OBJECTIVES: The American Heart Association (AHA) recently defined cardiovascular-kidney-metabolic (CKM) syndrome to better characterize the associations among cardiovascular, kidney, and metabolic diseases. Although about 9 in 10 United States adults have at least 1 risk factor for CKM syndrome, its prevalence in other populations is less understood. To fill this gap, we examined the prevalence of CKM syndrome in Korea and its association with demographic and socioeconomic status (SES). METHODS: Using data from the Korean National Health and Nutrition Examination Survey between 2011 and 2021, we calculated the prevalence of CKM syndrome across the following stages: stage 0 (no risk factors), stage 1 (excess or dysfunctional adiposity), stage 2 (other metabolic risk factors or chronic kidney disease), and stages 3-4 (subclinical/clinical cardiovascular diseases) among adults aged ≥20 years. Weighted analyses were used to estimate prevalence and 95% confidence intervals (CIs) for each CKM syndrome stage, stratified by age, gender, and SES factors. RESULTS: Among 54,994 Korean adults, the prevalence of CKM syndrome was as follows: stage 0 (25.2%; 95% CI, 24.7 to 25.8), stage 1 (19.3%; 95% CI, 18.9 to 19.7), stage 2 (51.6%; 95% CI, 51.1 to 52.2), and stages 3-4 (3.9%; 95% CI, 3.7 to 4.0). The prevalence of stages 2 and 3-4 was higher in men than in women. In addition, stages 3-4 were more prevalent among rural residents and those with lower education or income. CONCLUSIONS About 3 out of 4 Koreans are at risk for CKM syndrome. These findings highlight that CKM syndrome is a global health problem and that interventions are urgently needed to prevent further progression.

OBJECTIVES: The American Heart Association (AHA) recently defined cardiovascular-kidney-metabolic (CKM) syndrome to better characterize the associations among cardiovascular, kidney, and metabolic diseases. Although about 9 in 10 United States adults have at least 1 risk factor for CKM syndrome, its prevalence in other populations is less understood. To fill this gap, we examined the prevalence of CKM syndrome in Korea and its association with demographic and socioeconomic status (SES). METHODS: Using data from the Korean National Health and Nutrition Examination Survey between 2011 and 2021, we calculated the prevalence of CKM syndrome across the following stages: stage 0 (no risk factors), stage 1 (excess or dysfunctional adiposity), stage 2 (other metabolic risk factors or chronic kidney disease), and stages 3-4 (subclinical/clinical cardiovascular diseases) among adults aged ≥20 years. Weighted analyses were used to estimate prevalence and 95% confidence intervals (CIs) for each CKM syndrome stage, stratified by age, gender, and SES factors. RESULTS: Among 54,994 Korean adults, the prevalence of CKM syndrome was as follows: stage 0 (25.2%; 95% CI, 24.7 to 25.8), stage 1 (19.3%; 95% CI, 18.9 to 19.7), stage 2 (51.6%; 95% CI, 51.1 to 52.2), and stages 3-4 (3.9%; 95% CI, 3.7 to 4.0). The prevalence of stages 2 and 3-4 was higher in men than in women. In addition, stages 3-4 were more prevalent among rural residents and those with lower education or income. CONCLUSIONS About 3 out of 4 Koreans are at risk for CKM syndrome. These findings highlight that CKM syndrome is a global health problem and that interventions are urgently needed to prevent further progression.

