Neuropathic pain markedly impairs quality of life and imposes a substantial socioeconomic burden, while available treatments often provide only partial relief and are limited by safety concerns. The Neuropathic Pain Special Interest Group of the International Association for the Study of Pain (NeuPSIG-IASP) first published pharmacologic recommendations in 2007, followed by a major update in 2015 and a new guideline in 2025. This narrative review specifically compares the 2015 and 2025 NeuPSIG-IASP guidelines, outlining key methodological changes and therapeutic shifts. The 2025 guideline is based on a larger, more rigorous meta-analysis, maintains α2δ-ligands (adds mirogabalin), serotonin-noradrenaline reuptake inhibitors, and tricyclic antidepressants as first-line drugs, downgrades tramadol into the opioid third-line group. It also introduces high-frequency motor-cortex repetitive transcranial magnetic stimulation as a weakly recommended third-line option and discusses implications for Korean clinical practice.

Purpose: Drainage fluid may serve as a biologically informative indicator of immune and infectious status during postsurgical recovery after trauma. However, microbiome shifts in drainage fluid associated with clinical resilience have not yet been characterized. This study aimed to investigate microbial dynamics in drainage fluid across the intensive care unit (ICU) and ward recovery phases in Korean trauma patients. Methods: A total of 25 drainage and 10 stool samples were collected from 10 trauma patients who underwent abdominal surgery at a regional trauma center. Microbial composition was analyzed using 16S ribosomal RNA amplicon sequencing.Alpha and beta diversity were compared between sample types and recovery stages. Linear mixed-effects models were used to identify recovery-associated taxa while adjusting for clinical variables, and predicted metabolic pathways were assessed using PICRUSt2. Results: Drainage fluid harbored distinct microbial communities independent of the intestinal microbiota. Shared taxa between drainage and stool increased significantly in patients with bowel injury, suggesting microbial translocation.Seven genera and 5 species showed significantly decreased abundance during the ward stage, with Modestobacter and Blastococcus tunisiensis demonstrating the highest discriminative ability between recovery stages (area under the curve = 0.721). Predicted metabolic pathways related to fatty acid degradation, amino acid degradation, and pro-inflammatory processes were more active during the ICU stage. Conclusion These findings provide preliminary evidence that drainage fluid microbiome profiles may reflect recovery dynamics following trauma, supporting its potential utility for microbiome-based monitoring and biomarker discovery in trauma surgery.

Behçet disease (BD) is a multisystem inflammatory disorder characterized by a chronic relapsing course. Because immunosuppressive therapy is the mainstay of BD treatment, its use in transplant recipients may attenuate typical clinical manifestations, thereby complicating diagnosis. We report a case of new-onset intestinal BD that developed in a 28-year-old man 3 years after deceased donor kidney transplantation, following an episode of cytomegalovirus colitis. The patient presented with recurrent abdominal pain, diarrhea, weight loss, and hematochezia. Colonoscopy revealed a deep, oval ulcer with well-demarcated, edematous, and nodular margins in the terminal ileum as well as healed scars near the ileocecal valve, findings consistent with intestinal BD.The patient’s symptoms improved after 2 weeks of therapy with colchicine, mesalazine, and mercaptopurine. This case highlights the rare, de novo development of intestinal BD years after kidney transplantation. Intestinal BD should be considered in transplant recipients presenting with unexplained abdominal pain or diarrhea.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

