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.

Background: Lung ultrasound (LUS) has proven valuable in the initial assessment of coronavirus disease 2019 (COVID-19), but its role in detecting pulmonary fibrosis following intensive care remains unclear. This study aims to assess the presence of pulmonary sequelae and fibrosis-like changes using LUS in survivors of severe COVID-19 pneumonia one month after discharge. Methods: We prospectively enrolled patients with severe COVID-19 who required mechanical ventilation in the intensive care unit (ICU) and conducted LUS assessments from admission to the outpatient visit after discharge. We tracked changes in key LUS findings and applied our proprietary LUS scoring system. To evaluate LUS accuracy, we correlated measured LUS values with computed tomography scores. Results: We evaluated B-line presence, pleural thickness, and consolidation in 14 eligible patients. The LUS scores exhibited minimal changes, with values of 19.1, 19.2, and 17.5 at admission, discharge, and the outpatient visit, respectively. Notably, the number of B-lines decreased significantly, from 1.92 at admission to 0.56 at the outpatient visit (p<0.05), while pleural thickness increased significantly, from 2.05 at admission to 2.48 at the outpatient visit (p≤0.05). Conclusion This study demonstrates that LUS can track changes in lung abnormalities in severe COVID-19 patients from ICU admission through to outpatient follow-up. While pleural thickening and B-line patterns showed significant changes, no correlation was found between LUS and high-resolution computed tomography fibrosis scores. These findings suggest that LUS may serve as a supplementary tool for assessing pulmonary recovery in severe COVID-19 cases.

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: Residential smoking cessation therapy programs offer intensive treatment for heavy smokers who struggle to quit independently, particularly those with high nicotine dependence and health conditions that necessitate urgent cessation. While previous studies have established the effectiveness of such programs and identified various factors influencing smoking cessation success, it remains unclear how changes in smokers’ thoughts and attitudes following residential therapy correlate with their ability to quit smoking. We investigated the relationship between smoking cessation-related characteristics, smoking-related psychological status, and participants’ smoking cessation success after a residential smoking cessation therapy program. Methods: From January 2017 to December 2018, 291 participants completed the program. All participants completed questionnaires on smoking cessation-related characteristics and smoking-related psychological status before the program and on the 5th day. Six months later, smoking cessation success was assessed using a urine cotinine test. Results: After 6 months, 222 participants successfully quit smoking, while 69 failed. The success and failure groups exhibited statistically significant differences in age, marital status, total smoking duration, stress, and emotion regulation strategies. Participants who used ineffective emotion regulation strategies more frequently had a lower rate of smoking cessation success (odds ratio [OR], 0.969; 95% confidence interval [CI], 0.948–0.991). Moreover, an increase in the perception of the negative effects of smoking cessation (OR, 0.982; 95% CI, 0.967–0.997) and smoking temptation (OR, 0.960; 95% CI, 0.929–0.993) was associated with higher cessation success. Conclusion Emotion regulation strategies, perceptions of the effects of smoking cessation, and smoking temptation were associated with successful smoking cessation.

Background and Objectives: The Korean Organ Transplant Registry (KOTRY) provided data for this third official report on adult heart transplantation (HT), including information from 709 recipients. Methods: Data from HTs performed at seven major centers in Korea between March 2014 and December 2020 were analyzed, focusing on immunosuppression, acute rejection, cardiac allograft vasculopathy (CAV), post-transplant survival, and mechanical circulatory support (MCS) usage. Results: The median ages of the recipients and donors were 56.0 and 43.0 years, respectively.Cardiomyopathy and ischemic heart disease were the most common preceding conditions for HT. A significant portion of patients underwent HT at waiting list status 1 and 0. In the multivariate analysis, a predicted heart mass mismatch was associated with a higher risk of 1-year mortality. Patients over 70 years old had a significantly increased risk of 6-year mortality. The risk of CAV was higher for male donors and donors older than 45 years. Acute rejection was more likely in patients with panel reactive antibody levels above 80%, while statin use was associated with a reduced risk. The employment of left ventricular assist device as a bridge to transplantation increased from 2.17% to 22.4%. Pre-transplant extra-corporeal membrane oxygenation was associated with worse post-transplant survival. Conclusions In this third KOTRY report, we analyzed changes in the characteristics of adult HT recipients and donors and their impact on post-transplant outcomes. The most notable discovery was the increased use of MCS before HT and their impact on post-transplant outcomes.

Background: We hypothesized that intranasal combination of dexmedetomidine (2 μg/kg) and ketamine (3 mg/kg) (IN DEXKET) improves the success rate of sedation in pediatric patients compared with chloral hydrate (CH; 50 mg/kg). Methods: This prospective, two-center, single-blinded, randomized controlled trial involved 136 pediatric patients (aged < 7 years) requiring procedural sedation. The participants were randomized to receive CH or IN DEXKET via a mucosal atomizer device. The primary outcome was the success rate of sedation (Pediatric Sedation State Scale, scores 1–3) within 15 min. The secondary outcomes included sedation failure at 30 min and overall complications of first-attempt sedation. Results: After excluding eight patients, 128 were included (CH = 66, IN DEXKET = 62). IN DEXKET showed a similar sedation success rate (75.8% [47/62] vs. 66.7% [44/66]; P = 0.330) but a lower complication rate (3.2% [2/62] vs. 16.7% [11/66]; P = 0.017) than CH. In the subgroup analysis for patients aged < 1 year, IN DEXKET showed a reduced complication rate than CH (2.6% [1/38] vs. 22.9% [8/35]; P = 0.012). In the subgroup analysis of children aged 1–7 years, IN DEXKET showed a higher sedation success rate within 15 min (79.2% [19/24] vs. 51.6% [16/31]; P = 0.049) and a lower sedation failure after 30 min (0% vs. 29.0% [9/31]; P = 0.003) than CH. Conclusions The intranasal combination of dexmedetomidine (2 μg/kg) and ketamine (3 mg/kg) is a safe and effective alternative to CH (50 mg/kg) for sedation in pediatric patients aged < 7 years.

Purpose: This study aimed to evaluate the impact of declining surgical residency program enrollment on patient care and outcomes in colorectal cancer surgeries. Methods: This retrospective observational study included 676 patients (410 males; median age: 69 years) who underwent colorectal cancer surgery at Pusan National University Hospital between January 2018 and June 2024. Patients were divided into Group A (before December 31, 2023; with residents) and Group B (after January 1, 2024; without residents). All surgeries were performed by a single attending surgeon. Results: Preoperative variables were comparable between groups. Group A had more emergency and open surgeries, and a higher proportion of advanced-stage cancers. Overall complication rates were similar, but Group B had a longer hospital stay (9.72 days vs. 11.95 days). Specific complications such as anastomotic leakage and surgical site infections differed significantly. The overall number of surgical procedures declined markedly in 2024 compared to 2018 (77.1% vs. 49.9%). Conclusion The absence of residents did not increase overall complication rates but was associated with longer hospital stays and shifts in clinical practice. Greater reliance on attending surgeons contributed to more defensive decision-making and conservative patient management. Addressing these issues requires systemic reforms, including multidisciplinary collaboration and legal protections to improve surgical care.

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.