Purpose: Panic disorder (PD) and PD with comorbid agoraphobia (PDA) share similar clinical characteristics but possess distinct symptom structures. However, studies specifically investigating the differences between PD and PDA are rare. Thus, the present study conducted a network analysis to examine the clinical networks of PD and PDA, focusing on panic symptom severity, anxiety sensitivity, anticipatory fear, and avoidance responses. By comparing the differences in network structures between PD and PDA, with the goal of identifying the central and bridge, we suggest clinical implications for the development of targeted interventions. Materials and Methods: A total sample (n=147; 55 male, 92 female) was collected from the psychiatric outpatient clinic of the university hospital. We conducted network analysis to examine crucial nodes in the PD and PDA networks and compared the two networks to investigate disparities and similarities in symptom structure. Results The most influential node within the PD network was Anxiety Sensitivity Index-Revised (ASI-R1; fear of respiratory symptom), whereas Panic Disorder Severity Scale (PDSS5; phobic avoidance of physical sensations) had the highest influence in the PDA network. Additionally, bridge centrality estimates indicated that each of the two nodes met the criteria for “bridge nodes” within their respective networks: ASI-R1 (fear of respiratory symptom) and Albany Panic and Phobic Questionnaire (APPQ3; interoceptive fear) for the PD group, and PDSS5 (phobic avoidance of physical sensation) and APPQ1 (panic frequency) for the PDA group Conclusion: Although the network comparison test did not reveal statistical differences between the two networks, disparities in community structure, as well as central and bridging symptoms, were observed, suggesting the possibility of distinct etiologies and treatment targets for each group. The clinical implications derived from the similarities and differences between PD and PDA networks are discussed.

Purpose: Panic disorder (PD) and PD with comorbid agoraphobia (PDA) share similar clinical characteristics but possess distinct symptom structures. However, studies specifically investigating the differences between PD and PDA are rare. Thus, the present study conducted a network analysis to examine the clinical networks of PD and PDA, focusing on panic symptom severity, anxiety sensitivity, anticipatory fear, and avoidance responses. By comparing the differences in network structures between PD and PDA, with the goal of identifying the central and bridge, we suggest clinical implications for the development of targeted interventions. Materials and Methods: A total sample (n=147; 55 male, 92 female) was collected from the psychiatric outpatient clinic of the university hospital. We conducted network analysis to examine crucial nodes in the PD and PDA networks and compared the two networks to investigate disparities and similarities in symptom structure. Results The most influential node within the PD network was Anxiety Sensitivity Index-Revised (ASI-R1; fear of respiratory symptom), whereas Panic Disorder Severity Scale (PDSS5; phobic avoidance of physical sensations) had the highest influence in the PDA network. Additionally, bridge centrality estimates indicated that each of the two nodes met the criteria for “bridge nodes” within their respective networks: ASI-R1 (fear of respiratory symptom) and Albany Panic and Phobic Questionnaire (APPQ3; interoceptive fear) for the PD group, and PDSS5 (phobic avoidance of physical sensation) and APPQ1 (panic frequency) for the PDA group Conclusion: Although the network comparison test did not reveal statistical differences between the two networks, disparities in community structure, as well as central and bridging symptoms, were observed, suggesting the possibility of distinct etiologies and treatment targets for each group. The clinical implications derived from the similarities and differences between PD and PDA networks are discussed.

