Background/Aims: Fexuprazan, a novel potassium-competitive acid blocker, was developed for treating acid-related disorders. Pharmacokinetic and pharmacodynamic properties of fexuprazan, unlike those of proton pump inhibitors, are independent of food effect. This study aims to evaluate differences in efficacy and safety of fexuprazan in patients with erosive esophagitis (EE) according to the timing of dosing. Methods: In this multicenter, open-label noninferiority study, patients who had typical reflux symptoms with endoscopically confirmed EE were randomized 1:1 to receive fexuprazan 40 mg daily 30 minutes before or after meal. Treatment was completed after 2 weeks or 4 weeks when healing was endoscopically confirmed. The primary endpoint was the proportion of patients with healed EE confirmed by endoscopy up to week 4. Safety endpoints included treatment-emergent adverse events (TEAEs). Results: In the prior-to-meal group (n = 89) and after-meal group (n = 86), 4-week EE healing rates were 98.77% and 100.00% (difference, 0.01%; 95% CI, –0.01% to 0.04%) and 2-week EE healing rates were 95.77% and 97.14% (difference, 0.01%; 95% CI, –0.05% to 0.07%), respectively. TEAEs were 9.78% and 8.70% in the prior-to-meal group and the after-meal group, respectively. Conclusions Non-inferiority analysis revealed that taking fexuprazan after meal was non-inferior to taking fexuprazan before meals in patients with EE. The frequency of adverse events was similar between the 2 study groups. The drug is safe and effective for healing EE regardless of the timing of dosing.

Objectives: Lonely death is defined as “a person living in a state of social isolation, disconnected from family, relatives, and others, who dies from suicide, illness, or other causes”. This study investigated the characteristics of individuals who die alone in Korea. Methods: We constructed a database of lonely death cases by linking data from the Korea Crime Scene Investigation Unit of the Korea National Police Agency with National Health Insurance Service (NHIS) records. A descriptive analysis was performed to evaluate the demographics, underlying diseases, and healthcare utilization patterns among lonely death cases. Results: Among the 3122 individuals identified as lonely death cases, 2621 (84.0%) were male and 501 (16.0%) were female. The most common age group was 50-59 years (n=930, 29.8%). The NHIS covered 2161 individuals (69.2%), whereas 961 individuals (30.8%) were enrolled in Medical Aid (MA). The highest number of lonely deaths occurred in Seoul areas, with 1468 cases (47.0%). Mood disorders were diagnosed in 1020 individuals (32.7%), and various alcohol-related diseases, including alcoholic liver disease, were also observed. Outpatient visits increased leading up to death but declined in the final 3 months, while hospitalizations decreased and emergency room visits slightly increased. Conclusions Most lonely death cases involved male in their 50s, with a disproportionately high number of MA beneficiaries compared to the general population. Many of these individuals also experienced mental health issues or alcohol-related disorders. Preventing social isolation and strengthening social safety nets are critical to reducing the occurrence of lonely deaths.

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: Fexuprazan, a novel potassium-competitive acid blocker, was developed for treating acid-related disorders. Pharmacokinetic and pharmacodynamic properties of fexuprazan, unlike those of proton pump inhibitors, are independent of food effect. This study aims to evaluate differences in efficacy and safety of fexuprazan in patients with erosive esophagitis (EE) according to the timing of dosing. Methods: In this multicenter, open-label noninferiority study, patients who had typical reflux symptoms with endoscopically confirmed EE were randomized 1:1 to receive fexuprazan 40 mg daily 30 minutes before or after meal. Treatment was completed after 2 weeks or 4 weeks when healing was endoscopically confirmed. The primary endpoint was the proportion of patients with healed EE confirmed by endoscopy up to week 4. Safety endpoints included treatment-emergent adverse events (TEAEs). Results: In the prior-to-meal group (n = 89) and after-meal group (n = 86), 4-week EE healing rates were 98.77% and 100.00% (difference, 0.01%; 95% CI, –0.01% to 0.04%) and 2-week EE healing rates were 95.77% and 97.14% (difference, 0.01%; 95% CI, –0.05% to 0.07%), respectively. TEAEs were 9.78% and 8.70% in the prior-to-meal group and the after-meal group, respectively. Conclusions Non-inferiority analysis revealed that taking fexuprazan after meal was non-inferior to taking fexuprazan before meals in patients with EE. The frequency of adverse events was similar between the 2 study groups. The drug is safe and effective for healing EE regardless of the timing of dosing.

