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: The perianesthetic morbidity, mortality risk and anesthesia-associated risk after preoperative coronavirus disease 2019 (COVID-19) omicron variant in pediatric patients have not been fully demonstrated. We examined the association between preoperative COVID-19 omicron diagnosis and the incidence of overall perioperative adverse events in pediatric patients who received general anesthesia. Methods: This retrospective study included patients aged < 18 years who received general anesthesia between February 1 and June 10, 2022, in a single tertiary pediatric hospital.They were divided into two groups; patients in a COVID-19 group were matched to patients in a non-COVID-19 group during the omicron-predominant period in Korea. Data on patient characteristics, anesthesia records, post-anesthesia records, COVID-19-related history, symptoms, and mortality were collected. The primary outcomes were the overall perioperative adverse events, including perioperative respiratory adverse events (PRAEs), escalation of care, and mortality. Results: In total, 992 patients were included in the data analysis (n = 496, COVID-19; n = 496, non-COVID-19) after matching. The overall incidence of perioperative adverse events was significantly higher in the COVID-19 group than in the non-COVID-19 group (odds ratio [OR], 1.92; 95% confidence interval [CI], 1.89–1.94). The difference was significant for PRAEs (OR, 2.00; 95% CI, 1.96–2.02) but not in escalation of care or mortality. The most difference between the two groups was observed in instances of high peak inspiratory pressure ≥ 25 cmH 2 O during the intraoperative period (OR, 11.0; 95% CI, 10.5–11.4). Compared with the non-COVID-19 group, the risk of overall perioperative adverse events was higher in the COVID-19 group diagnosed 0–2 weeks before anesthesia (OR, 6.5; 95% CI, 2.1–20.4) or symptomatic on the anesthesia day (OR, 6.4; 95% CI, 3.30–12.4). Conclusion Pediatric patients with the preoperative COVID-19 omicron variant had increased risk of PRAEs. Patients within 2 weeks after COVID-19 or those with symptoms had a higher risk of PRAEs

Background: The integrase strand transfer inhibitor dolutegravir has been indicated in Korea since 2014 for the treatment of human immunodeficiency virus type 1 (HIV-1) infection in combination with other antiretroviral agents. This regulatory post-marketing surveillance (PMS) study evaluated the real-life safety and effectiveness of dolutegravir in patients with HIV-1 in clinical practice in Korea. Materials and Methods: This open-label PMS study examined data from consecutive patients (aged ≥12 years) with HIV-1 infection receiving dolutegravir according to locally approved prescribing information; treatment-naïve and treatment-experienced patients were permitted. Data regarding patient demographics, medical history, clinical characteristics, medications (HIV-related and concomitant), and comorbidities were extracted from patient records over a 1-year treatment period. Outcomes included the safety of dolutegravir (primary endpoint) and real-life effectiveness according to the Physician Global Assessment (PGA) and the proportion of patients with plasma HIV-1 RNA count <50 copies/mL at 48 weeks. Results: Of 147 patients treated with dolutegravir at 18 centers in Korea (August 2014 – August 2020), 139 were eligible for the safety analyses and 75 for effectiveness analyses.Patients (mean age 47 years) were mostly male (92.8%) and received dolutegravir in combination with nucleoside reverse transcriptase inhibitor (70.5%) or protease inhibitors (21.6%). Adverse events (AEs) (n = 179 in total) were mostly mild in severity, with the most common being nasopharyngitis (5.0%), dyspepsia (5.0%), pruritus (4.3%), and rash (4.3%).Of 16 adverse drug reactions (ADRs), the most frequent were rash, diarrhea, headache, insomnia, and somnolence (1.4% each). Of 2 serious ADRs, only 1 (gastroenteritis) was unexpected, and both resolved. The risk of experiencing an AE while receiving dolutegravir appeared to be especially increased in patients receiving concomitant medications for other conditions. Dolutegravir effectively suppressed HIV-1 (93.3% of patients had plasma HIV-1 RNA <50 copies/mL), and 100% of patients showed symptom improvement based on physician global assessment. Conclusion Results of this PMS study showed that dolutegravir administered as highly active antiretroviral therapy was well tolerated and effective in patients with HIV-1 infection.

Sepsis is one of the significant causes of morbidity and mortality. The burden caused by sepsis has continued to increase in recent years in the Korea, highlighting the urgent need for the implementation of strategies to improve sepsis treatment outcomes. We therefore designed a web-based sepsis registry system (“Korean Registry for Improving Sepsis Survival” [KISS]) protocol to be used in hospitals in the Korea for evaluation of the epidemiology and clinical characteristics of patients with sepsis, via an analysis of outcome predictors. The inclusion criteria of this registry are as follows: adult patients ≥18 years admitted to the participating hospitals who are diagnosed with sepsis or septic shock. Demographic and clinical information data of the patients will be collected from hospital medical records and will be recorded in a case report form, which will be entered into a web-based data management system. The analysis of the collected data will be performed as follows: (1) epidemiological and clinical characteristics of sepsis and septic shock, (2) application of sepsis bundles and antibiotic stewardship, and (3) audit and feedback. In conclusion, we aim to build the comprehensive web-based sepsis registry in the Korea through a nation-wide network of participating hospitals. Information collected and analyzed through the KISS can be used for further improvements in the clinical management of sepsis. Furthermore, the KISS will facilitate research leading to the formulation of public health policies regarding sepsis bundle and antibiotic stewardship strategies in the Korea.

