Background: Research on the relationship between the progression of interstitial lung abnormalities (ILA) and serum biomarkers, including white blood cell differential counts and Krebs von den Lungen-6 (KL-6), is limited. This study aimed to examine the clinical characteristics of patients with ILA and evaluate the association between disease progression and serum biomarkers. Methods: This retrospective cohort study analyzed data from 159 patients (63 with ILA and 74 with interstitial lung diseases) between October 2021 and September 2022. Data collected included clinical characteristics, pulmonary function tests, chest computed tomography (CT), complete blood cell counts, and KL-6 levels. In 52 of these patients who had previously undergone chest CT, the utility of serum biomarkers in reflecting radiologic progression was assessed using receiver operating characteristic curve analysis. Results: Patients with ILA exhibited clinical characteristics similar to those with idiopathic pulmonary fibrosis. Serum KL-6 levels did not correlate with forced vital capacity or diffusing capacity of the lung for carbon monoxide in patients with ILA. Among the 52 patients with ILA, 13 demonstrated radiologic progression. Serum KL-6 displayed moderate predictive performance, with area under the curves ranging from 0.57 to 0.89 (p=0.014) for radiologic progression. Levels of KL-6 greater than 400 U/mL were more frequently observed in patients with radiologic progression (61.5% vs. 20.5%, p=0.006). In multivariate analysis, age and KL-6 were independently associated with radiologic progression in patients with ILA. Conclusion Serum KL-6 levels may serve as a potential indicator of ILA progression in asymptomatic patients. Those with KL-6 levels exceeding 400 U/mL should be closely monitored for radiologic progression.

Objective: Disasters such as pandemics and natural catastrophes cause significant psychological distress, increasing the risk of depression, anxiety, and post-traumatic stress disorder (PTSD). This review aimed to evaluate the effectiveness of remote mental health interventions, such as telepsychiatry, online cognitive behavioral therapy (CBT), mobile applications, and artificial intelligence (AI)-based tools, during disasters. Methods: A comprehensive literature search was conducted using international databases (PubMed, EMBASE, PsycINFO, Web of Science, Scopus, CINAHL) and Korean domestic databases (KISS, RISS, DBpia, KMbase, KoreaMed). Studies published between January 2010 and February 2025 were screened following the PRISMA 2020 guidelines. Two board-certified psychiatrists independently reviewed titles, abstracts, and full texts. Peer-reviewed articles and relevant grey literature, including government manuals and expert guidelines, were included. Results: A total of 147 studies were synthesized, including randomized controlled trials, meta-analyses, observational studies, and policy documents. Evidence showed that remote interventions effectively reduced symptoms of depression, anxiety, and PTSD. Online CBT was beneficial for mild to moderate distress; mobile apps facilitated psychoeducation and symptom tracking; and telepsychiatry ensured care continuity, especially in underserved areas. AI tools showed promise in screening and triage but require better clinical integration. Effectiveness was highest when interventions were implemented early, integrated within stepped-care models, and guided by trained professionals. Conclusion Remote mental health interventions are practical and effective tools for bridging mental health care gaps during disasters. To ensure sustainability, these interventions should be integrated into national disaster mental health frameworks, accompanied by adequate funding, clinician training, legal safeguards, and cultural adaptation.

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.

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.