Objective: Interpersonal sensitivity, characterized by a heightened awareness of others’ behavior and emotions, is linked to mood disorders. However, current literature lacks a comprehensive analysis of how some items of the Interpersonal Sensitivity Measure (IPSM) interrelate and contribute to the overall construct. This study constructed a network for interpersonal sensitivity symptomatology to identify core IPSM items in patients with mood disorders. Methods: The IPSM, a 36-item self-report scale, was utilized to evaluate interpersonal sensitivity symptoms in 837 participants (major depressive disorder [MDD], n=265; bipolar I disorder [BD I], n=126; and bipolar II disorder [BD II], n=446). We performed exploratory graph analysis, employing regularized partial correlation models to estimate the network structure. Centrality analysis identified core IPSM symptoms for each mood disorder group. Network comparison tests assessed structural differences between the MDD and BD subgroups. Results: Network analysis detected five communities. Item 10 (“I worry about being criticized for things that I have said or done”) showed the highest value in strength. Multiple items on “Interpersonal Worry/Dependency” and “Low Self-Esteem” showed high strength centrality. Network structure invariance and global strength invariance test results indicated no significant differences between the MDD and BD subgroups. Conclusion Our findings emphasize the importance of addressing “Interpersonal Worry/Dependency” and “Low Self-Esteem” in the IPSM network among mood disorder patients based on core items of the network. Additionally, targeted treatments and comprehensive strategies in this aspect could be crucial for managing mood disorders.

Objective: Mood instability (MI) is a clinically significant trait associated with psychiatric disorders. However, there are no concise measurements to evaluate MI. The initial Mood Instability Questionnaire-Trait (MIQ-T) was developed to fill this gap. The current study aimed to create a short form of MIQ-T (MIQ-T-SF) that measures MI with high validity and reliability in the Korean general population. Methods: Of the 59 items in the MIQ-T, 17 items were chosen for the MIQ-T-SF following the factor analysis process. In total, 540 participants completed the MIQ-T-SF. Cronbach’s alpha and McDonald’s omega were used to evaluate reliability. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to determine construct validity. Concurrent validity was confirmed via comparisons with Personality Assessment Inventory-Borderline Features Scale. Measurement invariance across gender and age groups was confirmed before analyzing differences in scores using Kruskal-Wallis test. Results: The MIQ-T-SF displayed expected correlations and high internal consistency (α=0.71–0.90, Ωt=0.72–0.92). Using EFA and CFA, a five-factor structure was confirmed. Measurement invariance was supported, and gender differences were observed. Conclusion The MIQ-T-SF is an accurate and reliable method to detect MI in the Korean general population. The study’s results offer new perspectives for future studies on MI.

Objective: Mood disorder and borderline personality pathology (BPP) are frequently comorbid and relate to childhood trauma. We investigated the relationship between childhood trauma and BPP features in mood disorder patients versus controls. Methods: A total of 488 mood disorder patients, particularly major depressive disorder (MDD), bipolar I disorder (BD I), and bipolar II disorder (BD II), and 734 controls were included. We examined between-group BPP-related differences and correlated between BPP and childhood trauma using the Childhood Trauma Questionnaire-Short Form (CTQ) and the Personality Assessment Inventory–Borderline Features Scale. Results: BD II patients showed significantly higher BPP. Emotional abuse and neglect were prominently associated with BPP, while affective instability and negative relationships exhibited a stronger association with childhood trauma. We also found a positive relationship between childhood trauma and BPP in MDD, BD I, and BD II patients. Conclusion The findings of the present study imply that BPP features are more likely to be found in patients with BD II than BD I or MDD. Mood disorder patients with severe childhood trauma may have higher BPP features. Thus, further study of the relationship between childhood trauma and BPP features could improve the therapeutic approaches and help understand patients with mood disorders.

Objective: The Temperament Evaluation of Memphis, Pisa, Paris, and San Diego Autoquestionnaire (TEMPS-A) has been validated in more than 30 languages and is noted for its broad application in research and clinical settings. This study presents the first attempt to examine the reliability and validity of the TEMPS-A in Korea. Methods: A total of 540 non-clinical participants completed the Korean TEMPS-A, which was adapted from the original English version via a comprehensive translation procedure. Reliability was assessed using Cronbach’s α, and associations between temperaments were examined using Spearman’s correlation coefficient. Exploratory factor analysis (EFA) was performed, and differences in TEMPS-A scores between the gender- and age-based groups were examined using Kruskal-Wallis analysis. Results: The Korean TEMPS-A exhibited excellent internal consistency (0.70–0.91) and significant correlations between subscales. EFA resulted in a two-factor structure: Factor I (depressive, cyclothymic, irritable, and anxious) and Factor II (hyperthymic). Gender and age group differences were observed. Conclusion Overall, our results suggest that TEMPS-A is a reliable and valid measure of affective temperaments for the Korean population. This study opens new possibilities for further research on affective temperaments and their related traits.

