange, requiring close monitoring or dose adjustments), sofosbuvir/velpatasvir/voxilaprevir had the highest frequency (19.9%), followed by sofosbuvir/ledipasvir (18.2%), glecaprevir/pibrentasvir (12.6%), sofosbuvir/velpatasvir (12.6%), and elbasvir/grazoprevir (7.3%). Overall, lipid-lowering agents were the most common comedication class with red-category DDIs to all DAA regimens (n=62), followed by cardiovascular agents (n=15), and central nervous system agents (n=10). Conclusions HCV-viremic patients on hemodialysis had a very high prevalence of comedications with a broad spectrum, which had varied DDIs with currently available DAA regimens. Elbasvir/grazoprevir had the fewest potential DDIs, and sofosbuvir/velpatasvir/voxilaprevir had the most potential DDIs.

Background/Aims: Obstacles exist in facilitating hepatitis C virus (HCV) care cascade. To increase timely and accurate diagnosis, disease awareness and accessibility, in-hospital HCV reflex testing followed by automatic appointments and a late call-back strategy (R.N.A. model) was applied. We aimed to compare the HCV treatment rate of patients treated with this strategy compared to those without. Methods: One hundred and twenty-five anti-HCV seropositive patients who adopted the R.N.A. model in 2020 and another 1,396 controls treated in 2019 were enrolled to compare the gaps in accurate HCV RNA diagnosis to final treatment allocation. Results: The HCV RNA testing rate was significantly higher in patients who received reflex testing than in those without reflex testing (100% vs. 84.8%, P<0.001). When patients were stratified according to the referring outpatient department, a significant improvement in the HCV RNA testing rate was particularly noted in patients from non-hepatology departments (100% vs. 23.3%, P<0.001). The treatment rate in HCV RNA seropositive patients was 83% (83/100) after the adoption of the R.N.A. model, among whom 96.1% and 73.9% of patients were from the hepatology and non-hepatology departments, respectively. Compared to subjects without R.N.A. model application, a significant improvement in the treatment rate was observed for patients from non-hepatology departments (73.9% vs. 27.8%, P=0.001). The application of the R.N.A. model significantly increased the in-hospital HCV treatment uptake from 6.4% to 73.9% for patients from non-hepatology departments (P<0.001). Conclusions The care cascade increased the treatment uptake and set up a model for enhancing in-hospital HCV elimination.

Background/Aims: Growth hormone (GH) is the main regulator of somatic growth, metabolism, and gender dimorphism in the liver. GH receptor (GHR) signaling in cancer is derived from a large body of evidence, although the GHR signaling pathway involved in the prognosis of hepatocellular carcinoma (HCC) in patients with hepatitis C virus (HCV)-related HCC, remains unclear. We aimed to explore the expression of GHR and analyze its association with clinicopathologic features and prognosis of patients with chronic hepatitis C and HCC. Methods: The expression of GHR mRNA was investigated by quantitative real-time polymerase chain reaction in paired tumors and adjacent non-tumorous (ANT) liver tissues of 200 patients with chronic hepatitis C and HCC. Western blotting and immunofluorescence assays using the HCV-infected Huh7.5.1 cell model was performed. Results: GHR mRNA was significantly lower in HCV-HCC tissues than in corresponding ANT liver tissues. GHR mRNA and protein levels also decreased in the HCV-infected Huh7.5.1 cell model. Notably, lower GHR expression was associated with age of >60 years (P=0.0111) and worse clinicopathologic characteristics, including alpha-fetoprotein >100 ng/mL (P=0.0403), cirrhosis (P=0.0075), vascular invasion (P=0.0052), pathological stage II–IV (P=0.0002), and albumin ≤4.0 g/dL (P=0.0055), which were linked with poor prognosis of HCC. Most importantly, the high incidence of recurrence and poor survival rates in patients with a low ratio of tumor/ANT GHR (≤0.1) were observed, indicating that low expression levels of GHR had great risk for development of HCC in patients with chronic hepatitis C. Conclusions Our study demonstrates a significant down-regulation of GHR expression as a new unfavorable independent prognostic factor in patients with chronic hepatitis C and HCC.

Background/Aims: Real-world studies assessing the effectiveness and safety of sofosbuvir/velpatasvir (SOF/VEL) plus ribavirin (RBV) for Child-Pugh B/C hepatitis C virus (HCV)-related cirrhosis are limited. Methods: We included 107 patients with Child-Pugh B/C HCV-related cirrhosis receiving SOF/VEL plus RBV for 12 weeks in Taiwan. The sustained virologic response rates at off-treatment week 12 (SVR12) for the evaluable population (EP), modified EP, and per-protocol population (PP) were assessed. Thesafety profiles were reported. Results: The SVR12 rates in the EP, modified EP and PP were 89.7% (95% confidence interval [CI], 82.5–94.2%), 94.1% (95% CI, 87.8–97.3%), and 100% (95% CI, 96.2–100%). Number of patients who failed to achieve SVR12 were attributed to virologic failures. The SVR12 rates were comparable regardless of patient characteristics. One patient discontinued treatment because of adverse events (AEs). Twenty-four patients had serious AEs and six died, but none were related to SOF/VEL or RBV. Among the 96 patients achieving SVR12, 84.4% and 64.6% had improved Child-Pugh and model for endstage liver disease (MELD) scores. Multivariate analysis revealed that a baseline MELD score ≥15 was associated with an improved MELD score of ≥3 (odds ratio, 4.13; 95% CI, 1.16–14.71; P=0.02). Patients with chronic kidney disease (CKD) stage 1 had more significant estimated glomerular filtration rate declines than patients with CKD stage 2 (-0.42 mL/min/1.73 m2/month; P=0.01) or stage 3 (-0.56 mL/min/1.73 m2/month; P<0.001). Conclusions SOF/VEL plus RBV for 12 weeks is efficacious and well-tolerated for Child-Pugh B/C HCV-related cirrhosis.