OBJECTIVES: The American Heart Association (AHA) recently defined cardiovascular-kidney-metabolic (CKM) syndrome to better characterize the associations among cardiovascular, kidney, and metabolic diseases. Although about 9 in 10 United States adults have at least 1 risk factor for CKM syndrome, its prevalence in other populations is less understood. To fill this gap, we examined the prevalence of CKM syndrome in Korea and its association with demographic and socioeconomic status (SES). METHODS: Using data from the Korean National Health and Nutrition Examination Survey between 2011 and 2021, we calculated the prevalence of CKM syndrome across the following stages: stage 0 (no risk factors), stage 1 (excess or dysfunctional adiposity), stage 2 (other metabolic risk factors or chronic kidney disease), and stages 3-4 (subclinical/clinical cardiovascular diseases) among adults aged ≥20 years. Weighted analyses were used to estimate prevalence and 95% confidence intervals (CIs) for each CKM syndrome stage, stratified by age, gender, and SES factors. RESULTS: Among 54,994 Korean adults, the prevalence of CKM syndrome was as follows: stage 0 (25.2%; 95% CI, 24.7 to 25.8), stage 1 (19.3%; 95% CI, 18.9 to 19.7), stage 2 (51.6%; 95% CI, 51.1 to 52.2), and stages 3-4 (3.9%; 95% CI, 3.7 to 4.0). The prevalence of stages 2 and 3-4 was higher in men than in women. In addition, stages 3-4 were more prevalent among rural residents and those with lower education or income. CONCLUSIONS About 3 out of 4 Koreans are at risk for CKM syndrome. These findings highlight that CKM syndrome is a global health problem and that interventions are urgently needed to prevent further progression.

OBJECTIVES: The American Heart Association (AHA) recently defined cardiovascular-kidney-metabolic (CKM) syndrome to better characterize the associations among cardiovascular, kidney, and metabolic diseases. Although about 9 in 10 United States adults have at least 1 risk factor for CKM syndrome, its prevalence in other populations is less understood. To fill this gap, we examined the prevalence of CKM syndrome in Korea and its association with demographic and socioeconomic status (SES). METHODS: Using data from the Korean National Health and Nutrition Examination Survey between 2011 and 2021, we calculated the prevalence of CKM syndrome across the following stages: stage 0 (no risk factors), stage 1 (excess or dysfunctional adiposity), stage 2 (other metabolic risk factors or chronic kidney disease), and stages 3-4 (subclinical/clinical cardiovascular diseases) among adults aged ≥20 years. Weighted analyses were used to estimate prevalence and 95% confidence intervals (CIs) for each CKM syndrome stage, stratified by age, gender, and SES factors. RESULTS: Among 54,994 Korean adults, the prevalence of CKM syndrome was as follows: stage 0 (25.2%; 95% CI, 24.7 to 25.8), stage 1 (19.3%; 95% CI, 18.9 to 19.7), stage 2 (51.6%; 95% CI, 51.1 to 52.2), and stages 3-4 (3.9%; 95% CI, 3.7 to 4.0). The prevalence of stages 2 and 3-4 was higher in men than in women. In addition, stages 3-4 were more prevalent among rural residents and those with lower education or income. CONCLUSIONS About 3 out of 4 Koreans are at risk for CKM syndrome. These findings highlight that CKM syndrome is a global health problem and that interventions are urgently needed to prevent further progression.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

Countries around the world are spending a lot of social costs due to various social problems such as traffic congestion, housing, and living environments caused by urbanization. It is judged that it is urban air mobility (UAM) that can solve these problems, so each country and local government are actively participating in the UAM industry to solve these problems. And it is promoting it to the public with a blueprint as the best alternative that can dramatically solve the congested traffic environment in the city center. However, I don’t think it explains in detail the many limited operating environments and risks to be overcome in order to commercialize UAM. Therefore, I would like to consider the low-altitude airspace operating environment and vertical take-off landing aerodynamic limitations required for stable commercialization of UAM aircraft.

The aim of this study was to develop evidence-based recommendations for determining the surgical extent in patients with locally invasive differentiated thyroid cancer (DTC). Locally invasive DTC with gross extrathyroidal extension invading surrounding anatomical structures may lead to several functional deficits and poor oncological outcomes. At present, the optimal extent of surgery in locally invasive DTC remains a matter of debate, and there are no adequate guidelines. On October 8, 2021, four experts searched the PubMed, Embase, and Cochrane Library databases; the identified papers were reviewed by 39 experts in thyroid and head and neck surgery. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to assess the quality of evidence, and to develop and report recommendations. The strength of a recommendation reflects the confidence of a guideline panel that the desirable effects of an intervention outweigh any undesirable effects, across all patients for whom the recommendation is applicable. After completing the draft guidelines, Delphi questionnaires were completed by members of the Korean Society of Head and Neck Surgery. Twenty-seven evidence-based recommendations were made for several factors, including the preoperative workup; surgical extent of thyroidectomy; surgery for cancer invading the strap muscles, recurrent laryngeal nerve, laryngeal framework, trachea, or esophagus; and surgery for patients with central and lateral cervical lymph node involvement. Evidence-based guidelines were devised to help clinicians make safer and more efficient clinical decisions for the optimal surgical treatment of patients with locally invasive DTC.

