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.

Purpose: To confirm the clinical features of hemicentral retinal vein occlusion and identify predictors of visual outcomes. Methods: A retrospective analysis was conducted on patients diagnosed with hemicentral retinal vein occlusion between January 2018 and December 2022 and followed for more than 6 months. Patients underwent intravitreal injections as necessary for intraretinal edema. Visual acuity, central macular thickness, ellipsoid zone damage, and the location of inner retinal layer edema were assessed. Patients were categorized into groups A and group B based on the visual acuity at 6 months. Results: In total, 20 eyes were followed, with 15 eyes observed for up to 12 months. Seven patients (35.0%) had diabetes and 11 (55.0%) had hypertension. There was a correlation between poor vision at 6 months and hypertension (p = 0.033). The visual acuity of all patients improved from a logMAR of 0.96 at the initial visit to a logMAR of 0.35 at 6 months (p = 0.005). In the group with good initial visual acuity, there were no significant changes in visual acuity during the follow-up period (p = 0.444). The group with good visual acuity at 6 months had a lower degree of photoreceptor ellipsoid zone disruption compared to the group with poor initial vision, indicating a normal structure (p = 0.015). Conclusions During follow-up of patients with hemicentral retinal vein occlusion, overall visual acuity improved over time. Patients with good initial acuity maintained it. Favorable visual outcomes can be expected if the ellipsoid zone has a normal structure at the time of the first examination.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Purpose: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.

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.

Purpose: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.