Delirium is a common but serious complication in critically ill patients. Family visitation has been shown to reduce delirium; however, during the coronavirus disease 2019 (COVID-19) pandemic, intensive care units (ICUs) restricted regular visitation to prevent the spread of infection. This study aimed to evaluate the association between visitation policies and incidence of delirium in the ICUs. Methods: This was a retrospective before-and-after study conducted in medical and surgical ICUs at a tertiary hospital. Adult patients admitted to an ICU during one of two periods were included: before the COVID-19 pandemic (June 2017 to May 2019) with regular visitation and during the pandemic (June 2020 to May 2022) with prohibited visitation. Delirium was assessed using the Confusion Assessment Method for the ICU. The primary outcome was association between delirium incidence and visitation policy. Results: Totals of 1,566 patients from the pre-COVID-19 period and 1,404 patients from the COVID-19 period were analyzed. The incidence of delirium was higher during the COVID-19 period (48.1% vs. 38.4%, P<0.001). After adjusting for relevant variables, the restricted visitation policy during COVID-19 remained a risk factor for delirium (odds ratio, 1.37; 95% CI, 1.13–1.65; P=0.001). Conclusions: Complete restriction of ICU visitations during the COVID-19 pandemic was associated with a significant increase in delirium incidence. These findings suggest the importance of visitation policies on patient outcomes and suggest the need for alternative strategies, such as video visitation, to mitigate the adverse effects of visitation restrictions during pandemics.

We explored the utility of cerebrospinal fluid (CSF) circulating tumor DNA (ctDNA) sequencing as a noninvasive diagnostic tool for detecting central nervous system (CNS) involvement in patients with diffuse large B-cell lymphoma (DLBCL). Secondary CNS involvement in DLBCL, although rare (~5% of cases), presents diagnostic and prognostic challenges during systemic disease progression or relapse. Effective treatment is impeded by the blood–brain barrier. This was a prospective cohort study (Samsung Lymphoma Cohort Study III) involving 17 patients with confirmed CNS involvement. High-throughput sequencing was conducted using targeted gene panels designed to detect low-frequency variants and copy number alterations pertinent to lymphomas in ctDNA extracted from archived CSF samples. Despite challenges such as low DNA concentrations affecting library construction, the overall variant detection rate was 76%. Detected variants included those in genes commonly implicated in CNS lymphoma, such as MYD88. The study highlights the potential of CSF ctDNA sequencing to identify CNS involvement in DLBCL, providing a promising alternative to more invasive diagnostic methods such as brain biopsy, which are not always feasible. Further validation is necessary to establish the clinical utility of this method, which could significantly enhance the management and outcomes of DLBCL patients with suspected CNS involvement.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Purpose: Breast cancer gene (BRCA) mutation is a well-known risk factor for breast cancer, and clinical interest in prophylactic mastectomy has increased in recent years.We investigated patients who were BRCA mutation carriers and underwent contralateral risk-reducing mastectomy (RRM), focusing on the incidence of occult malignancy after contralateral RRM. Methods: Prospectively collected data of patients with breast cancer treated at a single institution were retrospectively reviewed. Patients who underwent RRM with BRCA mutation who underwent RRM between January 2010 and November 2023 were included in this study.Among patients who underwent contralateral RRM, those with a primary cancer diagnosis were included, and those with occult malignancy on the contralateral RRM side were reviewed additionally. The demographics and pathologies of both primary breast cancer and occult malignancies were evaluated. Results: In our institution, 925 patients were identified as BRCA mutation carriers, and 320 patients underwent contralateral RRM along with primary breast cancer surgery. BRCA2 mutation occurred more frequently (54.8%) in the overall BRCA mutation cohort. Furthermore, we reviewed 320 patients diagnosed with breast cancer and detected as BRCA mutation carriers who underwent contralateral RRM; high proportion of them were BRCA1 mutation carriers.Interestingly, we found a low incidence of only seven patients (2.2%) with occult malignancy on contralateral RRM side, which is different from that reported in other nations. Conclusion The incidence of occult malignancy in the contralateral breast of breast cancer patients with breast cancer with BRCA mutation is significantly low, and may be influenced by several factors. Increased utilization of screening and advancements in diagnostic technologies in South Korea have reduced the chance of occult malignancy in RRM, and a variety of pathologic examination methods may affect the rate of incidence.

