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/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: Sorafenib is the standard of care in the management of advanced hepatocellular carcinoma (HCC). The purpose of this study was to investigate the characteristics, treatment patterns and outcomes of sorafenib among HCC patients in South Korea. Methods: This population-based retrospective, single-arm, observational study used the Korean National Health Insurance database to identify patients with HCC who received sorafenib between July 1, 2008, and December 31, 2014. A total of 9,923 patients were recruited in this study. Results: Among 9,923 patients, 6,669 patients (68.2%) received loco-regional therapy prior to sorafenib, and 1,565 patients (15.8%) received combination therapy with concomitant sorafenib;2,591 patients (26.1%) received rescue therapy after sorafenib, and transarterial chemoembolization was the most common modality applied in 1,498 patients (15.1%). A total of 3,591 patients underwent rescue therapy after sorafenib, and the median overall survival was 14.5 months compared to 4.6 months in 7,332 patients who received supportive care after sorafenib. The mean duration of sorafenib administration in all patients was 105.7 days; 7,023 patients (70.8%) received an initial dose of 600 to 800 mg. The longest survival was shown in patients who received the recommended dose of 800 mg, subsequently reduced to 400 mg (15.0 months). The second longest survival was demonstrated in patients with a starting dose of 800 mg, followed by a dose reduction to 400–600 mg (9.6 months). Conclusions Real-life data show that the efficacy of sorafenib seems similar to that observed in clinical trials, suggesting that appropriate subsequent therapy after sorafenib might prolong patient survival.

Background/Aims: The recent update on Selecting Therapeutic Targets in Inflammatory Bowel Disease initiative has added a decrease in fecal calprotectin (FC) to an acceptable range as an intermediate target for Crohn’s disease (CD). We aimed to investigate whether postinduction FC could predict future persistent remission (PR) and endoscopic healing (EH) after 1 year of treatment with infliximab (IFX) in pediatric patients with CD. Methods: This multicenter retrospective observational study included pediatric patients with CD who were followed up for at least 1 year after starting IFX. The association of postinduction FC with PR and EH was investigated. Results: A total of 132 patients were included in this study. PR and EH were observed in 71.2% (94/132) and 73.9% (82/111) of the patients, respectively. In multivariate logistic regression analysis, only the postinduction FC level was associated with PR (odds ratio [OR], 0.26; 95% confidence interval [CI], 0.08 to 0.66; p=0.009). The FC levels at initiation of IFX and postinduction were significantly associated with EH (OR, 0.73; 95% CI, 0.53 to 0.99; p=0.044 and OR, 0.20; 95% CI, 0.06 to 0.49; p=0.002, respectively). According to the receiver operating characteristic curve analysis, the optimal cutoff level for postinduction FC associated with PR was 122 mg/kg, and that associated with EH was 377 mg/kg. Conclusions Postinduction FC was associated with PR and EH after 1 year of treatment with IFX in pediatric patients with CD. Our findings emphasize the importance of FC as an intermediate target in the treat-to-target era.

Background: Since the emergence of hypervirulent strains of Clostridioides difficile, the incidence of C. difficile infections (CDI) has increased significantly. Methods: To assess the incidence of CDI in Korea, we conducted a prospective multicentre observational study from October 2020 to October 2021. Additionally, we calculated the incidence of CDI from mass data obtained from the Health Insurance Review and Assessment Service (HIRA) from 2008 to 2020. Results: In the prospective study with active surveillance, 30,212 patients had diarrhoea and 907 patients were diagnosed with CDI over 1,288,571 patient-days and 193,264 admissions in 18 participating hospitals during 3 months of study period; the CDI per 10,000 patientdays was 7.04 and the CDI per 1,000 admission was 4.69. The incidence of CDI was higher in general hospitals than in tertiary hospitals: 6.38 per 10,000 patient-days (range: 3.25–12.05) and 4.18 per 1,000 admissions (range: 1.92–8.59) in 11 tertiary hospitals, vs. 9.45 per 10,000 patient-days (range: 5.68–13.90) and 6.73 per 1,000 admissions (range: 3.18–15.85) in seven general hospitals. With regard to HIRA data, the incidence of CDI in all hospitals has been increasing over the 13-year-period: from 0.3 to 1.8 per 10,000 patient-days, 0.3 to 1.6 per 1,000 admissions, and 6.9 to 56.9 per 100,000 population, respectively. Conclusion The incidence of CDI in Korea has been gradually increasing, and its recent value is as high as that in the United State and Europe. CDI is underestimated, particularly in general hospitals in Korea.

