Background: Malnutrition exacerbates the prognosis of numerous diseases; however, its specific impact on severe coronavirus disease 2019 (COVID-19) outcomes remains insufficiently explored. Methods: This multicenter study in Korea evaluated the nutritional status of 1,088 adults with severe COVID-19 using the Geriatric Nutritional Risk Index (GNRI) based on serum albumin levels and body weight. The patients were categorized into two groups: GNRI >98 (no-risk) and GNRI ≤98 (risk). Propensity score matching, adjusted for demographic and clinical variables, was conducted. Results: Of the 1,088 patients, 642 (59%) were classified as at risk of malnutrition. Propensity score matching revealed significant disparities in hospital (34.3% vs. 19.4%, p<0.001) and intensive care unit (ICU) mortality (31.5% vs. 18.9%, p<0.001) between the groups. The risk group was associated with a higher hospital mortality rate in the multivariate Cox regression analyses following propensity score adjustment (hazard ratio [HR], 1.64; p=0.001). Among the 670 elderly patients, 450 were at risk of malnutrition. Furthermore, the risk group demonstrated significantly higher hospital (52.1% vs. 29.5%, p<0.001) and ICU mortality rates (47.2% vs. 29.1%, p<0.001). The risk group was significantly associated with increased hospital mortality rates in the multivariate analyses following propensity score adjustment (HR, 1.66; p=0.001). Conclusion Malnutrition, as indicated by a low GNRI, was associated with increased mortality in patients with severe COVID-19. This effect was also observed in the elderly population. These findings underscore the critical importance of nutritional assessment and effective interventions for patients with severe COVID-19.

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: Malnutrition exacerbates the prognosis of numerous diseases; however, its specific impact on severe coronavirus disease 2019 (COVID-19) outcomes remains insufficiently explored. Methods: This multicenter study in Korea evaluated the nutritional status of 1,088 adults with severe COVID-19 using the Geriatric Nutritional Risk Index (GNRI) based on serum albumin levels and body weight. The patients were categorized into two groups: GNRI >98 (no-risk) and GNRI ≤98 (risk). Propensity score matching, adjusted for demographic and clinical variables, was conducted. Results: Of the 1,088 patients, 642 (59%) were classified as at risk of malnutrition. Propensity score matching revealed significant disparities in hospital (34.3% vs. 19.4%, p<0.001) and intensive care unit (ICU) mortality (31.5% vs. 18.9%, p<0.001) between the groups. The risk group was associated with a higher hospital mortality rate in the multivariate Cox regression analyses following propensity score adjustment (hazard ratio [HR], 1.64; p=0.001). Among the 670 elderly patients, 450 were at risk of malnutrition. Furthermore, the risk group demonstrated significantly higher hospital (52.1% vs. 29.5%, p<0.001) and ICU mortality rates (47.2% vs. 29.1%, p<0.001). The risk group was significantly associated with increased hospital mortality rates in the multivariate analyses following propensity score adjustment (HR, 1.66; p=0.001). Conclusion Malnutrition, as indicated by a low GNRI, was associated with increased mortality in patients with severe COVID-19. This effect was also observed in the elderly population. These findings underscore the critical importance of nutritional assessment and effective interventions for patients with severe COVID-19.

Background: Malnutrition exacerbates the prognosis of numerous diseases; however, its specific impact on severe coronavirus disease 2019 (COVID-19) outcomes remains insufficiently explored. Methods: This multicenter study in Korea evaluated the nutritional status of 1,088 adults with severe COVID-19 using the Geriatric Nutritional Risk Index (GNRI) based on serum albumin levels and body weight. The patients were categorized into two groups: GNRI >98 (no-risk) and GNRI ≤98 (risk). Propensity score matching, adjusted for demographic and clinical variables, was conducted. Results: Of the 1,088 patients, 642 (59%) were classified as at risk of malnutrition. Propensity score matching revealed significant disparities in hospital (34.3% vs. 19.4%, p<0.001) and intensive care unit (ICU) mortality (31.5% vs. 18.9%, p<0.001) between the groups. The risk group was associated with a higher hospital mortality rate in the multivariate Cox regression analyses following propensity score adjustment (hazard ratio [HR], 1.64; p=0.001). Among the 670 elderly patients, 450 were at risk of malnutrition. Furthermore, the risk group demonstrated significantly higher hospital (52.1% vs. 29.5%, p<0.001) and ICU mortality rates (47.2% vs. 29.1%, p<0.001). The risk group was significantly associated with increased hospital mortality rates in the multivariate analyses following propensity score adjustment (HR, 1.66; p=0.001). Conclusion Malnutrition, as indicated by a low GNRI, was associated with increased mortality in patients with severe COVID-19. This effect was also observed in the elderly population. These findings underscore the critical importance of nutritional assessment and effective interventions for patients with severe COVID-19.