Purpose: Panic disorder (PD) and PD with comorbid agoraphobia (PDA) share similar clinical characteristics but possess distinct symptom structures. However, studies specifically investigating the differences between PD and PDA are rare. Thus, the present study conducted a network analysis to examine the clinical networks of PD and PDA, focusing on panic symptom severity, anxiety sensitivity, anticipatory fear, and avoidance responses. By comparing the differences in network structures between PD and PDA, with the goal of identifying the central and bridge, we suggest clinical implications for the development of targeted interventions. Materials and Methods: A total sample (n=147; 55 male, 92 female) was collected from the psychiatric outpatient clinic of the university hospital. We conducted network analysis to examine crucial nodes in the PD and PDA networks and compared the two networks to investigate disparities and similarities in symptom structure. Results The most influential node within the PD network was Anxiety Sensitivity Index-Revised (ASI-R1; fear of respiratory symptom), whereas Panic Disorder Severity Scale (PDSS5; phobic avoidance of physical sensations) had the highest influence in the PDA network. Additionally, bridge centrality estimates indicated that each of the two nodes met the criteria for “bridge nodes” within their respective networks: ASI-R1 (fear of respiratory symptom) and Albany Panic and Phobic Questionnaire (APPQ3; interoceptive fear) for the PD group, and PDSS5 (phobic avoidance of physical sensation) and APPQ1 (panic frequency) for the PDA group Conclusion: Although the network comparison test did not reveal statistical differences between the two networks, disparities in community structure, as well as central and bridging symptoms, were observed, suggesting the possibility of distinct etiologies and treatment targets for each group. The clinical implications derived from the similarities and differences between PD and PDA networks are discussed.

Background: Obstructive sleep apnea (OSA) increases the risk of dyslipidemia, thereby heightening the likelihood of developing cardiovascular diseases. Hence, prior to the diagnosis of OSA, it is essential to investigate the association with dyslipidemia in at-risk individuals. Methods: Using raw data obtained from the 2021 Korea National Health and Nutrition Examination Survey, we examined the details of 2,882 participants aged 40 years and older, who were not diagnosed with sleep apnea and did not use lipid-lowering medications. Participants who had reported snoring, fatigue, or witnessed apnea episodes, were categorized into the “OSA Risk Group” (OSARG), and logistic regression analysis was performed to examine the association between serum lipid levels and dyslipidemia. Results: OSARG participants were found to have a higher prevalence of hypertriglyceridemia and increased prevalence of dyslipidemia. After adjusting for sex, age, education, economic status, housing type, obesity, physical activity, smoking, and alcohol consumption, only hypertriglyceridemia remained significantly associated with the OSARG, and the odds ratio for OSA was significantly higher among those patients with hypertriglyceridemia (1.39; 95% confidence interval, 1.06–1.83). Conclusion Among adults aged 40 years and older, compared with those not at risk, individuals at risk of OSA were characterized by a higher prevalence of hypertriglyceridemia. These findings thus indicate that the management of hypertriglyceridemia and dyslipidemia may be warranted prior to the diagnosis of OSA in this population.

Background: Obstructive sleep apnea (OSA) increases the risk of dyslipidemia, thereby heightening the likelihood of developing cardiovascular diseases. Hence, prior to the diagnosis of OSA, it is essential to investigate the association with dyslipidemia in at-risk individuals. Methods: Using raw data obtained from the 2021 Korea National Health and Nutrition Examination Survey, we examined the details of 2,882 participants aged 40 years and older, who were not diagnosed with sleep apnea and did not use lipid-lowering medications. Participants who had reported snoring, fatigue, or witnessed apnea episodes, were categorized into the “OSA Risk Group” (OSARG), and logistic regression analysis was performed to examine the association between serum lipid levels and dyslipidemia. Results: OSARG participants were found to have a higher prevalence of hypertriglyceridemia and increased prevalence of dyslipidemia. After adjusting for sex, age, education, economic status, housing type, obesity, physical activity, smoking, and alcohol consumption, only hypertriglyceridemia remained significantly associated with the OSARG, and the odds ratio for OSA was significantly higher among those patients with hypertriglyceridemia (1.39; 95% confidence interval, 1.06–1.83). Conclusion Among adults aged 40 years and older, compared with those not at risk, individuals at risk of OSA were characterized by a higher prevalence of hypertriglyceridemia. These findings thus indicate that the management of hypertriglyceridemia and dyslipidemia may be warranted prior to the diagnosis of OSA in this population.

