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.

Gout is the most common inflammatory arthritis in adults, associated with hyperuricemia and the chronic deposition of monosodium urate (MSU) crystals. Hyperuricemia results from increased production of uric acid and decreased excretion by the kidneys and intestines. Urate excretion is regulated by a group of urate transporters, and decreased renal or intestinal excretion is the primary mechanism of hyperuricemia in most people. Genetic variability in these urate transporters is strongly related to variances in serum urate levels. Not all individuals with hyperuricemia show deposition of MSU crystals or develop gout. The initiation of the inflammatory response to MSU crystals is mainly mediated by the nucleotide-binding oligomerization domain-, leucine-rich repeat- and pyrin domain-containing protein 3 (NLRP3) inflammasome. The activated NLRP3 inflammasome complex cleaves pro-interleukin-1β (IL-1β) into its active form, IL-1β, which is a key mediator of the inflammatory response in gout. IL-1β leads to the upregulation of cytokines and chemokines, resulting in the recruitment of neutrophils and other immune cells. Neutrophils recruited to the site of inflammation also play a role in resolving inflammation. Aggregated neutrophil extracellular traps (NETs) trap and degrade cytokines and chemokines through NET-bound proteases, promoting the resolution of inflammation. Advanced gout is characterized by tophi, chronic inflammatory responses, and structural joint damage. Tophi are chronic foreign body granuloma-like structures containing collections of MSU crystals encased by inflammatory cells and connective tissue. Tophi are closely related to chronic inflammation and structural damage.

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.

Gout is the most common inflammatory arthritis in adults, associated with hyperuricemia and the chronic deposition of monosodium urate (MSU) crystals. Hyperuricemia results from increased production of uric acid and decreased excretion by the kidneys and intestines. Urate excretion is regulated by a group of urate transporters, and decreased renal or intestinal excretion is the primary mechanism of hyperuricemia in most people. Genetic variability in these urate transporters is strongly related to variances in serum urate levels. Not all individuals with hyperuricemia show deposition of MSU crystals or develop gout. The initiation of the inflammatory response to MSU crystals is mainly mediated by the nucleotide-binding oligomerization domain-, leucine-rich repeat- and pyrin domain-containing protein 3 (NLRP3) inflammasome. The activated NLRP3 inflammasome complex cleaves pro-interleukin-1β (IL-1β) into its active form, IL-1β, which is a key mediator of the inflammatory response in gout. IL-1β leads to the upregulation of cytokines and chemokines, resulting in the recruitment of neutrophils and other immune cells. Neutrophils recruited to the site of inflammation also play a role in resolving inflammation. Aggregated neutrophil extracellular traps (NETs) trap and degrade cytokines and chemokines through NET-bound proteases, promoting the resolution of inflammation. Advanced gout is characterized by tophi, chronic inflammatory responses, and structural joint damage. Tophi are chronic foreign body granuloma-like structures containing collections of MSU crystals encased by inflammatory cells and connective tissue. Tophi are closely related to chronic inflammation and structural damage.

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.