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.

Objective: Bariatric surgery effectively treats non-alcoholic fatty liver disease (NAFLD). The glutamate-serine-glycine (GSG) index has emerged as a non-invasive diagnostic marker for NAFLD, but its ability to monitor treatment response remains unclear. This study investigates the GSG index's ability to monitor NAFLD's response to bariatric surgery. Methodology: Ten NAFLD participants were studied at baseline and 6 months post-bariatric surgery. Blood samples were collected for serum biomarkers and metabolomic profiling. Hepatic steatosis [proton density fat fraction (PDFF)] and fibroinflammation (cT1) were quantified with multiparametric magnetic resonance imaging (mpMRI), and hepatic stiffness with magnetic resonance elastography (MRE). Amino acids and acylcarnitines were measured with mass spectrometry. Statistical analyses included paired Student’s t-test, Wilcoxon-signed rank test, and Pearson’s correlation. Results: Eight participants provided complete data. At baseline, all had hepatic steatosis (BMI 39.3 ± 5.6 kg/m2, PDFF ≥ 5%). Post-surgery reductions in PDFF (from 12.4 ± 6.7% to 6.2 ± 2.8%, p = 0.013) and cT1 (from 823.3 ± 85.4ms to 757.5 ± 41.6ms, p = 0.039) were significant, along with the GSG index (from 0.272 ± 0.03 to 0.157 ± 0.05, p = 0.001). Conclusion The GSG index can potentially be developed as a marker for monitoring the response of patients with NAFLD to bariatric surgery.
Non-alcoholic Fatty Liver Disease ; Amino Acids ; Metabolomics

Hyperpolarized (HP) carbon-13 ( 13C) MRI represents an innovative approach for noninvasive, real-time assessment of dynamic metabolic flux, with potential integration into routine clinical MRI. The use of [1- 13C]pyruvate as a probe and its conversion to [1- 13C]lactate constitute an extensively explored metabolic pathway. This review comprehensively outlines the establishment of HP 13C-MRI, covering multidisciplinary team collaboration, hardware prerequisites, probe preparation, hyperpolarization techniques, imaging acquisition, and data analysis. This article discusses the clinical applications of HP 13C-MRI across various anatomical domains, including the brain, heart, skeletal muscle, breast, liver, kidney, pancreas, andprostate. Each section highlights the specific applications and findings pertinent to these regions, emphasizing the potential versatility of HP 13C-MRI in diverse clinical contexts. This review serves as a comprehensive update, bridging technical aspects with clinical applications and offering insights into the ongoing advancements in HP 13C-MRI.

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.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

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.