BACKGROUND: The delta neutrophil index (DNI) is a useful marker for diagnosing and predicting the prognosis of sepsis. The purpose of this study was to investigate the usefulness of DNI as a prognostic marker in patients within the pediatric intensive care unit (PICU), as well as its association with other prognostic factors. METHODS: A total of 516 children admitted to Severance Children's Hospital PICU from December 2009 to February 2015 were analyzed. DNI was measured on the day of PICU admission. Mortality was defined as death within 28 days following PICU admission. RESULTS: The median value of DNI was 1.2% (interquartile range [IQR] 0-4.3%) in the survivor group and 9.5% (IQR 2.3-20.8%) in the non-survivor group, and the difference was statistically significant (p < 0.001). DNI was significantly positively correlated with ICU scores such as Pediatric Index of Mortality 3 and Pediatric Risk of Mortality III, as well as with C-reactive protein and lactate levels. The area under the receiver operating characteristic curve of DNI for mortality was 0.748 (95% CI: 0.687-0.808) and the cut-off value was 4.95%. CONCLUSIONS: The initial DNI level can be considered a useful indicator for predicting prognosis in PICU patients.
C-Reactive Protein ; Child ; Critical Care* ; Humans ; Intensive Care Units* ; Lactic Acid ; Mortality ; Neutrophils* ; Prognosis ; ROC Curve ; Sepsis ; Survivors

BACKGROUND: The aim of this study was to investigate the prevalence of obesity in Korean men with Klinefelter syndrome (KS) and the associated risk factors for obesity and hyperglycemia. METHODS: Data were collected retrospectively from medical records from 11 university hospitals in Korea between 1994 and 2014. Subjects aged ≥18 years with newly diagnosed KS were enrolled. The following parameters were recorded at baseline before treatment: chief complaint, height, weight, fasting glucose level, lipid panel, blood pressure, testosterone, luteinizing hormone, follicle-stimulating hormone, karyotyping patterns, and history of hypertension, diabetes, and dyslipidemia. RESULTS: Data were analyzed from 376 of 544 initially enrolled patients. The rate of the 47 XXY chromosomal pattern was 94.1%. The prevalence of obesity (body mass index ≥25 kg/m²) in Korean men with KS was 42.6%. The testosterone level was an independent risk factor for obesity and hyperglycemia. CONCLUSION: Obesity is common in Korean men with KS. Hypogonadism in patients with KS was associated with obesity and hyperglycemia.
Blood Pressure ; Dyslipidemias ; Fasting ; Follicle Stimulating Hormone ; Glucose ; Hospitals, University ; Humans ; Hyperglycemia* ; Hypertension ; Hypogonadism ; Karyotyping ; Klinefelter Syndrome* ; Korea ; Luteinizing Hormone ; Male ; Medical Records ; Obesity* ; Prevalence ; Retrospective Studies ; Risk Factors ; Testosterone

BACKGROUND: The delta neutrophil index (DNI) is a useful marker for diagnosing and predicting the prognosis of sepsis. The purpose of this study was to investigate the usefulness of DNI as a prognostic marker in patients within the pediatric intensive care unit (PICU), as well as its association with other prognostic factors. METHODS: A total of 516 children admitted to Severance Children's Hospital PICU from December 2009 to February 2015 were analyzed. DNI was measured on the day of PICU admission. Mortality was defined as death within 28 days following PICU admission. RESULTS: The median value of DNI was 1.2% (interquartile range [IQR] 0-4.3%) in the survivor group and 9.5% (IQR 2.3-20.8%) in the non-survivor group, and the difference was statistically significant (p < 0.001). DNI was significantly positively correlated with ICU scores such as Pediatric Index of Mortality 3 and Pediatric Risk of Mortality III, as well as with C-reactive protein and lactate levels. The area under the receiver operating characteristic curve of DNI for mortality was 0.748 (95% CI: 0.687-0.808) and the cut-off value was 4.95%. CONCLUSIONS: The initial DNI level can be considered a useful indicator for predicting prognosis in PICU patients.
C-Reactive Protein ; Child ; Critical Care ; Humans ; Intensive Care Units ; Lactic Acid ; Mortality ; Neutrophils ; Prognosis ; ROC Curve ; Sepsis ; Survivors