Currently, the midurethral sling (MUS) is widely used as a standard treatment in patients with stress urinary incontinence (SUI). Several studies have reported the failure rate of MUS to be approximately 5%–20%. In general, sling failure can be defined as persistent SUI after surgery or a temporary improvement in incontinence followed by recurrence. Failure is also often considered to include cases requiring secondary surgery due to mesh exposure, postoperative voiding difficulty, de novo urgency/urge incontinence, and severe postoperative pain. Because of the lack of large-scale, high-quality research on this topic, no clear guidelines exist for second-line management. To date, transurethral bulking agent injections, tape shortening, repeat MUS, pubovaginal sling (PVS) using autologous fascia, and Burch colposuspension are available options for second-line surgery. Repeat MUS is the most widely used second-line surgical method at present. Bulking agent injections have lower durability and efficacy than other treatments. Tape shortening demonstrates a relatively low success rate, but comparable outcomes if the period from first treatment to relapse is short. In patients with intrinsic sphincter deficiency, PVS and retropubic (RP) MUS can be considered first as second-line management because of their higher success rate than other treatments. When revision or reoperation is required due to prior mesh-related complications, PVS or colposuspension, which is performed without a synthetic mesh, is appropriate for second-line surgery. For patients with detrusor underactivity, a readjustable sling can be a better option because of the high risk of postoperative voiding dysfunction in PVS or RP slings.

Currently, the midurethral sling (MUS) is widely used as a standard treatment in patients with stress urinary incontinence (SUI). Several studies have reported the failure rate of MUS to be approximately 5%–20%. In general, sling failure can be defined as persistent SUI after surgery or a temporary improvement in incontinence followed by recurrence. Failure is also often considered to include cases requiring secondary surgery due to mesh exposure, postoperative voiding difficulty, de novo urgency/urge incontinence, and severe postoperative pain. Because of the lack of large-scale, high-quality research on this topic, no clear guidelines exist for second-line management. To date, transurethral bulking agent injections, tape shortening, repeat MUS, pubovaginal sling (PVS) using autologous fascia, and Burch colposuspension are available options for second-line surgery. Repeat MUS is the most widely used second-line surgical method at present. Bulking agent injections have lower durability and efficacy than other treatments. Tape shortening demonstrates a relatively low success rate, but comparable outcomes if the period from first treatment to relapse is short. In patients with intrinsic sphincter deficiency, PVS and retropubic (RP) MUS can be considered first as second-line management because of their higher success rate than other treatments. When revision or reoperation is required due to prior mesh-related complications, PVS or colposuspension, which is performed without a synthetic mesh, is appropriate for second-line surgery. For patients with detrusor underactivity, a readjustable sling can be a better option because of the high risk of postoperative voiding dysfunction in PVS or RP slings.

Objectives: This study aims to examine the effect of adolescent suicide survivors’ experience on suicide death risk, and the effect of referral to mental health services (hereafter referral) in this regard. Methods: This study used the data of 878 suicide-deceased and suicide-attempted adolescents aged 8–19 years, managed by the Suicide and School Mental Health Institute from 2016 to 2018. Results: Regression analysis for main effects showed that although suicide experience had no direct effect on suicide death, non-referral status was associated with a greater risk of death by suicide. While the “non-suicide survivor with non-referral” and “suicide survivor with non-referral” groups showed 1.87 [adjusted odds ratio=1.87, 95% confidence interval (CI)=1.21–2.89] and 4.59 (adjusted odds ratio= 4.59, 95% CI=2.02–10.42) times higher odds of suicide death, respectively, the “suicide survivor with referral” group showed no difference compared to the “non-suicide survivor with referral” group. Conclusion From these findings, there is a need to strengthen referral to mental health services and apply complicated grief treatment to improve the mental health of adolescent suicide survivors.

Objectives: ：Somatic symptom disorder (SSD) is characterized by the manifestation of a variety of physical symptoms, but little is known about differences in autonomic nervous system activity according to symptom severity, especially within patient groups. In this study, we examined differences in heart rate variability (HRV) across symptom severity in a group of SSD patients to analyze a representative marker of autonomic nervous system changes by symptoms severity. Methods: ：Medical records were retrospectively reviewed for patients who were diagnosed with SSD based on DSM-5 from September 18, 2020 to October 29, 2021. We applied inverse probability of treatment weighting (IPTW) methods to generate more homogeneous comparisons in HRV parameters by correcting for selection biases due to sociodemographic and clinical characteristic differences between groups. Results: ：There were statistically significant correlations between the somatic symptom severity and LF (nu), HF (nu), LF/HF, as well as SD1/SD2 and Alpha1/Alpha2. After IPTW estimation, the mild to moderate group was corrected to 27 (53.0%) and the severe group to 24 (47.0%), and homogeneity was achieved as the differences in demographic and clinical characteristics were not significant. The analysis of inverse probability weighted regression adjustment model showed that the severe group was associated with significantly lower RMSSD (β=-0.70, p=0.003) and pNN20 (β=-1.04, p=0.019) in the time domain and higher LF (nu) (β=0.29, p<0.001), lower HF (nu) (β=-0.29, p<0.001), higher LF/HF (β=1.41, p=0.001), and in the nonlinear domain, significant differences were tested for SampEn15 (β=-0.35, p=0.014), SD1/SD2 (β=-0.68, p<0.001), and Alpha1/Alpha2 (ß=0.43,p=0.001). Conclusions ：These results suggest that differences in HRV parameters by SSD severity were showed in the time, frequency and nonlinear domains, specific parameters demonstrating significantly higher sympathetic nerve activity and reduced ability of the parasympathetic nervous system in SSD patients with severe symptoms.

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.

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: This study aimed to analyze the suicidal warning signs of Korean students with different psychometric profiles based on teacher reports. Methods: This was a retrospective cohort study based on Korean school teachers’ responses to the Student Suicide Report Form. In total, 546 consecutive cases of student suicide were reported from 2017 to 2020. After missing data were excluded, 528 cases were included. The report consisted of demographic factors, the Korean version of the Strengths and Difficulties Questionnaire (SDQ) for teacher reporting, and warning signs of suicide. Frequency analysis, multiple response analysis, the χ2 test, and Latent Class Analysis (LCA) were performed. Results: Based on the scores of the Korean version of the teacher-reported SDQ, the group was divided into nonsymptomatic (n = 411) and symptomatic (n = 117) groups. Based on the LCA results, four latent hierarchical models were selected. The four classes of deceased students showed significant differences in school type (χ2 = 20.410, P < 0.01), physical illness (χ2 = 7.928, P < 0.05), mental illness (χ2 = 94.332, P < 0.001), trigger events (χ2 = 14.817, P < 0.01), self-harm experience (χ2 = 30.618, P < 0.001), suicide attempts (χ2 = 24.072, P < 0.001), depressive symptoms (χ2 = 59.561, P < 0.001), anxiety (χ2 = 58.165, P < 0.001), impulsivity (χ2 = 62.241, P < 0.001), and social problems (χ2 = 64.952, P < 0.001). Conclusion Notably, many students who committed suicide did not have any psychiatric pathology. The proportion of the group with a prosocial appearance was also high. Therefore, the actual suicide warning signals were similar regardless of students’ difficulties and prosocial behaviors, so it is necessary to include this information in gatekeeper education.