Background: Acute deterioration of patients in general wards often leads to major adverse events (MAEs), including unplanned intensive care unit transfers, cardiac arrest, or death. Traditional early warning scores (EWSs) have shown limited predictive accuracy, with frequent false positives. We conducted a prospective observational external validation study of an artificial intelligence (AI)-based EWS, the VitalCare - Major Adverse Event Score (VC-MAES), at a tertiary medical center in the Republic of Korea. Methods: Adult patients from general wards, including internal medicine (IM) and obstetrics and gynecology (OBGYN)—the latter were rarely investigated in prior AI-based EWS studies—were included. The VC-MAES predictions were compared with National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) predictions using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and logistic regression for baseline EWS values. False-positives per true positive (FPpTP) were assessed based on the power threshold. Results: Of 6,039 encounters, 217 (3.6%) had MAEs (IM: 9.5%, OBGYN: 0.26%). Six hours prior to MAEs, the VC-MAES achieved an AUROC of 0.918 and an AUPRC of 0.352, including the OBGYN subgroup (AUROC, 0.964; AUPRC, 0.388), outperforming the NEWS (0.797 and 0.124) and MEWS (0.722 and 0.079). The FPpTP was reduced by up to 71%. Baseline VC-MAES was strongly associated with MAEs (P<0.001). Conclusions The VC-MAES significantly outperformed traditional EWSs in predicting adverse events in general ward patients. The robust performance and lower FPpTP suggest that broader adoption of the VC-MAES may improve clinical efficiency and resource allocation in general wards.

Background: Primary cutaneous CD30+ lymphoproliferative disorders (pcCD30-LPDs) are a diseases with various clinical and prognostic characteristics. Objective: Increasing our knowledge of the clinical characteristics of pcCD30-LPDs and identifying potential prognostic variables in an Asian population. Methods: Clinicopathological features and survival data of pcCD30-LPD cases obtained from 22 hospitals in South Korea were examined. Results: A total of 413 cases of pcCD30-LPDs (lymphomatoid papulosis [LYP], n=237; primary cutaneous anaplastic large cell lymphoma [C-ALCL], n=176) were included. Ninety percent of LYP patients and roughly 50% of C-ALCL patients presented with multiple skin lesions. Both LYP and C-ALCL affected the lower limbs most frequently. Multiplicity and advanced T stage of LYP lesions were associated with a chronic course longer than 6 months. Clinical morphology with patch lesions and elevated serum lactate dehydrogenase were significantly associated with LPDs during follow-up in LYP patients. Extracutaneous involvement of C-ALCL occurred in 13.2% of patients. Lesions larger than 5 cm and increased serum lactate dehydrogenase were associated with a poor prognosis in C-ALCL. The survival of patients with C-ALCL was unaffected by the anatomical locations of skin lesions or other pathological factors. Conclusion The multiplicity or size of skin lesions was associated with a chronic course of LYP and survival among patients with C-ALCL.

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.

This study investigated the molecular mechanism of phenotypic switching of vascular smooth muscle cells (VSMCs), which play a crucial role in vascular remodeling using 9H-Carbazol-3-yl 4-aminobenzoate (CAB). CAB significantly attenuated platelet-derived growth factor (PDGF)-induced VSMC proliferation and migration. CAB suppressed PDGF-induced STAT3 activation by directly binding to the SH2 domain of STAT3. Downregulation of STAT3 phosphorylation by CAB attenuated CIAPIN1/JAK2/STAT3 axis through a decrease in CIAPIN1 transcription. Furthermore, abrogated CIAPIN1 decreased KLF4-mediated VSMC dedifferentiation and increased CDKN1B-induced cell cycle arrest and MMP9 suppression. CAB inhibited intimal hyperplasia in injury-induced neointima animal models by inhibition of the CIAPIN1/JAK2/STAT3 axis. However, CIAPIN1 overexpression attenuated CAB-mediated suppression of VSMC proliferation, migration, phenotypic switching, and intimal hyperplasia. Our study clarified the molecular mechanism underlying STAT3 inhibition of VSMC phenotypic switching and vascular remodeling and identified novel active CAB. These findings demonstrated that STAT3 can be a major regulator to control CIAPIN1/JAK2/STAT3 axis that may be a therapeutic target for treating vascular proliferative diseases.