Purpose: The severity of a patient’s medical condition, changing pharmacodynamics and pharmacokinetics, and variability in medication highlight the importance of pharmacological intervention by intensive care unit (ICU) specialized pharmacists.
Methods: Retrospective observations of ICU interventions (omission, changes in medicine, side effects, changes in administration route and dosage, redundancy, and nutritional care) performed between April 2017 and March 2018, determined by an interdisciplinary team (including a specialized ICU pharmacist and a surgical intensivist) on their surgical ICU round, were analyzed. Medicinal prescriptions were screened weekly during the surgical ICU round, and interventions were made if any corrections were necessary. Two days later another team including a surgical intensivist, a pharmacist, and a nutritionist evaluated the patients’ nutritional status (performed weekly).
Results: In the 23-bed ICU, the average number of patients whose prescriptions were examined was 22.38 per surgical round. There were 382 interventions made over 1 year, which was 9.68 interventions per day. The interventions were for nutritional care (161 cases, 42.2%), followed by changes in administration route and dosage (94 cases, 24.6%), omission (59 cases, 15.5%), redundancy (40 cases, 10.4%), changes in medicine (15 cases, 3.9%), and side effects (13 cases, 3.4%).
Conclusion The conditions of patients admitted to ICU are typically unstable. Pharmacological interventions suggested by a specialized pharmacist may help control the changing medical condition of patients in ICU. A higher participation of pharmacists specialized in working in an interdisciplinary ICU team-based system could lead to safer treatments.

Background: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea.For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification. Methods: We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers. Results: The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81–0.9), KNN (AUROC, 0.89; 95% CI, 0.85–0.93), RF (AUROC, 0.86; 95% CI, 0.82–0.9) and BERT (AUROC, 0.82; 95% CI, 0.75–0.87) achieved excellent classification performance.Based on SHAP, we found “stress”, “pain score point”, “fever”, “breath”, “head” and “chest” were the important vocabularies for determining KTAS and symptoms. Conclusion We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.

Purpose: The severity of a patient’s medical condition, changing pharmacodynamics and pharmacokinetics, and variability in medication highlight the importance of pharmacological intervention by intensive care unit (ICU) specialized pharmacists.
Methods: Retrospective observations of ICU interventions (omission, changes in medicine, side effects, changes in administration route and dosage, redundancy, and nutritional care) performed between April 2017 and March 2018, determined by an interdisciplinary team (including a specialized ICU pharmacist and a surgical intensivist) on their surgical ICU round, were analyzed. Medicinal prescriptions were screened weekly during the surgical ICU round, and interventions were made if any corrections were necessary. Two days later another team including a surgical intensivist, a pharmacist, and a nutritionist evaluated the patients’ nutritional status (performed weekly).
Results: In the 23-bed ICU, the average number of patients whose prescriptions were examined was 22.38 per surgical round. There were 382 interventions made over 1 year, which was 9.68 interventions per day. The interventions were for nutritional care (161 cases, 42.2%), followed by changes in administration route and dosage (94 cases, 24.6%), omission (59 cases, 15.5%), redundancy (40 cases, 10.4%), changes in medicine (15 cases, 3.9%), and side effects (13 cases, 3.4%).
Conclusion The conditions of patients admitted to ICU are typically unstable. Pharmacological interventions suggested by a specialized pharmacist may help control the changing medical condition of patients in ICU. A higher participation of pharmacists specialized in working in an interdisciplinary ICU team-based system could lead to safer treatments.

Background: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea.For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification. Methods: We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers. Results: The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81–0.9), KNN (AUROC, 0.89; 95% CI, 0.85–0.93), RF (AUROC, 0.86; 95% CI, 0.82–0.9) and BERT (AUROC, 0.82; 95% CI, 0.75–0.87) achieved excellent classification performance.Based on SHAP, we found “stress”, “pain score point”, “fever”, “breath”, “head” and “chest” were the important vocabularies for determining KTAS and symptoms. Conclusion We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.

Background: Most abdominal aortic aneurysms are degenerative atherosclerotic aneurysms. Inflammatory or infected abdominal aortic aneurysms, which show a slightly different clinical course, are rarely encountered in clinical settings. Therefore, we aimed to investigate the clinical course of these variants of abdominal aortic aneurysms. Methods: This retrospective study included 32 patients with atypical inflammatory or infected abdominal aortic aneurysms who underwent emergent graft replacement between November 1997 and December 2017. Patients were followed up at the outpatient clinic for a mean period of 4.9±6.9 years. We analyzed the patients’ clinical course and compared it with that of patients with atherosclerotic abdominal aortic aneurysms. Results: There was 1 surgical mortality (3.0%) in a case complicated by aneurysmal free rupture. In 2 cases of infected abdominal aortic aneurysms, anastomotic complications developed immediately postoperatively. During the follow-up period, 10 patients (30%) developed graft complications, and 9 of them underwent reoperations; of these, 2 patients (22.2%) died of postoperative complications after the second operation, whereas 2 patients survived despite graft occlusion. Conclusion Patients with inflammatory abdominal aneurysms frequently develop postoperative graft complications requiring secondary surgical treatment, so they require close mandatory postoperative follow-up.