Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Purpose: Cybersickness is a type of motion sickness induced by virtual reality (VR) or augmented reality (AR) environments that presents symptoms including nausea, dizziness, and headaches. This study aimed to investigate how cathodal transcranial direct current stimulation (tDCS) alleviates motion sickness symptoms and modulates brain activity in individuals experiencing cybersickness after exposure to a VR environment. Methods: This study was performed on two groups of healthy adults with cybersickness symptoms. Subjects were randomly assigned to receive either cathodal tDCS intervention or sham tDCS intervention. Brain activity during VR stimulation was measured by 38-channel functional near-infrared spectroscopy (fNIRS). tDCS was administered to the right temporoparietal junction (TPJ) for 20 minutes at an intensity of 2mA, and the severity of cybersickness was assessed pre- and post-intervention using a simulator sickness questionnaire (SSQ).Result: Following the experiment, cybersickness symptoms in subjects who received cathodal tDCS intervention were reduced based on SSQ scores, whereas those who received sham tDCS showed no significant change. fNIRS analysis revealed that tDCS significantly diminished cortical activity in subjects with high activity in temporal and parietal lobes, whereas high cortical activity was maintained in these regions after intervention in subjects who received sham tDCS. Conclusion These findings suggest that cathodal tDCS applied to the right TPJ region in young adults experiencing cybersickness effectively reduces motion sickness induced by VR environments.

Purpose: Cybersickness is a type of motion sickness induced by virtual reality (VR) or augmented reality (AR) environments that presents symptoms including nausea, dizziness, and headaches. This study aimed to investigate how cathodal transcranial direct current stimulation (tDCS) alleviates motion sickness symptoms and modulates brain activity in individuals experiencing cybersickness after exposure to a VR environment. Methods: This study was performed on two groups of healthy adults with cybersickness symptoms. Subjects were randomly assigned to receive either cathodal tDCS intervention or sham tDCS intervention. Brain activity during VR stimulation was measured by 38-channel functional near-infrared spectroscopy (fNIRS). tDCS was administered to the right temporoparietal junction (TPJ) for 20 minutes at an intensity of 2mA, and the severity of cybersickness was assessed pre- and post-intervention using a simulator sickness questionnaire (SSQ).Result: Following the experiment, cybersickness symptoms in subjects who received cathodal tDCS intervention were reduced based on SSQ scores, whereas those who received sham tDCS showed no significant change. fNIRS analysis revealed that tDCS significantly diminished cortical activity in subjects with high activity in temporal and parietal lobes, whereas high cortical activity was maintained in these regions after intervention in subjects who received sham tDCS. Conclusion These findings suggest that cathodal tDCS applied to the right TPJ region in young adults experiencing cybersickness effectively reduces motion sickness induced by VR environments.

Purpose: Cybersickness is a type of motion sickness induced by virtual reality (VR) or augmented reality (AR) environments that presents symptoms including nausea, dizziness, and headaches. This study aimed to investigate how cathodal transcranial direct current stimulation (tDCS) alleviates motion sickness symptoms and modulates brain activity in individuals experiencing cybersickness after exposure to a VR environment. Methods: This study was performed on two groups of healthy adults with cybersickness symptoms. Subjects were randomly assigned to receive either cathodal tDCS intervention or sham tDCS intervention. Brain activity during VR stimulation was measured by 38-channel functional near-infrared spectroscopy (fNIRS). tDCS was administered to the right temporoparietal junction (TPJ) for 20 minutes at an intensity of 2mA, and the severity of cybersickness was assessed pre- and post-intervention using a simulator sickness questionnaire (SSQ).Result: Following the experiment, cybersickness symptoms in subjects who received cathodal tDCS intervention were reduced based on SSQ scores, whereas those who received sham tDCS showed no significant change. fNIRS analysis revealed that tDCS significantly diminished cortical activity in subjects with high activity in temporal and parietal lobes, whereas high cortical activity was maintained in these regions after intervention in subjects who received sham tDCS. Conclusion These findings suggest that cathodal tDCS applied to the right TPJ region in young adults experiencing cybersickness effectively reduces motion sickness induced by VR environments.

Purpose: Cybersickness is a type of motion sickness induced by virtual reality (VR) or augmented reality (AR) environments that presents symptoms including nausea, dizziness, and headaches. This study aimed to investigate how cathodal transcranial direct current stimulation (tDCS) alleviates motion sickness symptoms and modulates brain activity in individuals experiencing cybersickness after exposure to a VR environment. Methods: This study was performed on two groups of healthy adults with cybersickness symptoms. Subjects were randomly assigned to receive either cathodal tDCS intervention or sham tDCS intervention. Brain activity during VR stimulation was measured by 38-channel functional near-infrared spectroscopy (fNIRS). tDCS was administered to the right temporoparietal junction (TPJ) for 20 minutes at an intensity of 2mA, and the severity of cybersickness was assessed pre- and post-intervention using a simulator sickness questionnaire (SSQ).Result: Following the experiment, cybersickness symptoms in subjects who received cathodal tDCS intervention were reduced based on SSQ scores, whereas those who received sham tDCS showed no significant change. fNIRS analysis revealed that tDCS significantly diminished cortical activity in subjects with high activity in temporal and parietal lobes, whereas high cortical activity was maintained in these regions after intervention in subjects who received sham tDCS. Conclusion These findings suggest that cathodal tDCS applied to the right TPJ region in young adults experiencing cybersickness effectively reduces motion sickness induced by VR environments.

Background: Limited data are available on the mortality rates of patients receiving extracorporeal membrane oxygenation (ECMO) support for coronavirus disease 2019 (COVID-19). We aimed to analyze the relationship between COVID-19 and clinical outcomes for patients receiving ECMO. Methods: We retrospectively investigated patients with COVID-19 pneumonia requiring ECMO in 19 hospitals across Korea from January 1, 2020 to August 31, 2021. The primary outcome was the 90-day mortality after ECMO initiation. We performed multivariate analysis using a logistic regression model to estimate the odds ratio (OR) of 90-day mortality. Survival differences were analyzed using the Kaplan–Meier (KM) method. Results: Of 127 patients with COVID-19 pneumonia who received ECMO, 70 patients (55.1%) died within 90 days of ECMO initiation. The median age was 64 years, and 63% of patients were male. The incidence of ECMO was increased with age but was decreased after 70 years of age. However, the survival rate was decreased linearly with age. In multivariate analysis, age (OR, 1.048; 95% confidence interval [CI], 1.010–1.089; P = 0.014) and receipt of continuous renal replacement therapy (CRRT) (OR, 3.069; 95% CI, 1.312–7.180; P = 0.010) were significantly associated with an increased risk of 90-day mortality. KM curves showed significant differences in survival between groups according to age (65 years) (log-rank P = 0.021) and receipt of CRRT (log-rank P = 0.004). Conclusion Older age and receipt of CRRT were associated with higher mortality rates among patients with COVID-19 who received ECMO.