A unified consensus statement on medication-related osteonecrosis of the jaw (MRONJ) has not yet been established among the Asian member countries or regions of the Asian Federation of Osteoporosis Societies (AFOS). This study aimed to develop a consensus on MRONJ in patients with osteoporosis across these countries and regions. In this study, the term “Asia-Pacific” refers specifically to the Asian member countries and regions of AFOS. A structured survey consisting of nine MRONJ-related questions was distributed across 10 countries and regions to assess the level of agreement and summarize regional perspectives. In addition, a manual literature review and voting were conducted to evaluate the current evidence on MRONJ. The key aspects of MRONJ, including definition, staging, diagnosis, pathogenesis, risk factors, management, and prevention, were generally consistent among the AFOS countries and regions. The annual incidence and incidence rate of MRONJ associated with low-dose antiresorptive therapy in patients with osteoporosis ranged from 0.025% to 0.136% and 21 to 283 cases per 100,000 person-years, respectively. However, evidence regarding the benefits of drug discontinuation before dental surgery, such as tooth extraction, remains insufficient. Large-scale, multinational studies across AFOS countries and regions are warranted to determine the incidence of MRONJ better and evaluate the impact of antiresorptive drug discontinuation before dental procedures. These findings may contribute to the devel opment of effective evidence-based strategies for preventing MRONJ in patients with osteoporosis.

Objectives: . Idiopathic sudden sensorineural hearing loss (ISSNHL) is a significant cause of hearing loss. Intratympanic steroid injection (ITSI) is commonly used as an initial or salvage treatment; however, the lack of a standardized treatment protocol has resulted in variability in clinical practice. In addition, no efficient prediction model currently exists to support personalized management. Therefore, this study aimed to develop tailored management strategies for ISSNHL using a machine-learning model. Methods: . This retrospective multicenter cohort study was conducted between January 2015 and December 2020, with data analysis performed between January 2021 and March 2024. Patients were selected based on the International Classification of Diseases, 10th Revision criteria for ISSNHL, along with relevant medication and procedure codes. Patients with pure-tone audiogram results not meeting ISSNHL criteria, better initial hearing in the affected ear, an identifiable etiology, no post-treatment audiogram, or delayed treatment (>6 weeks) were excluded. We included 770 patients diagnosed with ISSNHL who received ITSI. The primary outcome was the area under the receiver operating characteristic curve for prediction performance. Recovery status was determined using the last pure-tone audiogram. Modeling was conducted on the Quanta for Medical Care AI platform using five machine-learning algorithms and a nested cross-validation framework, in which feature selection and hyperparameter tuning were performed in the inner folds and model performance was evaluated in the outer folds. Results: . A random forest classifier outperformed the other models in predicting hearing outcomes, achieving an area under the receiver operating characteristic curve of 0.788. Time to ITSI was the most influential treatment-related factor, with ITSI administered within 10 days of hearing loss being associated with better outcomes. This model can be used to provide personalized prognostic estimates under different treatment protocols. Conclusion . The machine-learning-based prediction model facilitates personalized treatment strategies and timely treatment adjustments for ISSNHL, thereby optimizing the likelihood of complete recovery.

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.

Atypical femoral fractures (AFFs) are a rare but serious complication of prolonged anti-resorptive therapy for osteoporosis. This study aimed to develop consensus-based recommendations for the clinical management of AFFs across the Asian Federation of Osteoporosis Societies (AFOS), for harmonizing practice and improving patient outcomes.A structured questionnaire covering ten key domains related to AFFs was distributed to expert representatives from the 10 AFOS member countries or regions. Responses were analyzed to identify areas of consensus and variation in regional practice. A concurrent narrative review of the literature was conducted to inform evidencebased recommendations. Survey responses were obtained from 8 of 10 participating AFOS member nations or regions. Among these, Thailand, Malaysia, South Korea, and Hong Kong reported established national guidelines or position statements on AFFs. Contributing risk factors include extended anti-resorptive therapy, femoral geometry, comorbidities, and specific pharmacologic exposures. Diagnosis depends on clinical suspicion and multimodal imaging, with high concordance in diagnostic criteria across regions. Screening emphasizes full-length femoral imaging in highrisk individuals. Incomplete AFFs are managed conservatively or with prophylactic fixation, while complete AFFs typically require intramedullary nailing, tailored to anatomic variations such as femoral bowing. Post-fracture care involves discontinuation of anti-resorptives, assessment for secondary osteoporosis, and potential initia tion of anabolic therapy, including teriparatide, abaloparatide, and romosozumab.This AFOS-led initiative highlights the importance for early detection, individualized management, and region-specific strategies. A multidisciplinary, patient-centered approach—encompassing risk assessment, im aging surveillance, surgical intervention, and tailored pharmacologic treatment—is crucial to reduce AFFs impact and improve skeletal health outcomes across Asia.

Objective: Endoscopic lumbar interbody fusion is a novel yet safe approach that provides a clear and expansive endoscopic view, facilitating the preservation of spinal structures. Whether biportal or uniportal incisions should be made for endoscopic interbody fusion is unclear. This study evaluated surgical efficacy and functional outcomes following biportal endoscopic transforaminal lumbar interbody fusion (BE-TLIF) with pedicle screw incisions for the treatment of lumbar spinal pathologies. Methods: Patients who underwent BE-TLIF between October 2019 and June 2023 with a minimum 12-month follow-up were retrospectively included. Functional outcomes were assessed using the visual analogue scale (VAS), Oswestry Disability Index (ODI), EuroQoL-5 Dimensions VAS (EQ5D-VAS), and modified MacNab criteria. Interbody fusion and cage subsidence rates were evaluated using lumbar x-rays. Results: In total, 21 patients (7 men and 14 women) with a mean age of 69.9 years (range, 41–90 years) were included, covering 23 total surgical levels. Compared to their preoperative values, 12-month postoperative mean back and leg VAS and ODI scores were significantly lower (back VAS, 3.3–0.9, p<0.001; leg VAS, 3.4–1.8, p<0.001; ODI, 18–2, p=0.049). In contrast, the EQ5D-VAS scores were significantly higher (79.8–98.2, p=0.009). Most patients (90%) achieved good or excellent modified MacNab criteria outcomes at the 12-month follow-up, and no serious complications were reported. The overall fusion and subsidence rates at 12 months were 87% and 13%, respectively. Conclusion BE-TLIF via pedicle screw incision provides favorable clinical outcomes, minimizes skin incisions, and is a feasible treatment option for lumbar spine pathologies.

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.