Background: Obstructive sleep apnea (OSA) increases the risk of dyslipidemia, thereby heightening the likelihood of developing cardiovascular diseases. Hence, prior to the diagnosis of OSA, it is essential to investigate the association with dyslipidemia in at-risk individuals. Methods: Using raw data obtained from the 2021 Korea National Health and Nutrition Examination Survey, we examined the details of 2,882 participants aged 40 years and older, who were not diagnosed with sleep apnea and did not use lipid-lowering medications. Participants who had reported snoring, fatigue, or witnessed apnea episodes, were categorized into the “OSA Risk Group” (OSARG), and logistic regression analysis was performed to examine the association between serum lipid levels and dyslipidemia. Results: OSARG participants were found to have a higher prevalence of hypertriglyceridemia and increased prevalence of dyslipidemia. After adjusting for sex, age, education, economic status, housing type, obesity, physical activity, smoking, and alcohol consumption, only hypertriglyceridemia remained significantly associated with the OSARG, and the odds ratio for OSA was significantly higher among those patients with hypertriglyceridemia (1.39; 95% confidence interval, 1.06–1.83). Conclusion Among adults aged 40 years and older, compared with those not at risk, individuals at risk of OSA were characterized by a higher prevalence of hypertriglyceridemia. These findings thus indicate that the management of hypertriglyceridemia and dyslipidemia may be warranted prior to the diagnosis of OSA in this population.

Purpose: Panic disorder (PD) and PD with comorbid agoraphobia (PDA) share similar clinical characteristics but possess distinct symptom structures. However, studies specifically investigating the differences between PD and PDA are rare. Thus, the present study conducted a network analysis to examine the clinical networks of PD and PDA, focusing on panic symptom severity, anxiety sensitivity, anticipatory fear, and avoidance responses. By comparing the differences in network structures between PD and PDA, with the goal of identifying the central and bridge, we suggest clinical implications for the development of targeted interventions. Materials and Methods: A total sample (n=147; 55 male, 92 female) was collected from the psychiatric outpatient clinic of the university hospital. We conducted network analysis to examine crucial nodes in the PD and PDA networks and compared the two networks to investigate disparities and similarities in symptom structure. Results The most influential node within the PD network was Anxiety Sensitivity Index-Revised (ASI-R1; fear of respiratory symptom), whereas Panic Disorder Severity Scale (PDSS5; phobic avoidance of physical sensations) had the highest influence in the PDA network. Additionally, bridge centrality estimates indicated that each of the two nodes met the criteria for “bridge nodes” within their respective networks: ASI-R1 (fear of respiratory symptom) and Albany Panic and Phobic Questionnaire (APPQ3; interoceptive fear) for the PD group, and PDSS5 (phobic avoidance of physical sensation) and APPQ1 (panic frequency) for the PDA group Conclusion: Although the network comparison test did not reveal statistical differences between the two networks, disparities in community structure, as well as central and bridging symptoms, were observed, suggesting the possibility of distinct etiologies and treatment targets for each group. The clinical implications derived from the similarities and differences between PD and PDA networks are discussed.

Background: Obstructive sleep apnea (OSA) increases the risk of dyslipidemia, thereby heightening the likelihood of developing cardiovascular diseases. Hence, prior to the diagnosis of OSA, it is essential to investigate the association with dyslipidemia in at-risk individuals. Methods: Using raw data obtained from the 2021 Korea National Health and Nutrition Examination Survey, we examined the details of 2,882 participants aged 40 years and older, who were not diagnosed with sleep apnea and did not use lipid-lowering medications. Participants who had reported snoring, fatigue, or witnessed apnea episodes, were categorized into the “OSA Risk Group” (OSARG), and logistic regression analysis was performed to examine the association between serum lipid levels and dyslipidemia. Results: OSARG participants were found to have a higher prevalence of hypertriglyceridemia and increased prevalence of dyslipidemia. After adjusting for sex, age, education, economic status, housing type, obesity, physical activity, smoking, and alcohol consumption, only hypertriglyceridemia remained significantly associated with the OSARG, and the odds ratio for OSA was significantly higher among those patients with hypertriglyceridemia (1.39; 95% confidence interval, 1.06–1.83). Conclusion Among adults aged 40 years and older, compared with those not at risk, individuals at risk of OSA were characterized by a higher prevalence of hypertriglyceridemia. These findings thus indicate that the management of hypertriglyceridemia and dyslipidemia may be warranted prior to the diagnosis of OSA in this population.

