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: An international reference measurement laboratory network for creatinine (Cr) is lacking; therefore, Korea developed an independent evaluation and certification system. The in vitro diagnostics (IVD) certification program, launched in 2017, formed part of a broader Cr standardization initiative intended to enhance accuracy at the manufacturing stage. Methods: The program was designed to evaluate analytical systems, including all reagent lots, calibrators, and instrument models, twice annually. Bias, imprecision, total error (TE), and linearity were evaluated based on established acceptance criteria. A post-certification process allows submission for a second challenge and validation of corrective actions. Results: Between 2017 and 2023, 489 analytical systems were evaluated. Average acceptance rates for bias, imprecision, TE, and linearity were 70.8%, 95.9%, 87.7%, and 87.8%, respectively. The lowest acceptance rate for bias evaluation was 8.7% for the kinetic Jaffe method without compensation in 2018. Over the 7-year period, the mean absolute percentage bias (absBias%), coefficient of variation (CV), and TE were 4.62%, 1.37%,and 7.29%, respectively. The highest absBias% (7.94%) was observed in the 0.0 ≤ Cr < 1.0target value range. Since 2019, a consistent reduction in absBias% has been observed. Conclusions This program is a pioneering response to the absence of a global certification program for Cr assays. It offers significant advantages, including comprehensive evaluations, fee-free participation, and a robust post-certification process. Continuous participation and improvement efforts by manufacturers have contributed to enhanced accuracy in Cr assays.

Activating mutations in the epidermal growth factor receptor (EGFR) gene, particularly in the tyrosine kinase domain, occur in approximately 20% of non-small cell lung cancer (NSCLC) cases. These mutations, commonly found as deletions in exon 19 or the L858R mutation in exon 21, are crucial targets for EGFR tyrosine kinase inhibitors (TKIs). The third-generation EGFR TKI, osimertinib (TAGRISSO; AstraZeneca, UK), is effective against tumors with the T790M mutation and a liquid biopsy test has been approved by the US Food and Drug Administration as a companion diagnostic for TAGRISSO.This test is now widely implemented in various countries, including South Korea. To ensure high-quality testing, the Korean Association of External Quality Assessment Service launched a liquid biopsy EGFR proficiency testing program in 2017. This study analyzed the results of this program for 2017–2023. The number of participating laboratories increased from three in 2017 to 30 in 2023. All participating laboratories reported results using the Cobas z 480 analyzer (Roche Diagnostics, Germany). The accuracy of the test results was high with 100% correctness in several trials. However, errors were reported in some trials with false positives (e.g., detecting mutations in mutation-free samples) being more common than false negatives. This study evaluated Korean clinical laboratory performance when undertaking liquid biopsies for EGFR mutations. Continuous participation in the external quality assessment program helps laboratories maintain and improve the quality of liquid biopsy EGFR testing, ultimately benefiting patient care by ensuring accurate and reliable mutation detection.

Next-generation sequencing (NGS)-based liquid biopsy using peripheral blood offers a minimally invasive approach to detect tumor-derived circulating tumor DNA (ctDNA). Given the low abundance of ctDNA, accurate analysis is crucial, necessitating external quality assessments.Since 2020, the Korean Association of External Quality Assessment Service has conducted proficiency testing for NGS-based liquid biopsy. This study reviews the proficiency testing results from 2020 to 2023. The program was conducted biannually. Specimens were created by spiking fragmented DNA into fresh frozen plasma to simulate actual clinical samples. The number of target genes reported increased from 5 in 2020 to 17 in 2023. Results were assessed based on concordance with those obtained from targeted NGS panel testing performed before shipping the manufactured specimens.Participating laboratories used various NGS instruments and reagents. The read depth for each genetic variant varied across laboratories, while the reported read percentage of detected variants was generally consistent.Most laboratories accurately reported variants; however, some discrepancies related to variant position descriptions or incorrect reference sequence transcripts were noted. This study evaluates the performance of Korean clinical laboratories in NGS-based liquid biopsy. Continued vigilance in result reporting is necessary, and ongoing external quality assessments can enhance the reliability of NGS-based liquid biopsy testing.