Background: The purpose of this study was to verify the accuracy and validity of using machine learning (ML) to select risk factors, to discriminate differences in feature selection by ML between men and women, and to develop predictive models for patients with osteoporosis in a big database. Methods: The data on 968 observed features from a total of 3,484 the Korea National Health and Nutrition Examination Survey participants were collected. To find preliminary features that were well-related to osteoporosis, logistic regression, random forest, gradient boosting, adaptive boosting, and support vector machine were used. Results: In osteoporosis feature selection by 5 ML models in this study, the most selected variables as risk factors in men and women were body mass index, monthly alcohol consumption, and dietary surveys. However, differences between men and women in osteoporosis feature selection by ML models were age, smoking, and blood glucose level. The receiver operating characteristic (ROC) analysis revealed that the area under the ROC curve for each ML model was not significantly different for either gender. Conclusions ML performed a feature selection of osteoporosis, considering hidden differences between men and women. The present study considers the preprocessing of input data and the feature selection process as well as the ML technique to be important factors for the accuracy of the osteoporosis prediction model.

Background: Dual energy X-ray absorptiometry (DXA) is a preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture. However, the DXA test is difficult to implement easily in some developing countries, and fractures have been observed before patients underwent DXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using artificial intelligence (AI) or machine learning, organize the results of each study, and analyze the usefulness of this technology. Methods: The PubMed, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched including “hip fractures” AND “artificial intelligence”. Results: A total of 7 studies are included in this study. The total number of subjects included in the 7 studies was 330,099. There were 3 studies that included only women, and 4 studies included both men and women. One study conducted AI training after 1:1 matching between fractured and non-fractured patients. The area under the curve of AI prediction model for hip fracture risk was 0.39 to 0.96. The accuracy of AI prediction model for hip fracture risk was 70.26% to 90%. Conclusions We believe that predicting the risk of hip fracture by the AI model will help select patients with high fracture risk among osteoporosis patients. However, to apply the AI model to the prediction of hip fracture risk in clinical situations, it is necessary to identify the characteristics of the dataset and AI model and use it after performing appropriate validation.

Purpose: With advances in surgical techniques, reduced-port laparoscopic surgery is increasingly being performed for the treatment of gastric carcinoma. Many studies have reported satisfactory short-term outcomes after reduced 3-port laparoscopic gastrectomy (LG). The aim of this study was to investigate the long-term oncological outcomes of 3-port LG in patients with gastric carcinoma. Materials and Methods: We reviewed the medical records of 1,117 patients who underwent LG for gastric carcinoma in three major institutions between 2012 and 2015. The data showed that 460 patients underwent 3-port LG without assistance, and 657 underwent conventional 5-port LG. We compared the overall and disease-free survival rates between the 2 groups. Results: There were 642 male and 475 female patients with a mean age of 56.1 years.Among them, 1,028 (92.0%) underwent distal gastrectomy and 89 (8.0%) underwent total gastrectomy. In the final pathologic examination, 1,027 patients (91.9%) were stage I, 73 (6.5%) were stage II, and 17 (1.5%) were stage III, and there were no significant difference in the pathologic stage between groups. The 3- and 5-port LG groups showed no significant differences in the 5-year overall survival (94.3% vs. 96.7%, P=0.138) or disease-free survival (94.3% vs. 95.9%, P=0.231). Stratified analyses according to pT and pN stages also showed no significant differences in overall or disease-free survival between the two groups. Conclusions Long-term survival after 3- and 5-port LG was comparable in patients with early-stage gastric carcinoma. The 3-port technique requiring limited surgical assistance may be an appropriate surgical option for this patient population.