INTRODUCTION: We aimed to understand the awareness and attitudes of elderly Southeast Asians towards telehealth services during the coronavirus disease 2019 (COVID-19) pandemic in this study. METHODS: In this qualitative study, 78 individuals from Singapore (51.3% female, mean age 73.0 ± 7.6 years) were interviewed via telephone between 13 May 2020 and 9 June 2020 during Singapore's first COVID-19 'circuit breaker'. Participants were asked to describe their understanding of telehealth, their experience of and willingness to utilise these services, and the barriers and facilitators underlying their decision. Transcripts were analysed using thematic analysis, guided by the United Theory of Acceptance Use of Technology framework. RESULTS: Of the 78 participants, 24 (30.8%) were able to describe the range of telehealth services available and 15 (19.2%) had previously utilised these services. Conversely, 14 (17.9%) participants thought that telehealth comprised solely home medication delivery and 50 (51.3%) participants did not know about telehealth. Despite the advantages offered by telehealth services, participants preferred in-person consultations due to a perceived lack of human interaction and accuracy of diagnoses, poor digital literacy and a lack of access to telehealth-capable devices. CONCLUSION Our results showed poor overall awareness of the range of telehealth services available among elderly Asian individuals, with many harbouring erroneous views regarding their use. These data suggest that public health education campaigns are needed to improve awareness of and correct negative perceptions towards telehealth services in elderly Asians.
Humans ; COVID-19/epidemiology* ; Female ; Telemedicine ; Aged ; Male ; Singapore/epidemiology* ; Qualitative Research ; Health Knowledge, Attitudes, Practice ; SARS-CoV-2 ; Aged, 80 and over ; Middle Aged ; Pandemics ; Awareness ; Asian People ; Southeast Asian People

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective: This study investigated to what extent a range of depressive symptoms was differentially present depending on age group in Korean population. Methods: Data was pooled from five nationally representative surveys in which 29,418 respondents aged at least 18 years were interviewed face-to-face using the Korean version of the Composite International Diagnostic Interview. A total of 691 (2.1%) respondents were found to have had at least 1 episode of major depressive disorder (MDD) within the last 12 months. Logistic regression analysis was conducted to identify the association between age groups (18–39 years, 40–59 years, and 60 years or older) and 26 depressive symptoms among the respondents with MDD. Results: Associations were observed between somatic symptoms—including insomnia, awakening 2 h earlier—and cognitive symptoms such as feelings of guilt, thoughts of death, and suicidal ideation with the older age group. Whereas, atypical depressive symptoms such as increased appetite, weight gain, and hypersomnia were associated with the younger age group. When adjusted for sociodemographic factors, symptoms such as depressed mood, awakening 2 h earlier, and feeling guilty in the older age group, and hypersomnia, psychomotor retardation, and worse in the morning in the younger age group still remained statistically significant. Furthermore, fatigue and decreased libido were newly associated with the younger age group. Conclusion The findings of this study revealed distinct patterns of symptomatology in MDD based on age groups. These differences should be considered owing to their potential relevance to treatment response and prognosis in the clinical setting.

Background/Aims: With the global increase in patients with solid malignancies, it is helpful to understand the outcomes of intensive care unit (ICU) admission for these patients. This study evaluated the risk factors for ICU mortality and the shortand long-term outcomes in patients with solid malignancies who had unplanned ICU admission. Methods: This retrospective cohort study included patients with solid malignancies treated at the medical ICU of a single tertiary center in South Korea between 2016 and 2022. Results: Among the 955 patients, the ICU mortality rate was 23.5%. Lung cancer was the most common cancer type (34.2%) and was significantly associated with increased ICU mortality (odd ratio [OR] 1.58, p = 0.030). Higher Sequential Organ Failure Assessment scores at ICU admission (OR 1.11, p < 0.001), the need for mechanical ventilation (OR 6.74, p < 0.001), or renal replacement therapy during the ICU stay (OR 2.49, p < 0.001) were significantly associated with higher ICU mortality. The 1-year survival rate after ICU admission was 29.3%, with a median survival of 37 days for patients requiring mechanical deviaventilation, and 23 days for patients requiring renal replacement therapy. Conclusions This study showed that critically ill patients with solid malignancies had poor 1-year survival despite relatively low ICU mortality. These findings highlight the need for careful consideration of ICU admission in patients with solid malignancy, and decision-making should be based on an understanding of the expected short- and long-term prognosis of ICU admission after an informed discussion among patients, families, and physicians.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.