Activating mutations in the epidermal growth factor receptor (EGFR) gene, particularly in the tyrosine kinase domain, occur in approximately 20% of non-small cell lung cancer (NSCLC) cases. These mutations, commonly found as deletions in exon 19 or the L858R mutation in exon 21, are crucial targets for EGFR tyrosine kinase inhibitors (TKIs). The third-generation EGFR TKI, osimertinib (TAGRISSO; AstraZeneca, UK), is effective against tumors with the T790M mutation and a liquid biopsy test has been approved by the US Food and Drug Administration as a companion diagnostic for TAGRISSO.This test is now widely implemented in various countries, including South Korea. To ensure high-quality testing, the Korean Association of External Quality Assessment Service launched a liquid biopsy EGFR proficiency testing program in 2017. This study analyzed the results of this program for 2017–2023. The number of participating laboratories increased from three in 2017 to 30 in 2023. All participating laboratories reported results using the Cobas z 480 analyzer (Roche Diagnostics, Germany). The accuracy of the test results was high with 100% correctness in several trials. However, errors were reported in some trials with false positives (e.g., detecting mutations in mutation-free samples) being more common than false negatives. This study evaluated Korean clinical laboratory performance when undertaking liquid biopsies for EGFR mutations. Continuous participation in the external quality assessment program helps laboratories maintain and improve the quality of liquid biopsy EGFR testing, ultimately benefiting patient care by ensuring accurate and reliable mutation detection.

Next-generation sequencing (NGS)-based liquid biopsy using peripheral blood offers a minimally invasive approach to detect tumor-derived circulating tumor DNA (ctDNA). Given the low abundance of ctDNA, accurate analysis is crucial, necessitating external quality assessments.Since 2020, the Korean Association of External Quality Assessment Service has conducted proficiency testing for NGS-based liquid biopsy. This study reviews the proficiency testing results from 2020 to 2023. The program was conducted biannually. Specimens were created by spiking fragmented DNA into fresh frozen plasma to simulate actual clinical samples. The number of target genes reported increased from 5 in 2020 to 17 in 2023. Results were assessed based on concordance with those obtained from targeted NGS panel testing performed before shipping the manufactured specimens.Participating laboratories used various NGS instruments and reagents. The read depth for each genetic variant varied across laboratories, while the reported read percentage of detected variants was generally consistent.Most laboratories accurately reported variants; however, some discrepancies related to variant position descriptions or incorrect reference sequence transcripts were noted. This study evaluates the performance of Korean clinical laboratories in NGS-based liquid biopsy. Continued vigilance in result reporting is necessary, and ongoing external quality assessments can enhance the reliability of NGS-based liquid biopsy testing.

Activating mutations in the epidermal growth factor receptor (EGFR) gene, particularly in the tyrosine kinase domain, occur in approximately 20% of non-small cell lung cancer (NSCLC) cases. These mutations, commonly found as deletions in exon 19 or the L858R mutation in exon 21, are crucial targets for EGFR tyrosine kinase inhibitors (TKIs). The third-generation EGFR TKI, osimertinib (TAGRISSO; AstraZeneca, UK), is effective against tumors with the T790M mutation and a liquid biopsy test has been approved by the US Food and Drug Administration as a companion diagnostic for TAGRISSO.This test is now widely implemented in various countries, including South Korea. To ensure high-quality testing, the Korean Association of External Quality Assessment Service launched a liquid biopsy EGFR proficiency testing program in 2017. This study analyzed the results of this program for 2017–2023. The number of participating laboratories increased from three in 2017 to 30 in 2023. All participating laboratories reported results using the Cobas z 480 analyzer (Roche Diagnostics, Germany). The accuracy of the test results was high with 100% correctness in several trials. However, errors were reported in some trials with false positives (e.g., detecting mutations in mutation-free samples) being more common than false negatives. This study evaluated Korean clinical laboratory performance when undertaking liquid biopsies for EGFR mutations. Continuous participation in the external quality assessment program helps laboratories maintain and improve the quality of liquid biopsy EGFR testing, ultimately benefiting patient care by ensuring accurate and reliable mutation detection.