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.

The lack of clear definition and classification for “moderate ulcerative colitis (UC)” creates ambiguity regarding the suitability of step-up versus top-down treatment approaches. In this paper, experts address crucial gaps in assessing and managing moderate UC. The Asia-Pacific, Middle East, and Africa Inflammatory Bowel Disease Coalition comprised 24 experts who convened to share, discuss and vote electronically on management recommendations for moderate UC. Experts emphasized that the goal of treating UC is to attain clinical, biomarker, and endoscopic remission using cost-effective strategies such as 5-aminosalicylates (5-ASAs), well-tolerated therapy that can be optimized to improve outcomes. Experts agreed that 5-ASA therapy could be optimized by maximizing dosage (4 g/day for induction of remission), combining oral and topical administration, extending treatment duration beyond 8 weeks, and enhancing patient adherence through personalized counselling and reduced pill burden. Treatment escalation should ideally be reserved for patients with predictors of aggressive disease or those who do not respond to 5-ASA optimization. Premature treatment escalation to advanced therapies (including biologics and oral small molecules) may have long-term health and financial consequences. This paper provides consensus-based expert recommendations and a treatment algorithm, based on current evidence and practices, to assist decision-making in real-world settings.

The lack of clear definition and classification for “moderate ulcerative colitis (UC)” creates ambiguity regarding the suitability of step-up versus top-down treatment approaches. In this paper, experts address crucial gaps in assessing and managing moderate UC. The Asia-Pacific, Middle East, and Africa Inflammatory Bowel Disease Coalition comprised 24 experts who convened to share, discuss and vote electronically on management recommendations for moderate UC. Experts emphasized that the goal of treating UC is to attain clinical, biomarker, and endoscopic remission using cost-effective strategies such as 5-aminosalicylates (5-ASAs), well-tolerated therapy that can be optimized to improve outcomes. Experts agreed that 5-ASA therapy could be optimized by maximizing dosage (4 g/day for induction of remission), combining oral and topical administration, extending treatment duration beyond 8 weeks, and enhancing patient adherence through personalized counselling and reduced pill burden. Treatment escalation should ideally be reserved for patients with predictors of aggressive disease or those who do not respond to 5-ASA optimization. Premature treatment escalation to advanced therapies (including biologics and oral small molecules) may have long-term health and financial consequences. This paper provides consensus-based expert recommendations and a treatment algorithm, based on current evidence and practices, to assist decision-making in real-world settings.

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.

The lack of clear definition and classification for “moderate ulcerative colitis (UC)” creates ambiguity regarding the suitability of step-up versus top-down treatment approaches. In this paper, experts address crucial gaps in assessing and managing moderate UC. The Asia-Pacific, Middle East, and Africa Inflammatory Bowel Disease Coalition comprised 24 experts who convened to share, discuss and vote electronically on management recommendations for moderate UC. Experts emphasized that the goal of treating UC is to attain clinical, biomarker, and endoscopic remission using cost-effective strategies such as 5-aminosalicylates (5-ASAs), well-tolerated therapy that can be optimized to improve outcomes. Experts agreed that 5-ASA therapy could be optimized by maximizing dosage (4 g/day for induction of remission), combining oral and topical administration, extending treatment duration beyond 8 weeks, and enhancing patient adherence through personalized counselling and reduced pill burden. Treatment escalation should ideally be reserved for patients with predictors of aggressive disease or those who do not respond to 5-ASA optimization. Premature treatment escalation to advanced therapies (including biologics and oral small molecules) may have long-term health and financial consequences. This paper provides consensus-based expert recommendations and a treatment algorithm, based on current evidence and practices, to assist decision-making in real-world settings.

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.

This video presents a case of L4–5 unstable spondylolisthesis treated with full-endoscopic transforaminal lumbar interbody fusion (Endo-TLIF), emphasizing the GUARD (Glider Used as a Rotary Device) technique for nerve root protection. This innovative approach involves controlled rotation of the cage glider before cage insertion to minimize the risk of nerve root injury, a significant complication in Endo-TLIF procedures. The GUARD technique, validated in previous cadaveric studies, provides enhanced safety during cage insertion by protecting the nerve root. A 48-year-old woman with a 3-year history of progressive low back pain and bilateral lower extremity radiculopathy (right-sided predominance) was diagnosed with L4–5 unstable spondylolisthesis and spinal stenosis. After failure of conservative management, she underwent uniportal full-endoscopic facet-resecting transforaminal lumbar interbody fusion using the GUARD technique. Postoperatively, the patient experienced significant symptomatic improvement and resolution of radiculopathy, without any intraoperative nerve root injury or postoperative neurological deficits. This case demonstrates the effectiveness of the GUARD technique in reducing neurological complications and improving patient outcomes.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

This video presents a case of L4–5 unstable spondylolisthesis treated with full-endoscopic transforaminal lumbar interbody fusion (Endo-TLIF), emphasizing the GUARD (Glider Used as a Rotary Device) technique for nerve root protection. This innovative approach involves controlled rotation of the cage glider before cage insertion to minimize the risk of nerve root injury, a significant complication in Endo-TLIF procedures. The GUARD technique, validated in previous cadaveric studies, provides enhanced safety during cage insertion by protecting the nerve root. A 48-year-old woman with a 3-year history of progressive low back pain and bilateral lower extremity radiculopathy (right-sided predominance) was diagnosed with L4–5 unstable spondylolisthesis and spinal stenosis. After failure of conservative management, she underwent uniportal full-endoscopic facet-resecting transforaminal lumbar interbody fusion using the GUARD technique. Postoperatively, the patient experienced significant symptomatic improvement and resolution of radiculopathy, without any intraoperative nerve root injury or postoperative neurological deficits. This case demonstrates the effectiveness of the GUARD technique in reducing neurological complications and improving patient outcomes.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.