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: Malnutrition exacerbates the prognosis of numerous diseases; however, its specific impact on severe coronavirus disease 2019 (COVID-19) outcomes remains insufficiently explored. Methods: This multicenter study in Korea evaluated the nutritional status of 1,088 adults with severe COVID-19 using the Geriatric Nutritional Risk Index (GNRI) based on serum albumin levels and body weight. The patients were categorized into two groups: GNRI >98 (no-risk) and GNRI ≤98 (risk). Propensity score matching, adjusted for demographic and clinical variables, was conducted. Results: Of the 1,088 patients, 642 (59%) were classified as at risk of malnutrition. Propensity score matching revealed significant disparities in hospital (34.3% vs. 19.4%, p<0.001) and intensive care unit (ICU) mortality (31.5% vs. 18.9%, p<0.001) between the groups. The risk group was associated with a higher hospital mortality rate in the multivariate Cox regression analyses following propensity score adjustment (hazard ratio [HR], 1.64; p=0.001). Among the 670 elderly patients, 450 were at risk of malnutrition. Furthermore, the risk group demonstrated significantly higher hospital (52.1% vs. 29.5%, p<0.001) and ICU mortality rates (47.2% vs. 29.1%, p<0.001). The risk group was significantly associated with increased hospital mortality rates in the multivariate analyses following propensity score adjustment (HR, 1.66; p=0.001). Conclusion Malnutrition, as indicated by a low GNRI, was associated with increased mortality in patients with severe COVID-19. This effect was also observed in the elderly population. These findings underscore the critical importance of nutritional assessment and effective interventions for patients with severe COVID-19.

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: Malnutrition exacerbates the prognosis of numerous diseases; however, its specific impact on severe coronavirus disease 2019 (COVID-19) outcomes remains insufficiently explored. Methods: This multicenter study in Korea evaluated the nutritional status of 1,088 adults with severe COVID-19 using the Geriatric Nutritional Risk Index (GNRI) based on serum albumin levels and body weight. The patients were categorized into two groups: GNRI >98 (no-risk) and GNRI ≤98 (risk). Propensity score matching, adjusted for demographic and clinical variables, was conducted. Results: Of the 1,088 patients, 642 (59%) were classified as at risk of malnutrition. Propensity score matching revealed significant disparities in hospital (34.3% vs. 19.4%, p<0.001) and intensive care unit (ICU) mortality (31.5% vs. 18.9%, p<0.001) between the groups. The risk group was associated with a higher hospital mortality rate in the multivariate Cox regression analyses following propensity score adjustment (hazard ratio [HR], 1.64; p=0.001). Among the 670 elderly patients, 450 were at risk of malnutrition. Furthermore, the risk group demonstrated significantly higher hospital (52.1% vs. 29.5%, p<0.001) and ICU mortality rates (47.2% vs. 29.1%, p<0.001). The risk group was significantly associated with increased hospital mortality rates in the multivariate analyses following propensity score adjustment (HR, 1.66; p=0.001). Conclusion Malnutrition, as indicated by a low GNRI, was associated with increased mortality in patients with severe COVID-19. This effect was also observed in the elderly population. These findings underscore the critical importance of nutritional assessment and effective interventions for patients with severe COVID-19.

Background: The utility of the QuantiFERON-Monitor (QFM, Qiagen), a tool developed to assess general immune function, remains insufficiently explored in critically ill patients with acute respiratory failure (ARF). Therefore, we used the QFM to evaluate the immune function of patients with ARF at intensive care unit (ICU) admission and monitored QFM changes based on disease severity and clinical outcome correlations. Methods: We evaluated the immune function of 99 patients with ARF in an ICU setting.The QFM was evaluated upon ICU admission, day 7 post-ICU admission, and discharge.Their results were compared with those of five healthy controls. Results: The QFM levels at ICU admission were significantly lower in patients with ARF than in healthy controls (median IUs/mL: 5.5 vs. 465.0, respectively). The QFM levels in patients with coronavirus disease 2019 or pneumonia (9.2 and 7.9 IUs/mL, respectively) were higher than those in patients with acute respiratory distress syndrome or septic shock (4.9 and 3.6 IUs/mL, respectively). On day 7, the QFM levels increased to 8.3 IUs/ mL and reached 16.7 IUs/mL at discharge. At ICU admission, patients requiring ventilator support had lower QFM levels than those requiring nasal prong or high-flow nasal cannula support. Those who died in the ICU had significantly lower QFM levels (4.0 IUs/mL) at ICU admission than those who survived (5.8 IUs/mL). Conclusions Reduced QFM levels among patients with severe ARF reflect impaired cellular immune responses and suggest that QFM may serve as a practical tool for early risk stratification and immune monitoring in ICU settings.