Background: We aimed to investigate the characteristics of pediatric ulcerative colitis (UC) at diagnosis in Korea. Methods: This was a multicenter, registry-based, inception cohort study conducted in Korea between 2021 and 2023. Children and adolescents newly diagnosed with UC < 18 years were included. Baseline clinicodemographics, results from laboratory, endoscopic exams, and Paris classification factors were collected, and associations between factors at diagnosis were investigated. Results: A total 205 patients with UC were included. Male-to-female ratio was 1.59:1, and the median age at diagnosis was 14.7 years (interquartile range 11.9–16.2). Disease extent of E1 comprised 12.2% (25/205), E2 24.9% (51/205), E3 11.2% (23/205), and E4 51.7% (106/205) of the patients. S1 comprised 13.7% (28/205) of the patients. The proportion of patients with a disease severity of S1 was significantly higher in patients with E4 compared to the other groups (E1: 0% vs. E2: 2% vs. E3: 0% vs. E4: 24.5%, P < 0.001). Significant differences between disease extent groups were also observed in Pediatric Ulcerative Colitis Activity Index (median 25 vs. 35 vs. 40 vs. 45, respectively, P < 0.001), hemoglobin (median 13.5 vs.13.2 vs. 11.6 vs. 11.4 g/dL, respectively, P < 0.001), platelet count (median 301 vs. 324 vs. 372 vs. 377 × 103 /μL, respectively, P = 0.001), C-reactive protein (median 0.05 vs. 0.10 vs. 0.17 vs. 0.38 mg/dL, respectively, P < 0.001), and Ulcerative Colitis Endoscopic Index of Severity (median 4 vs. 4 vs. 4 vs. 5, respectively, P = 0.006). No significant differences were observed in factors between groups divided according to sex and diagnosis age. Conclusion This study represents the largest multicenter pediatric inflammatory bowel disease cohort in Korea. Disease severity was associated with disease extent in pediatric patients with UC at diagnosis.

Purpose: For neonates admitted to the neonatal intensive care unit (NICU), appropriate nutritional assessment and intervention are important for adequate growth. In this study, we aimed to determine whether there were changes in the nutritional supply and growth status of premature infants hospitalized in the NICU after the introduction of the Nutrition support team (NST). Methods: This study retrospectively analyzed premature infants admitted to the NICU for over 14 days. The average daily calorie, protein, and fat supply at 1 and 2 weeks after birth were compared before and after NST, and growth was evaluated by changes in length, weight, and head circumference z-scores at birth and 28 days after birth. Results: A total of 79 neonates were included in the present study, with 32 in the preNST group and 47 in the post-NST group. The average daily energy supply during the first (p=0.001) and second (p=0.029) weeks postnatal was significantly higher in the post-NST group than in the pre-NST group. Lipid supply for the first week was significantly higher in the post-NST group than in the pre-NST group (p=0.010). The change in the z-score for length was significantly higher in the post-NST group than in the pre-NST group (p=0.049). Conclusion Nutrient supply and length z-score change increased significantly at 28 days after birth in the post-NST group. These results suggest that calorie calculators and NST activity can promote adequate growth and development in neonates.

Purpose: For neonates admitted to the neonatal intensive care unit (NICU), appropriate nutritional assessment and intervention are important for adequate growth. In this study, we aimed to determine whether there were changes in the nutritional supply and growth status of premature infants hospitalized in the NICU after the introduction of the Nutrition support team (NST). Methods: This study retrospectively analyzed premature infants admitted to the NICU for over 14 days. The average daily calorie, protein, and fat supply at 1 and 2 weeks after birth were compared before and after NST, and growth was evaluated by changes in length, weight, and head circumference z-scores at birth and 28 days after birth. Results: A total of 79 neonates were included in the present study, with 32 in the preNST group and 47 in the post-NST group. The average daily energy supply during the first (p=0.001) and second (p=0.029) weeks postnatal was significantly higher in the post-NST group than in the pre-NST group. Lipid supply for the first week was significantly higher in the post-NST group than in the pre-NST group (p=0.010). The change in the z-score for length was significantly higher in the post-NST group than in the pre-NST group (p=0.049). Conclusion Nutrient supply and length z-score change increased significantly at 28 days after birth in the post-NST group. These results suggest that calorie calculators and NST activity can promote adequate growth and development in neonates.