Background: Although Korea ranks first in the suicide rate of elderly individuals, there is limited research on those who attempt suicide, with preventive measures largely based on population-based studies. We compared the demographic and clinical characteristics of elderly individuals who attempted suicide with those of younger adults who visited the emergency department after suicide attempts and identified the factors associated with lethality in the former group. Methods: Individuals who visited the emergency department after a suicide attempt from April 1, 2017, to January 31, 2020, were included. Participants were classified into two groups according to age (elderly, ≥65 years; adult, 18–64 years). Among the 779 adult patients, 123 were elderly. We conducted a chi-square test to compare the demographic and clinical features between these groups and a logistic regression analysis to identify the risk factors for lethality in the elderly group. Results: Most elderly participants were men, with no prior psychiatric history or suicide attempts, and had a higher prevalence of underlying medical conditions and attributed their attempts to physical illnesses. Being sober and planning suicide occurred more frequently in this group. In the elderly group, factors that increased the mortality rate were biological male sex (p<0.05), being accompanied by family members (p<0.05), and poisoning as a suicide method (p<0.01). Conclusion Suicide attempts in elderly individuals have different characteristics from those in younger adults and are associated with physical illness. Suicides in the former group are unpredictable, deliberate, and fatal. Therefore, tailored prevention and intervention strategies addressing the characteristics of those who are elderly and attempt suicide are required.

Objective: This study aimed to use deep learning (DL) to develop a cost-effective and accessible screening tool to improve the detection of cognitive decline, a precursor of Alzheimer’s disease (AD). This study integrating a comprehensive battery of neuropsychological tests adjusted for individual demographic variables such as age, sex, and education level. Methods: A total of 2,863 subjects with subjective cognitive complaints who underwent a comprehensive neuropsychological assessment were included. A random forest classifier was used to discern the most predictive test combinations to distinguish between dementia and nondementia cases. The model was trained and validated on this dataset, focusing on feature importance to determine the cognitive tests that were most indicative of decline. Results: Subjects had a mean age of 72.68 years and an average education level of 7.62 years. The DL model achieved an accuracy of 82.42% and an area under the curve of 0.816, effectively classifying dementia. Feature importance analysis identified significant tests across cognitive domains: attention was gauged by the Trail Making Test Part B, language by the Boston Naming Test, memory by the Rey Complex Figure Test delayed recall, visuospatial skills by the Rey Complex Figure Test copy score, and frontal function by the Stroop Test Word reading time. Conclusion This study showed the potential of DL to improve AD diagnostics, suggesting that a wide range of cognitive assessments could yield a more accurate diagnosis than traditional methods. This research establishes a foundation for future broader studies, which could substantiate the approach and further refine the screening tool.

Objective: This study aimed to explore the characteristics and factors related to changes in cognitive function in vulnerable individuals with cognitive impairment during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Among patients who visited a local university hospital with subjective cognitive complaints, those who had been tested for cognitive function at least once after the onset of COVID-19 and tested regularly at least three times within the last 5 years were included (1st, the initial screening; 2nd, the test immediately before the COVID-19 pandemic; 3rd, the most recent test after the pandemic). Finally, 108 patients were included in this study. They were divided into groups according to whether the Clinical Dementia Rating (CDR) was maintained/improved and deteriorated. We investigated the characteristics of the changes in cognitive function and related factors during COVID-19. Results: When comparing CDR changes before and after COVID-19, there was no significant difference between the two groups (p=0.317). Alternatively, the main effect of the time when the test was conducted was significant (p<0.001). There was also a significant difference in the interaction between the groups and time. When the effect of the interaction was analyzed, the CDR score of the maintained/ improved group significantly decreased before COVID-19 (1st–2nd) (p=0.045). After COVID-19 (2nd–3rd), the CDR score of the deteriorated group was significantly higher than that of the maintained/improved group (p<0.001). Mini-Mental State Examination recall memory and changes in activity during COVID-19 were significantly associated with CDR deterioration. Conclusion Memory dysfunction and decreased activity during the COVID-19 pandemic are strongly related to the deterioration of cognitive impairment.