Purpose: Obesity is a well-established risk factor for dyslipidemia. Normal-weight obesity (NWO) is associated with lipid profile abnormalities. However, research in Korea on the relationship between NWO and dyslipidemia is limited. Therefore, this study evaluated the relationship between obesity types, including NWO, and dyslipidemia, and investigated these relationships by age and sex. Methods: Secondary data analysis was performed using data from the 9th Korea National Health and Nutrition Examination Survey conducted in 2022. In total, 3,805 participants were analyzed using complex sample multiple logistic regression based on body mass index and body fat percentage (BF%). Results: NWO was significantly associated with an increased risk of dyslipidemia relative to normal weight non-obesity (odds ratio: 1.66, 95% confidence interval: 1.34~2.06). Notably, individuals with NWO under the age of 65 and men had a higher risk of dyslipidemia, underscoring the importance of targeted health management in these populations. Conclusion NWO was positively correlated with dyslipidemia. Therefore, the role of clinical nurses in early screening for NWO is of considerable importance, particularly among high-risk populations such as men under the age of 65. To support obesity management and the prevention of dyslipidemia, nursing interventions aimed at reducing BF% by promoting lifestyle modifications and regular monitoring are essential.

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: Differentiating between asymptomatic bacteriuria (ASB) and urinary tract infection (UTI) is difficult in patients who have difficulty communicating their symptoms.This study aimed to evaluate the diagnostic accuracy of urine leukocytes in distinguishing between UTI and ASB, and the clinical outcomes of patients with UTI according to the degree of pyuria. Methods: This retrospective cohort study included patients with positive urine cultures between July 2022 and June 2023 at two hospitals. UTI and ASB were diagnosed through a comprehensive review of medical records. We evaluated the differences in urine leukocyte counts between patients with UTI and ASB. The diagnostic performance of urine leukocytes to differentiate between UTI and ASB was evaluated. To investigate the clinical outcomes based on the degree of pyuria, we classified patients with upper UTI according to their urine leukocyte counts. Results: Of the 1,793 eligible patients with bacteriuria included, 1,464 had UTI and 329 had ASB. Patients with UTI had higher urinary leukocytes than patients with ASB did (490.4 vs.123.5 cells/µL; P < 0.001). The area under the receiver operating characteristic curve was 0.702 for discriminating between ASB and UTI. The optimal urine leukocyte cutoff was 195.35 cells/µL, with a sensitivity and specificity of 0.70 and 0.60, respectively. A sequential rise in secondary bacteremia rate was observed according to an increase in urine leukocytes in patients with upper UTI, whereas in-hospital mortality showed no corresponding trend. Conclusion Urine leukocyte counts could be used to predict UTI occurrence and bacteremia secondary to UTI. Higher degrees of pyuria were associated with bacteremia but not with mortality. Urine leukocyte counts can provide additive information for patients with bacteriuria with vague symptoms.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Purpose: This study aimed to identify the key factors in nursing workforce management for pandemic response in general hospitals and long-term care hospitals and to analyze the relative importance of these factors. Methods: A validity test was conducted with experts to select four categories and 30 key factors related to nursing workforce management for pandemic response. Surveys were collected from 25 nursing managers in general hospitals and 21 nursing managers in long-term care hospitals, and the relative importance of the key factors was analyzed using the analytic hierarchy process method. Results: Differences were found between the two groups in the relative importance of nursing workforce management for pandemic response. Specifically, the highest-ranking category was “workforce recruitment and redeployment” for general hospitals, but “workforce support and protection” for long-term care hospitals. The most important factor regarding nursing workforce management was the “nurse-to-patient ratio” for both general and long-term care hospitals. Conclusion General and long-term care hospitals need to establish nursing workforce management strategies to effectively respond to pandemics with appropriate consideration of the relative importance and prioritization of key factors based on hospital characteristics.