Purpose: Panic disorder (PD) and PD with comorbid agoraphobia (PDA) share similar clinical characteristics but possess distinct symptom structures. However, studies specifically investigating the differences between PD and PDA are rare. Thus, the present study conducted a network analysis to examine the clinical networks of PD and PDA, focusing on panic symptom severity, anxiety sensitivity, anticipatory fear, and avoidance responses. By comparing the differences in network structures between PD and PDA, with the goal of identifying the central and bridge, we suggest clinical implications for the development of targeted interventions. Materials and Methods: A total sample (n=147; 55 male, 92 female) was collected from the psychiatric outpatient clinic of the university hospital. We conducted network analysis to examine crucial nodes in the PD and PDA networks and compared the two networks to investigate disparities and similarities in symptom structure. Results The most influential node within the PD network was Anxiety Sensitivity Index-Revised (ASI-R1; fear of respiratory symptom), whereas Panic Disorder Severity Scale (PDSS5; phobic avoidance of physical sensations) had the highest influence in the PDA network. Additionally, bridge centrality estimates indicated that each of the two nodes met the criteria for “bridge nodes” within their respective networks: ASI-R1 (fear of respiratory symptom) and Albany Panic and Phobic Questionnaire (APPQ3; interoceptive fear) for the PD group, and PDSS5 (phobic avoidance of physical sensation) and APPQ1 (panic frequency) for the PDA group Conclusion: Although the network comparison test did not reveal statistical differences between the two networks, disparities in community structure, as well as central and bridging symptoms, were observed, suggesting the possibility of distinct etiologies and treatment targets for each group. The clinical implications derived from the similarities and differences between PD and PDA networks are discussed.

Background: Nirmatrelvir-ritonavir is highly effective in preventing severe coronavirus disease 2019 (COVID-19) in high-risk patients with mild-to-moderate severity. However, real-world performance data are limited, and the drug is not so acceptable to the COVID-19 patients at high risk who need it in Korea. Methods: To evaluate the effectiveness of nirmatrelvir-ritonavir, we conducted a propensity score-matched retrospective cohort study on patients with mild-to-moderate COVID-19 at high risk for a severe disease who were hospitalized at four hospitals in South Korea from February 2022 to April 2022. A total of 236 patients in the treatment group (administered nirmatrelvir-ritonavir) and 236 in the matched control group (supportive care only) were analyzed for the primary outcome, i.e., the time to oxygen support-free survival. The secondary outcome was a composite result of disease progression. The reason for not prescribing nirmatrelvir-ritonavir to the indicated patients was also investigated. Results: The treatment group showed significantly longer oxygen support-free survival than the matched control group (adjusted hazard ratio [aHR], 0.07; 95% confidence interval [CI], 0.01–0.31; P < 0.001). Multivariate Cox regression analysis showed that age (aHR, 1.03; 95% CI, 1.00–1.07), National Early Warning Score-2 at admission (aHR, 1.36; 95% CI, 1.08–1.71), nirmatrelvir-ritonavir treatment, female sex (aHR, 0.37; 95% CI, 0.15–0.88), and time from symptom onset to admission (aHR, 0.67; 95% CI, 0.48–0.95) were significantly associated with oxygen therapy. However, none of the factors were related to the composite outcome. In the unmatched control group, 19.9% of 376 patients had documented explanations for nirmatrelvir-ritonavir non-prescription, and 44.0% of these were due to contraindication criteria. In the treatment group, 10.9% of patients discontinued the medication primarily because of adverse events (71.4%), with gastrointestinal symptoms being the most common (50.0%). Conclusion Nirmatrelvir-ritonavir treatment significantly reduced oxygen therapy requirements in high-risk patients with COVID-19 during the omicron variant surge in South Korea. Physicians are encouraged to consider the active use of nirmatrelvir-ritonavir and to be watchful for gastrointestinal symptoms during medication.