Acute pancreatitis can range from a mild, self-limiting disease requiring no more than supportive care, to severe disease with life-threatening complications. With the goal of providing a recommendation framework for clinicians to manage acute pancreatitis, and to contribute to improvements in national health care, the Korean Pancreatobiliary Association (KPBA) established the Korean guidelines for acute pancreatitis management in 2013. However, many challenging issues exist which often lead to differences in clinical practices. In addition, with newly obtained evidence regarding acute pancreatitis, there have been great changes in recent knowledge and information regarding this disorder. Therefore, the KPBA committee underwent an extensive revision of the guidelines. The revised guidelines were developed using the Delphi method, and the main topics of the guidelines include the following: diagnosis, severity assessment, initial treatment, nutritional support, convalescent treatment, and the treatment of local complications and necrotizing pancreatitis. Specific recommendations are presented, along with the evidence levels and recommendation grades.

Objective: Dysphagia is a common clinical condition characterized by difficulty in swallowing. It is sub-classified into oropharyngeal dysphagia, which refers to problems in the mouth and pharynx, and esophageal dysphagia, which refers to problems in the esophageal body and esophagogastric junction. Dysphagia can have a significant negative impact one’s physical health and quality of life as its severity increases. Therefore, proper assessment and management of dysphagia are critical for improving swallowing function and preventing complications. Thus a guideline was developed to provide evidence-based recommendations for assessment and management in patients with dysphagia. Methods: Nineteen key questions on dysphagia were developed. These questions dealt with various aspects of problems related to dysphagia, including assessment, management, and complications. A literature search for relevant articles was conducted using Pubmed, Embase, the Cochrane Library, and one domestic database of KoreaMed, until April 2021. The level of evidence and recommendation grade were established according to the Grading of Recommendation Assessment, Development and Evaluation methodology. Results: Early screening and assessment of videofluoroscopic swallowing were recommended for assessing the presence of dysphagia. Therapeutic methods, such as tongue and pharyngeal muscle strengthening exercises and neuromuscular electrical stimulation with swallowing therapy, were effective in improving swallowing function and quality of life in patients with dysphagia. Nutritional intervention and an oral care program were also recommended. Conclusion This guideline presents recommendations for the assessment and management of patients with oropharyngeal dysphagia, including rehabilitative strategies.

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). Results: The AI model showed an AUROC of 0.922 (95% CI, 0.842–0.969) in the internal test set and 0.870 (95% CI, 0.785–0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%–92.0%) and specificity of 91.3% (95% CI, 79.2%–97.6%) for the internal test set and 78.9% (95% CI, 54.4%–93.9%) and 88.2% (95% CI, 78.7%– 94.4%), respectively, for the external test set. With the model’s assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020–0.168; p = 0.012) and 0.069 (95% CI, 0.002–0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074–0.090; p = 0.850). Conclusion A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.

Background: Patient-centered management is becoming increasingly important in gout, but there are limited studies exploring patients' perspectives and preferences. We aimed to investigate patients' perspectives and preferences regarding gout and gout management, and their impacts on adherence to urate lowering therapy (ULT). Methods: A paper-based survey was performed in patients with gout seen at the rheumatology outpatient clinics of 16 tertiary hospitals. The survey included questions regarding demographics, comorbidities, gout attacks, current treatment and adherence, and patients' perspectives and preferences regarding gout and gout management. Multivariate regression analysis was performed to determine the factors associated with ULT adherence. Results: Of 809 surveyed patients with gout, 755 (94.5%) were using ULT. Among those using ULT, 89.1% had ≥ 80% adherence to ULT. Majority of the patients knew management strategies to some extent (94.8%), perceived gout as a life-long disease (91.2%), and were making efforts toward practicing at least one lifestyle modification (89.2%). Most patients (71.9%) obtained information about gout management during their clinic visits.Approximately half of the patients (53.6%) preferred managing their disease with both ULT and lifestyle modification, 28.4% preferred ULT only, and 17.4% preferred lifestyle modification only. Adherence was better in patients with older age (odds ratio [OR], 1.03), those with better knowledge of gout management strategies (OR, 3.56), and those who had preference for ULT (OR, 2.07). Conclusion Patients' perspectives and management preferences had high impacts on adherence to ULT in gout. Consideration of patients' perspectives and preferences is important for achieving the desired clinical outcome in gout.

