Background: This study evaluated the use of brainstem score (BSS) on pre-procedural diffusion-weighted imaging (DWI) to predict outcomes after mechanical thrombectomy (MT) in acute basilar artery occlusion (ABAO) patients and compared its predictive effectiveness to the critical area perfusion score (CAPS) on perfusion magnetic resonance imaging (MRI) using RAPID. Methods: This study focused on ABAO patients who underwent MT after MRI at Dong-A University Hospital from 2013 to 2023. Ischemic lesion volume and DWI BSS were measured for all. For the group that underwent perfusion MRI using RAPID, CAPS were measured. The primary end point was a poor outcome at 90 days (modified Rankin scale [mRS], >2). Results: 71 patients had ABAO and underwent MT after MRI. The poor outcome group (66.2%) had significantly larger ischemic lesion volume and higher DWI BSS compared with the good outcome group. In the multiple logistic regression analysis, DWI BSS (odds ratio, 8.27; 95% confidence interval, 1.93-35.50; p<0.01) was an independent predictor of poor outcomes. In 26 patients, CAPS was measured on perfusion MRI. In this subgroup, poor outcome group (50.0%) had higher DWI BSS and CAPS than the good outcome group. In the multiple logistic regression analysis, DWI BSS remained a valid independent predictor for predicting outcomes, but CAPS did not function as an independent predictor. Conclusion In this study, the DWI BSS before MT in ABAO patients emerged as a useful imaging marker for predicting post-procedural outcomes. Its predictive ability is not only comparable to but even superior to CAPS on perfusion MRI.

Background: This study evaluated the use of brainstem score (BSS) on pre-procedural diffusion-weighted imaging (DWI) to predict outcomes after mechanical thrombectomy (MT) in acute basilar artery occlusion (ABAO) patients and compared its predictive effectiveness to the critical area perfusion score (CAPS) on perfusion magnetic resonance imaging (MRI) using RAPID. Methods: This study focused on ABAO patients who underwent MT after MRI at Dong-A University Hospital from 2013 to 2023. Ischemic lesion volume and DWI BSS were measured for all. For the group that underwent perfusion MRI using RAPID, CAPS were measured. The primary end point was a poor outcome at 90 days (modified Rankin scale [mRS], >2). Results: 71 patients had ABAO and underwent MT after MRI. The poor outcome group (66.2%) had significantly larger ischemic lesion volume and higher DWI BSS compared with the good outcome group. In the multiple logistic regression analysis, DWI BSS (odds ratio, 8.27; 95% confidence interval, 1.93-35.50; p<0.01) was an independent predictor of poor outcomes. In 26 patients, CAPS was measured on perfusion MRI. In this subgroup, poor outcome group (50.0%) had higher DWI BSS and CAPS than the good outcome group. In the multiple logistic regression analysis, DWI BSS remained a valid independent predictor for predicting outcomes, but CAPS did not function as an independent predictor. Conclusion In this study, the DWI BSS before MT in ABAO patients emerged as a useful imaging marker for predicting post-procedural outcomes. Its predictive ability is not only comparable to but even superior to CAPS on perfusion MRI.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: This study evaluated the use of brainstem score (BSS) on pre-procedural diffusion-weighted imaging (DWI) to predict outcomes after mechanical thrombectomy (MT) in acute basilar artery occlusion (ABAO) patients and compared its predictive effectiveness to the critical area perfusion score (CAPS) on perfusion magnetic resonance imaging (MRI) using RAPID. Methods: This study focused on ABAO patients who underwent MT after MRI at Dong-A University Hospital from 2013 to 2023. Ischemic lesion volume and DWI BSS were measured for all. For the group that underwent perfusion MRI using RAPID, CAPS were measured. The primary end point was a poor outcome at 90 days (modified Rankin scale [mRS], >2). Results: 71 patients had ABAO and underwent MT after MRI. The poor outcome group (66.2%) had significantly larger ischemic lesion volume and higher DWI BSS compared with the good outcome group. In the multiple logistic regression analysis, DWI BSS (odds ratio, 8.27; 95% confidence interval, 1.93-35.50; p<0.01) was an independent predictor of poor outcomes. In 26 patients, CAPS was measured on perfusion MRI. In this subgroup, poor outcome group (50.0%) had higher DWI BSS and CAPS than the good outcome group. In the multiple logistic regression analysis, DWI BSS remained a valid independent predictor for predicting outcomes, but CAPS did not function as an independent predictor. Conclusion In this study, the DWI BSS before MT in ABAO patients emerged as a useful imaging marker for predicting post-procedural outcomes. Its predictive ability is not only comparable to but even superior to CAPS on perfusion MRI.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: The Korea Expert Committee on Immunization Practices (KECIP) is a key advisory body the government to develop guidelines and provide technical advisory activities on immunization policies in Korea. A recent policy study, inspired by global best practices, aims to enhance KECIP's functionality for providing timely and transparent recommendations in the face of evolving vaccine science and emerging infectious diseases like COVID-19. Methods: This study reviewed the current status of KECIP and collected expert opinions through surveys and consultations. Among the 40 panel members who were surveyed, 19 responded to a questionnaire specifically designed to assess the potential areas of improvement within KECIP. Results: The majority of respondents favored maintaining the current member count and emphasized the need for a subcommittee. Opinions varied on issues such as the length of KECIP’s term, the representation of vaccine manufacturers’ perspectives, and the chairperson’s role. However, there was a consensus on the importance of expertise, transparency, and fair proceedings within the committee. Conclusion This study underscores the pivotal role of KECIP in shaping national immunization policies, emphasizing the necessity for informed guidance amidst evolving vaccine science and emerging infectious diseases. Furthermore, it stressed the importance of enhancing KECIP’s capacity to effectively address evolving public health challenges and maintain successful immunization programs in South Korea.

Objective: : There is still controversy regarding whether neck remodeling stent affects the occurrence of silent embolic infarction (SEI) after aneurysm coiling. Thus, the aim of the present study is to investigate the incidence of SEI after stent-assisted coiling (SAC) using Neuroform Atlas Stent (NAS) and possible risk factors. This study also includes a comparison with simple coiling group during the same period to estimate the impact of NAS on the occurrence of SEI. Methods: : This study included a total of 96 unruptured intracranial aneurysms in 96 patients treated with SAC using NAS. Correlations of demographic data, aneurysm characteristics, and angiographic parameters with properties of SEI were analyzed. The incidence and characteristics of SEI were investigated in 28 patients who underwent simple coiling during the same period, and the results were compared with the SAC group. Results: : In the diffusion-weighted imaging obtained on the 1st day after SAC, a total of 106 SEI lesions were observed in 48 (50%) of 96 patients. Of these 48 patients, 38 (79.2%) had 1–3 lesions. Of 106 lesions, 74 (69.8%) had a diameter less than 3 mm. SEI occurred more frequently in older patients (≥60 years, p=0.013). The volume of SEI was found to be significantly increased in older age (≥60 years, p=0.032), hypertension (p=0.036), and aneurysm size ≥5 mm (p=0.047). The incidence and mean volume of SEI in the SAC group (n=96) were similar to those of the simple coiling group (n=28) during the same period. Conclusion : SEIs are common after NAS-assisted coiling. Their incidence in SAC was comparable to that in simple coiling. They occurred more frequently at an older age. Therefore, the use of NAS in the treatment of unruptured intracranial aneurysm does not seem to be associated with an increased risk of thromboembolic events if antiplatelet premedication has been performed well.