Purpose: This study aimed to identify predictive factors affecting adolescents’ subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive performance was compared with traditional regression models. Methods: Responses from a total of 44,320 students from grades 7 to 12 were analyzed. Data pre-processing involved handling missing values and selecting variables to construct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance. Results: The random forest model demonstrated a stable predictive performance, with an R2 of .37. Mental and physical health factors were found to significantly affect subjective happiness. Adolescents’ subjective happiness was most strongly influenced by perceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic performance. Conclusion This study highlights the utility of machine learning in identifying factors influencing adolescents’ subjective happiness, addressing limitations of traditional regression approaches. These findings underscore the need for multidimensional interventions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents’ subjective happiness.

Background/Aims: Direct-acting antivirals (DAAs) effectively eradicate hepatitis C virus. This study investigated whether metabolic dysfunction influences the likelihood of fibrosis regression after DAA treatment in patients with chronic hepatitis C (CHC). Methods: This multicenter, retrospective study included 8,819 patients diagnosed with CHC who were treated with DAAs and achieved a sustained virological response (SVR) between January 2014 and December 2022. Fibrosis regression was defined as a 20% reduction in noninvasive surrogates for liver fibrosis, such as liver stiffness (LS) measured by vibration-controlled transient elastography (VCTE) and the fibrosis-4 (FIB-4) score. Hypercholesterolemia (h-TC) was defined as >200 mg/dL. Results: The median age of the study population was 59.6 years, with a predominance of male patients (n=4,713, 57.3%). Genotypes 1, 2, and others were confirmed in 3,872 (46.2%), 3,487 (41.6%), and 1,024 (12.2%) patients, respectively. Diabetes mellitus (DM) was present in 1,442 (17.2%) patients and the median LS was 7.50 kPa (interquartile range, 5.30–12.50). Multivariate analysis revealed that the presence of DM and pre-DAA h-TC were independently associated with a decreased probability of fibrosis regression by VCTE. Additionally, pre-DAA h-TC was independently associated with a decreased probability of fibrosis regression by the FIB-4. Conclusions Metabolic dysfunction has an unfavorable influence on fibrosis regression in patients with CHC who achieve SVR after DAA treatment.

Purpose: Breast cancer gene (BRCA) mutation is a well-known risk factor for breast cancer, and clinical interest in prophylactic mastectomy has increased in recent years.We investigated patients who were BRCA mutation carriers and underwent contralateral risk-reducing mastectomy (RRM), focusing on the incidence of occult malignancy after contralateral RRM. Methods: Prospectively collected data of patients with breast cancer treated at a single institution were retrospectively reviewed. Patients who underwent RRM with BRCA mutation who underwent RRM between January 2010 and November 2023 were included in this study.Among patients who underwent contralateral RRM, those with a primary cancer diagnosis were included, and those with occult malignancy on the contralateral RRM side were reviewed additionally. The demographics and pathologies of both primary breast cancer and occult malignancies were evaluated. Results: In our institution, 925 patients were identified as BRCA mutation carriers, and 320 patients underwent contralateral RRM along with primary breast cancer surgery. BRCA2 mutation occurred more frequently (54.8%) in the overall BRCA mutation cohort. Furthermore, we reviewed 320 patients diagnosed with breast cancer and detected as BRCA mutation carriers who underwent contralateral RRM; high proportion of them were BRCA1 mutation carriers.Interestingly, we found a low incidence of only seven patients (2.2%) with occult malignancy on contralateral RRM side, which is different from that reported in other nations. Conclusion The incidence of occult malignancy in the contralateral breast of breast cancer patients with breast cancer with BRCA mutation is significantly low, and may be influenced by several factors. Increased utilization of screening and advancements in diagnostic technologies in South Korea have reduced the chance of occult malignancy in RRM, and a variety of pathologic examination methods may affect the rate of incidence.

Purpose: This study aimed to identify predictive factors affecting adolescents’ subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive performance was compared with traditional regression models. Methods: Responses from a total of 44,320 students from grades 7 to 12 were analyzed. Data pre-processing involved handling missing values and selecting variables to construct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance. Results: The random forest model demonstrated a stable predictive performance, with an R2 of .37. Mental and physical health factors were found to significantly affect subjective happiness. Adolescents’ subjective happiness was most strongly influenced by perceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic performance. Conclusion This study highlights the utility of machine learning in identifying factors influencing adolescents’ subjective happiness, addressing limitations of traditional regression approaches. These findings underscore the need for multidimensional interventions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents’ subjective happiness.