Background: Patient-centered management is becoming increasingly important in gout, but there are limited studies exploring patients' perspectives and preferences. We aimed to investigate patients' perspectives and preferences regarding gout and gout management, and their impacts on adherence to urate lowering therapy (ULT). Methods: A paper-based survey was performed in patients with gout seen at the rheumatology outpatient clinics of 16 tertiary hospitals. The survey included questions regarding demographics, comorbidities, gout attacks, current treatment and adherence, and patients' perspectives and preferences regarding gout and gout management. Multivariate regression analysis was performed to determine the factors associated with ULT adherence. Results: Of 809 surveyed patients with gout, 755 (94.5%) were using ULT. Among those using ULT, 89.1% had ≥ 80% adherence to ULT. Majority of the patients knew management strategies to some extent (94.8%), perceived gout as a life-long disease (91.2%), and were making efforts toward practicing at least one lifestyle modification (89.2%). Most patients (71.9%) obtained information about gout management during their clinic visits.Approximately half of the patients (53.6%) preferred managing their disease with both ULT and lifestyle modification, 28.4% preferred ULT only, and 17.4% preferred lifestyle modification only. Adherence was better in patients with older age (odds ratio [OR], 1.03), those with better knowledge of gout management strategies (OR, 3.56), and those who had preference for ULT (OR, 2.07). Conclusion Patients' perspectives and management preferences had high impacts on adherence to ULT in gout. Consideration of patients' perspectives and preferences is important for achieving the desired clinical outcome in gout.

Objectives: ：Suicide attempt history is one of the major suicide risk factors and the suicide mortality rate increases with the number of suicide attempts. This study aimed to compare demographic and clinical factors between first and multiple suicide attempters and investigate risk factors for multiple suicide attempts. Methods: ：Participants were 537 patients who were admitted to an emergency room after attempting suicide and divided into two groups (393 first attempters and 144 multiple attempters). Demographic factors, clinical characteristics, and suicide-related characteristics were compared between the two groups. Variables with a p-value lower than 0.1 were included in a multivariate logistic regression analysis. Logistic regression models were considered to identify independent risk factors for multiple suicide attempts. The significance level was set to 0.05. Results: ：Among demographic factors, the two groups differed in age, sex, education, occupation, and marital and cohabitation status. Multiple attempters were more likely to have a mental disorder, current use of psychiatric medication, interpersonal and psychiatric motivation for suicide, current suicidal thoughts, help seeking behavior, awareness of suicide, and agreement with psychiatric follow up. In the regression analysis, unmarried status, mental disorder, interpersonal or psychiatric stress, help seeking behavior, and antidepressant use emerged as significant risk factors of multiple suicide attempts. Conclusion ：The study’s findings suggested that there are differences in the demographic and clinical characteristics of first and multiple suicide attempters. Specific strategies that consider unmarried status, mental disorder, interpersonal or psychiatric stress, help seeking behavior, and antidepressant use may be valuable for future suicide prevention.

Objectives: ：Suicide attempt history is one of the major suicide risk factors and the suicide mortality rate increases with the number of suicide attempts. This study aimed to compare demographic and clinical factors between first and multiple suicide attempters and investigate risk factors for multiple suicide attempts. Methods: ：Participants were 537 patients who were admitted to an emergency room after attempting suicide and divided into two groups (393 first attempters and 144 multiple attempters). Demographic factors, clinical characteristics, and suicide-related characteristics were compared between the two groups. Variables with a p-value lower than 0.1 were included in a multivariate logistic regression analysis. Logistic regression models were considered to identify independent risk factors for multiple suicide attempts. The significance level was set to 0.05. Results: ：Among demographic factors, the two groups differed in age, sex, education, occupation, and marital and cohabitation status. Multiple attempters were more likely to have a mental disorder, current use of psychiatric medication, interpersonal and psychiatric motivation for suicide, current suicidal thoughts, help seeking behavior, awareness of suicide, and agreement with psychiatric follow up. In the regression analysis, unmarried status, mental disorder, interpersonal or psychiatric stress, help seeking behavior, and antidepressant use emerged as significant risk factors of multiple suicide attempts. Conclusion ：The study’s findings suggested that there are differences in the demographic and clinical characteristics of first and multiple suicide attempters. Specific strategies that consider unmarried status, mental disorder, interpersonal or psychiatric stress, help seeking behavior, and antidepressant use may be valuable for future suicide prevention.