Background/Aims: We aim to compare the remission of erosive esophagitis (EE) among individuals with different phenotypes based on their metabolic health and obesity status and investigate the impact of changes in metabolic health on the EE remission. Methods: Asymptomatic adults (n = 16 845) with EE at baseline, who underwent follow-up esophagogastroduodenoscopy (EGD) were categorized into 4 groups as follows: metabolically healthy (MH) nonobese, metabolically unhealthy (MU) nonobese, MH obese, and MU obese. EE was defined as grade A or higher mucosal breaks observed using esophagogastroduodenoscopy. Results: During a median follow-up of 2.2 years, the remission rates of EE were 286.4/10 3 , 260.1/10 3 , 201.5/10 3 , and 219.9/10 3 person-years in MH nonobese, MU nonobese, MH obese, and MU obese groups, respectively. Multivariate-adjusted hazard ratios (95% CI) for EE remission among the MH nonobese, MU nonobese, and MH obese groups versus that of the MU obese group were 1.30 (1.23-1.37), 1.17 (1.12-1.23), and 0.98 (0.90-1.06), respectively, whereas those of the persistent MH, progression of MH to MU, and remission of MU to MH compared with the persistent MU group were 1.37 (1.23-1.52), 1.15 (1.01-1.30), and 1.28 (1.12-1.46), respectively.Increased EE remission in the persistent MH group was consistently observed in individuals with and without obesity (or abdominal obesity). Conclusions Metabolic health and nonobesity independently and favorably impact EE remission. Maintaining normal weight and healthy metabolic status may contribute to EE remission.

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/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: We aim to compare the remission of erosive esophagitis (EE) among individuals with different phenotypes based on their metabolic health and obesity status and investigate the impact of changes in metabolic health on the EE remission. Methods: Asymptomatic adults (n = 16 845) with EE at baseline, who underwent follow-up esophagogastroduodenoscopy (EGD) were categorized into 4 groups as follows: metabolically healthy (MH) nonobese, metabolically unhealthy (MU) nonobese, MH obese, and MU obese. EE was defined as grade A or higher mucosal breaks observed using esophagogastroduodenoscopy. Results: During a median follow-up of 2.2 years, the remission rates of EE were 286.4/10 3 , 260.1/10 3 , 201.5/10 3 , and 219.9/10 3 person-years in MH nonobese, MU nonobese, MH obese, and MU obese groups, respectively. Multivariate-adjusted hazard ratios (95% CI) for EE remission among the MH nonobese, MU nonobese, and MH obese groups versus that of the MU obese group were 1.30 (1.23-1.37), 1.17 (1.12-1.23), and 0.98 (0.90-1.06), respectively, whereas those of the persistent MH, progression of MH to MU, and remission of MU to MH compared with the persistent MU group were 1.37 (1.23-1.52), 1.15 (1.01-1.30), and 1.28 (1.12-1.46), respectively.Increased EE remission in the persistent MH group was consistently observed in individuals with and without obesity (or abdominal obesity). Conclusions Metabolic health and nonobesity independently and favorably impact EE remission. Maintaining normal weight and healthy metabolic status may contribute to EE remission.

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: We aim to compare the remission of erosive esophagitis (EE) among individuals with different phenotypes based on their metabolic health and obesity status and investigate the impact of changes in metabolic health on the EE remission. Methods: Asymptomatic adults (n = 16 845) with EE at baseline, who underwent follow-up esophagogastroduodenoscopy (EGD) were categorized into 4 groups as follows: metabolically healthy (MH) nonobese, metabolically unhealthy (MU) nonobese, MH obese, and MU obese. EE was defined as grade A or higher mucosal breaks observed using esophagogastroduodenoscopy. Results: During a median follow-up of 2.2 years, the remission rates of EE were 286.4/10 3 , 260.1/10 3 , 201.5/10 3 , and 219.9/10 3 person-years in MH nonobese, MU nonobese, MH obese, and MU obese groups, respectively. Multivariate-adjusted hazard ratios (95% CI) for EE remission among the MH nonobese, MU nonobese, and MH obese groups versus that of the MU obese group were 1.30 (1.23-1.37), 1.17 (1.12-1.23), and 0.98 (0.90-1.06), respectively, whereas those of the persistent MH, progression of MH to MU, and remission of MU to MH compared with the persistent MU group were 1.37 (1.23-1.52), 1.15 (1.01-1.30), and 1.28 (1.12-1.46), respectively.Increased EE remission in the persistent MH group was consistently observed in individuals with and without obesity (or abdominal obesity). Conclusions Metabolic health and nonobesity independently and favorably impact EE remission. Maintaining normal weight and healthy metabolic status may contribute to EE remission.

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.