Background/Aims: Elevated troponin levels predict in-hospital mortality and influence decisions regarding thrombolytic therapy in patients with acute pulmonary embolism (PE). However, the usefulness of high-sensitivity troponin T (hsTnT) regarding PE remains uncertain. We aimed to establish the optimal cut-off level and compare its performance for precise risk stratification. Methods: 374 patients diagnosed with acute PE were reviewed. PE-related adverse outcomes, a composite of PE-related deaths, cardiopulmonary resuscitation incidents, systolic blood pressure < 90 mmHg, and all-cause mortality within 30 days were evaluated. The optimal hsTnT cut-off for all-cause mortality, and the net reclassification index (NRI) was used to assess the incremental value in risk stratification. Results: Among 343 normotensive patients, 17 (5.0%) experienced all-cause mortality, while 40 (10.7%) had PE-related adverse outcomes. An optimal hsTnT cut-off value of 60 ng/L for all-cause mortality (AUC 0.74, 95% CI 0.61–0.85, p < 0.001) was identified, which was significantly associated with PE-related adverse outcomes (OR 4.07, 95% CI 2.06–8.06, p < 0.001). Patients with hsTnT ≥ 60 ng/L were older, hypotensive, had higher creatinine levels, and right ventricular dysfunction signs. Combining hsTnT ≥ 60 ng/L with simplified pulmonary embolism severity index ≥1 provided additional prognostic information. Reclassification analysis showed a significant shift in risk categories, with an NRI of 1.016 ± 0.201 (p < 0.001). Conclusions We refined troponin’s predictive value in patients with acute PE, proposing a new cut-off value of hsTnT ≥ 60 ng/L. Validation through large-scale studies is essential to offer clinically useful guidance for managing patient population.

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.

This review summarized the results of clinical trials in 2024 that were believed to have a significant impact on clinical practice in the field of gynecologic oncology. The SHAPE trial, INTERLACE and KEYNOTE-A18 trials, and BEATcc and COMPASSION-16 trials were included in early-stage, locally advanced, and recurrent/metastatic cervical cancer, respectively. For uterine corpus cancer, updated survival data of the four trials (NRG-GY018, RUBY, AtTEnd, DUO-E) for endometrial cancer and the first survival data of LMS-04 trial for leiomyosarcoma were described. For ovarian cancer, the final overall survival results of PRIMA study were followed by DUO-O, ATHENA-combo, and FIRST-ENGOT-OV44 trial in different disease conditions. Finally, the results of DESTINY-PanTumor02, a basket trial of trastuzumab deruxtecan, were briefly addressed.

We describe the updated Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of cervical cancer, version 5.1. The KSGO announced the fifth version of its clinical practice guidelines for the management of cervical cancer in March 2024. The selection of the key questions and the systematic reviews were based on data available up to December 2022. Between 2023 and 2024, substantial findings from large-scale clinical trials and new advancements in cervical cancer research remarkably emerged. Therefore, based on the existing version 5.0, we updated the guidelines with newly accumulated clinical data and added 4 new key questions reflecting the latest insights in the field of cervical cancer. For each question, recommendation was formulated with corresponding level of evidence and grade of recommendation, all established through expert consensus.

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: While the clinical course of radiation-induced meningioma (RIM) is considered to be more aggressive than that of sporadic meningioma (SM), the genetic predisposition for RIM is not established well. The present study aimed to analyze the clinical and genetic characteristics of RIMs to increase understanding of the tumorigenesis and prognosis of RIMs. Methods: We investigated a database of 24 patients who met the RIM criteria between January 2000 and April 2023. Genetic analysis through next-generation sequencing with a targeted gene panel was performed on 10 RIM samples. Clinical, radiological, and pathological parameters were evaluated with genetic analyses. Results: The median ages for receiving radiotherapy (RT) and RIM diagnosis were 8.0 and 27.5 years, respectively, with an interval of 17.5 years between RT and RIM diagnosis. RIMs tended to develop in non-skull bases and multifocal locations. Most primary pathologies included germ cell tumors and medulloblastoma. The tumor growth rate was 3.83 cm 3 per year, and the median doubling time was 0.8 years. All patients underwent surgical resection of RIMs. The histological grade of RIMs was World Health Organization grade 1 (64%) or 2 (36%). RIMs showed higher incidences in young-age (63%), high-dose (75%), and extendedfield (79%) RT groups. The recurrence rate was 21%. Genetic analysis revealed NF2 one copy loss in 90% of the patients, with truncating NF2 mutations and additional copy number aberrations in grade 2 RIMs. TERT promoter mutation and CDKN2A/B deletion were not identified. Notably, loss of H3K27me3 was identified in 26% of RIMs. H3K27me3 loss was associated with a higher prevalence of grade 2 RIMs (67%) and high recurrence rates (33%). Conclusion The study reveals a higher prevalence of high-grade tumors among RIMs with more rapid growth and higher recurrences than SMs. Genetically, RIMs are primarily associated with NF-2 alterations with chromosomal abnormalities in grade 2 tumors, along with a higher proportion of H3K27me3 loss.

