Objective: The Daegu region experienced the first wave of the pandemic at the beginning of the coronavirus disease 2019 (COVID-19) outbreak in Korea. Other non-COVID-19-related treatments during a community outbreak, such as cardiovascular diseases, were expected to impact emergency departments. In acute myocardial infarctions, time is an important factor affecting the patient outcome. This study examined how community COVID-19 outbreak affected STsegment elevated myocardial infarction (STEMI) care in emergency departments. Methods: A retrospective analysis was performed on patients visiting five emergency departments in the Daegu area who were diagnosed with STEMI from February 18 to April 17 each year from 2018 to 2020. The demographic characteristics, prehospital variables, in-hospital time variables, and treatment results were collected. The cases were divided into the pre-COVID period and the COVID period for comparison. Results: The study included 254 patients (194 pre-COVID, 60 during COVID). The symptom-to-door time did not differ. Although the door-to-first doctor time was shortened (4 min vs. 2 min, P=0.01), the rate of coronary angiogram along with the door-to-angiogram time and the door-to-balloon time did not change. The length of stay in the emergency department was delayed during COVID-19 (median, 136 min vs. 404 min; P<0.01). The in-hospital length of stay and mortality were similar in both groups. Conclusion The time to treat STEMI was not delayed significantly during the first wave of the COVID-19 outbreak in the Daegu area compared with the pre-pandemic period. Mortality did not change. The length of stay was elongated significantly in the emergency department but not in the hospital.

Mesenchymal tumors of the breast, which originate from the mammary stroma, are rare accounting for only approximately 0.5%–1% of all breast tumors. Pathologically, they can exist on a spectrum, ranging from benign to malignant. Such tumors may present with nonspecific findings on breast imaging, including mammography, ultrasound, and MRI, which can lead to diagnostic challenges. In the 2019 revised 5th edition of the World Health Organization classification, breast mesenchymal tumors are categorized into six groups. The current pictorial essay aimed to explore the clinical, pathological, and imaging characteristics of representative lesions in each category according to this six-group classification, with the ultimate goal of enhancing awareness for early diagnosis.

Background: Skipping breakfast is associated with an increased risk of chronic inflammatory diseases. This study aimed to examine the association between breakfast-eating habits and inflammation, using high-sensitivity C-reactive protein (hs-CRP) as a marker. Methods: A total of 4,000 Korean adult males with no history of myocardial infarction, angina, stroke, diabetes, rheumatoid arthritis, cancer, or current smoking were included. Data from the 2016–2018 Korea National Health and Nutrition Examination Survey were used for analysis. The frequency of breakfast consumption was assessed through a questionnaire item in the dietary survey section asking participants about their weekly breakfast consumption routines over the past year. Participants were categorized into two groups, namely “0–2 breakfasts per week” and “3–7 breakfasts per week”; hs-CRP concentrations were measured through blood tests. Results: Comparing between the “infrequent breakfast consumption (0–2 breakfasts per week)” and “frequent breakfast consumption (3–7 breakfasts per week)” groups, the mean hs-CRP was found to be significantly higher in the “infrequent breakfast consumption” group, even after adjusting for age, body mass index, physical activity, alcohol consumption, systolic blood pressure, blood pressure medication, fasting blood glucose, and triglycerides (mean hs-CRP: frequent breakfast consumption, 1.36±0.09 mg/L; infrequent breakfast consumption, 1.17±0.05 mg/L; P-value=0.036). Conclusion Less frequent breakfast consumption was associated with elevated hs-CRP levels. Further large-scale studies incorporating adjusted measures of daily eating patterns as well as food quality and quantity are required for a deeper understanding of the role of breakfast in the primary prevention of chronic inflammatory diseases.

Background: Chronic pain significantly affects daily activities, mental health, and the interpersonal relationships of patients. Consequently, physicians use various treatments to manage pain. This study investigated the perceptions of treatment, accompanying symptoms, and other problems in patients with chronic pain. Methods: The authors enrolled patients with chronic pain from 19 university hospitals in South Korea. Data was collected on age, gender, diagnosis, disease duration, severity of pain, perception of pain treatment, and accompanying symptoms or problems using an anonymous survey comprising 19 questions. Results: In total, 833 patients with chronic pain completed the survey, and 257 (31.0%) and 537 (64.5%) patientsexpressed concerns about the potential adverse effects of medication and opioid addiction, respectively. Personalitychanges such as irritability or anger were the most frequent accompanying symptoms in 507 (63.8%) patients, followed by depression and sleep disturbance in 462 (58.1%) and 450 (54.5%) patients, respectively. Depression (P = 0.001) and anxiety (P = 0.029) were more common among women, whereas divorce (P = 0.016), family conflict (P < 0.001), unemployment (P < 0.001), suicide attempts (P < 0.001), and restrictions on economic activity (P < 0.001) were more common among men. The frequency of accompanying symptoms, except for suicidal ideation,was higher in the younger patients aged ≤ 40 years than in the older patients aged > 40 years. Conclusions Many patients with chronic pain had concerns about adverse effects or medication tolerance and experienced anxiety, depression, or sleep disturbances. The prevalence of accompanying problems varies according to age and gender.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Background: Chronic pain significantly affects daily activities, mental health, and the interpersonal relationships of patients. Consequently, physicians use various treatments to manage pain. This study investigated the perceptions of treatment, accompanying symptoms, and other problems in patients with chronic pain. Methods: The authors enrolled patients with chronic pain from 19 university hospitals in South Korea. Data was collected on age, gender, diagnosis, disease duration, severity of pain, perception of pain treatment, and accompanying symptoms or problems using an anonymous survey comprising 19 questions. Results: In total, 833 patients with chronic pain completed the survey, and 257 (31.0%) and 537 (64.5%) patientsexpressed concerns about the potential adverse effects of medication and opioid addiction, respectively. Personalitychanges such as irritability or anger were the most frequent accompanying symptoms in 507 (63.8%) patients, followed by depression and sleep disturbance in 462 (58.1%) and 450 (54.5%) patients, respectively. Depression (P = 0.001) and anxiety (P = 0.029) were more common among women, whereas divorce (P = 0.016), family conflict (P < 0.001), unemployment (P < 0.001), suicide attempts (P < 0.001), and restrictions on economic activity (P < 0.001) were more common among men. The frequency of accompanying symptoms, except for suicidal ideation,was higher in the younger patients aged ≤ 40 years than in the older patients aged > 40 years. Conclusions Many patients with chronic pain had concerns about adverse effects or medication tolerance and experienced anxiety, depression, or sleep disturbances. The prevalence of accompanying problems varies according to age and gender.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.