Objectives: Lonely death is defined as “a person living in a state of social isolation, disconnected from family, relatives, and others, who dies from suicide, illness, or other causes”. This study investigated the characteristics of individuals who die alone in Korea. Methods: We constructed a database of lonely death cases by linking data from the Korea Crime Scene Investigation Unit of the Korea National Police Agency with National Health Insurance Service (NHIS) records. A descriptive analysis was performed to evaluate the demographics, underlying diseases, and healthcare utilization patterns among lonely death cases. Results: Among the 3122 individuals identified as lonely death cases, 2621 (84.0%) were male and 501 (16.0%) were female. The most common age group was 50-59 years (n=930, 29.8%). The NHIS covered 2161 individuals (69.2%), whereas 961 individuals (30.8%) were enrolled in Medical Aid (MA). The highest number of lonely deaths occurred in Seoul areas, with 1468 cases (47.0%). Mood disorders were diagnosed in 1020 individuals (32.7%), and various alcohol-related diseases, including alcoholic liver disease, were also observed. Outpatient visits increased leading up to death but declined in the final 3 months, while hospitalizations decreased and emergency room visits slightly increased. Conclusions Most lonely death cases involved male in their 50s, with a disproportionately high number of MA beneficiaries compared to the general population. Many of these individuals also experienced mental health issues or alcohol-related disorders. Preventing social isolation and strengthening social safety nets are critical to reducing the occurrence of lonely deaths.

Background/Aims: Fexuprazan, a novel potassium-competitive acid blocker, was developed for treating acid-related disorders. Pharmacokinetic and pharmacodynamic properties of fexuprazan, unlike those of proton pump inhibitors, are independent of food effect. This study aims to evaluate differences in efficacy and safety of fexuprazan in patients with erosive esophagitis (EE) according to the timing of dosing. Methods: In this multicenter, open-label noninferiority study, patients who had typical reflux symptoms with endoscopically confirmed EE were randomized 1:1 to receive fexuprazan 40 mg daily 30 minutes before or after meal. Treatment was completed after 2 weeks or 4 weeks when healing was endoscopically confirmed. The primary endpoint was the proportion of patients with healed EE confirmed by endoscopy up to week 4. Safety endpoints included treatment-emergent adverse events (TEAEs). Results: In the prior-to-meal group (n = 89) and after-meal group (n = 86), 4-week EE healing rates were 98.77% and 100.00% (difference, 0.01%; 95% CI, –0.01% to 0.04%) and 2-week EE healing rates were 95.77% and 97.14% (difference, 0.01%; 95% CI, –0.05% to 0.07%), respectively. TEAEs were 9.78% and 8.70% in the prior-to-meal group and the after-meal group, respectively. Conclusions Non-inferiority analysis revealed that taking fexuprazan after meal was non-inferior to taking fexuprazan before meals in patients with EE. The frequency of adverse events was similar between the 2 study groups. The drug is safe and effective for healing EE regardless of the timing of dosing.

Objectives: Lonely death is defined as “a person living in a state of social isolation, disconnected from family, relatives, and others, who dies from suicide, illness, or other causes”. This study investigated the characteristics of individuals who die alone in Korea. Methods: We constructed a database of lonely death cases by linking data from the Korea Crime Scene Investigation Unit of the Korea National Police Agency with National Health Insurance Service (NHIS) records. A descriptive analysis was performed to evaluate the demographics, underlying diseases, and healthcare utilization patterns among lonely death cases. Results: Among the 3122 individuals identified as lonely death cases, 2621 (84.0%) were male and 501 (16.0%) were female. The most common age group was 50-59 years (n=930, 29.8%). The NHIS covered 2161 individuals (69.2%), whereas 961 individuals (30.8%) were enrolled in Medical Aid (MA). The highest number of lonely deaths occurred in Seoul areas, with 1468 cases (47.0%). Mood disorders were diagnosed in 1020 individuals (32.7%), and various alcohol-related diseases, including alcoholic liver disease, were also observed. Outpatient visits increased leading up to death but declined in the final 3 months, while hospitalizations decreased and emergency room visits slightly increased. Conclusions Most lonely death cases involved male in their 50s, with a disproportionately high number of MA beneficiaries compared to the general population. Many of these individuals also experienced mental health issues or alcohol-related disorders. Preventing social isolation and strengthening social safety nets are critical to reducing the occurrence of lonely deaths.

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.