Managing giant partially thrombosed intracranial aneurysms presents significant challenges due to their unfavorable natural history and the lack of standardized treatment approaches. Conventional treatments, whether open surgical or endovascular, often struggle to manage these aneurysms effectively, resulting in high recurrence rates or significant morbidity. The patient was a 62-year-old male with a symptomatic giant partially thrombosed aneurysm at the tip of the basilar artery, presenting with left-sided hemiparesis and dysarthria. Diagnostic imaging revealed a giant aneurysm with a wide-necked, canalized portion. A two-stage endovascular treatment was conducted, involving a balloon occlusion test and intraoperative monitoring for maximum patient safety. The treatment utilized stent-assisted Woven EndoBridge (WEB) embolization and serial bilateral vertebral artery trapping. The procedure successfully isolated the aneurysm and postoperative imaging confirmed the absence of recanalization, preserving the intact posterior circulation. The patient showed stable recovery and no neurological deficits during the 12-month follow-up period. This technical note demonstrates the feasibility and efficacy of strategically integrating intrasaccular flow diversion using a WEB device and flow reversal through bilateral vertebral artery trapping for treating giant partially thrombosed aneurysms.

Background: Korea National Health and Nutrition Examination Survey (KNHANES) triglyceride testing changed from the glycerol blanking method (2005–2021) to the glycerol nonblanking method (2022). We converted triglyceride data from 2005–2021 to that obtained since 2022 with different analytical methods. Methods: To develop a conversion equation, 98 fresh serum specimen pairs were compared using Passing–Bablok regression analysis. Implications of the conversion equation on epidemiological data were evaluated using KNHANES data from 2019–2021. Bias estimations determined using the Lipid Standardization Program (LSP) of the United States Centers for Disease Control and Prevention (CDC) enhanced the accuracy and comparability of the triglyceride results. Results: Triglyceride concentrations measured via the glycerol non-blanking method were 10.7 mg/dL (0.12 mmol/L, 10.0%) higher than those from the glycerol blanking method, with a 9.9 mg/dL (0.11 mmol/L, 5.0%) difference at a concentration of 200 mg/dL (2.26 mmol/L, N = 98). The conversion equation y (glycerol non-blanking, 2022) = 11.94+0.99x (glycerol blanking, 2005–2021) changed the mean triglyceride concentrations of the KNHANES 2019–2021 data (N = 16,015) from 123.7 mg/dL (1.40 mmol/L, 95% confidence interval [CI]: 122.2–125.1 mg/dL [1.38–1.41 mmol/L]) to 134.3 mg/dL (1.52 mmol/L, 95% CI: 132.9–135.8 mg/dL [1.50–1.53 mmol/L]). Since 2022, bias monitoring using the CDC’s LSP has remained within a 5.0% limit. Conclusions KNHANES triglyceride values in 2022 (non-blanking) were substantially higher than those from 2005–2021 (blanking). Conversion equations helped effectively adjust 2005–2021 data. Researchers should consider adjusting the KNHANES triglyceride data based on their study characteristics.

Background: Ensuring reference material (RM) commutability is crucial for evaluating measurement traceability in order to standardize laboratory tests. However, commutability assessment is not routinely performed. We assessed whether RMs prepared following CLSI C37-A guidelines could be used without assessing commutability by evaluating their commutability for four lipid measurements using the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) and CLSI EP14 protocols. Methods: We analyzed total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) in frozen sera from 20 individuals and 11 RMs, prepared by the Korea Disease Control and Prevention AgencyLaboratory Standardization Project (per CLSI C37-A), using six routine measurement procedures (MPs). Regression equations and 95% prediction intervals derived from single-donor sera were analyzed following CLSI EP14. The IFCC protocol was used to assess differences in inter-MP biases between RM and clinical samples. The effect of the TG concentration on commutability was evaluated by analyzing biases between MP results and reference procedure-assigned values. Results: RMs were commutable for most MP pairs for TC and TG. Commutability for HDL-C and LDL-C varied across RMs, with RM10 and RM11 showing higher TG levels (2.38 and 2.95 mmol/L, respectively) and lower commutability. Increased bias percentages from assigned values were observed for RMs with higher TG levels. Conclusions RMs prepared per CLSI C37-A were commutable with most MP pairs for TC and TG. Elevated TG levels affected HDL-C and LDL-C commutability, highlighting the need to consider TG concentrations during RM preparation and assess commutability to standardize laboratory tests.

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.