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.

Background: Data on the status of long-term follow-up (LTFU) care for childhood cancer survivors (CCSs) in Korea is lacking. This study was conducted to evaluate the current status of LTFU care for CCSs and relevant physicians’ perspectives. Methods: A nationwide online survey of pediatric hematologists/oncologists in the Republic of Korea was undertaken. Results: Overall, 47 of the 74 board-certified Korean pediatric hematologists/oncologists currently providing pediatric hematology/oncology care participated in the survey (response rate = 63.5%). Forty-five of the 47 respondents provided LTFU care for CCSs five years after the completion of primary cancer treatment. However, some of the 45 respondents provided LTFU care only for CCS with late complications or CCSs who requested LTFU care. Twenty of the 45 respondents oversaw LTFU care for adult CCSs, although pediatric hematologists/ oncologists experienced more difficulties managing adult CCSs. Many pediatric hematologists/oncologists did not perform the necessary screening test, although CCSs had risk factors for late complications, mostly because of insurance coverage issues and the lack of Korean LTFU guidelines. Regarding a desirable LTFU care system for CCSs in Korea, 27 of the 46 respondents (58.7%) answered that it is desirable to establish a multidisciplinary CCSs care system in which pediatric hematologists/oncologists and adult physicians cooperate. Conclusion The LTFU care system for CCS is underdeveloped in the Republic of Korea. It is urgent to establish an LTFU care system to meet the growing needs of Korean CCSs, which should include Korean CCSs care guidelines, provider education plans, the establishment of multidisciplinary care systems, and a supportive national healthcare policy.

Background: There is difference in the incidence of multi-system inflammatory syndrome in children (MIS-C) in patients with different variants of severe acute respiratory syndrome coronavirus 2, however, little is known about the epidemiology in Asian countries. We investigated and compared the epidemiology of the MIS-C during omicron-dominant period with that of previous periods in South Korea. Methods: We obtained clinical, epidemiological and laboratory data on MIS-C cases from national MIS-C surveillance in South Korea. We defined pre-delta period as January 2020–May 2021; delta period as June 2021–December 2021; and omicron period as January 2022–April 2022. We describe the clinical characteristics and outcomes of MIS-C patients by period. Results: A total of 91 cases were assessed to be MIS-C cases. Number of MIS-C cases have increased from six cases during pre-delta period to 66 cases during omicron period, while the incidence rate (the number of MIS-C cases per 100,000 cases of reported coronavirus disease 2019) has decreased from 38.5 cases per 100,000 (95% confidence interval [CI], 14.1–83.9) during pre-delta period to 1.6 cases per 100,000 (95% CI, 1.2–2.0) during omicron periods. During pre-delta period, 66.7% and 100% had hypotension and gastrointestinal involvement, respectively; while during omicron period, 12.1% and 6.1% had such clinical manifestations. Fifty percent of pre-delta MIS-C patients were taken intensive care unit (ICU) cares, while 10.6% of patients during omicron periods were in ICUs. Conclusion Omicron period were associated with less severe clinical manifestation compared to pre-delta and delta periods. Although incidence rate of MIS-C was lower for the omicron period than pre-delta and delta periods, number of patients reported with MIS-C may pose a substantial clinical burden.

Background: The advancement of treatment with direct-acting antiviral (DAA) agents has improved the cure rate of hepatitis C virus (HCV) infection close to 100%. The aim of our study was to assess the real-world effectiveness and safety of DAA regimens for the treatment of patients with chronic HCV genotype 2. Methods: We retrospectively analyzed the clinical data of patients treated with sofosbuvir plus ribavirin (SOF + RBV) or glecaprevir/pibrentasvir (G/P) for chronic HCV genotype 2 infection at seven university hospitals in the Korean southeast region. Results: SOF + RBV therapy produced an 89% and 98.3% sustained virologic response 12 week (SVR12) after treatment completion in the full analysis set and per-protocol set, respectively, and the corresponding values for G/P therapy were 89.5% and 99.2%, respectively. The difference between the treatments was probably because 6.2% (59/953) of patients in the SOF + RBV group did not complete the treatment and 9.8% (14/143) in the G/P group did not test HCV RNA after treatment completion. Adverse events (A/Es) were reported in 59.7% (569/953) and 25.9% (37/143) of the SOF + RBV and G/P groups, respectively. In the SOF + RBV group, 12 (1.26%) patients discontinued treatment owing to A/Es, whereas no patients discontinued treatment because of A/Es in the G/P group. Conclusion In both treatment groups, SVR was high when treatment was completed.However, there was a high dropout rate in the SOF + RBV group, and the dropout analysis showed that these were patients with liver cirrhosis (LC; 43/285, 15.1%), especially those with decompensated LC (12/32, 37.5%). Therefore, an early initiation of antiviral therapy is recommended for a successful outcome before liver function declines. Furthermore, patients with decompensated LC who are considered candidates for SOF + RBV treatment should be carefully monitored to ensure that their treatment is completed, especially those with low hemoglobin and high alanine transaminase.

Background: The advancement of treatment with direct-acting antiviral (DAA) agents has improved the cure rate of hepatitis C virus (HCV) infection close to 100%. The aim of our study was to assess the real-world effectiveness and safety of DAA regimens for the treatment of patients with chronic HCV genotype 2. Methods: We retrospectively analyzed the clinical data of patients treated with sofosbuvir plus ribavirin (SOF + RBV) or glecaprevir/pibrentasvir (G/P) for chronic HCV genotype 2 infection at seven university hospitals in the Korean southeast region. Results: SOF + RBV therapy produced an 89% and 98.3% sustained virologic response 12 week (SVR12) after treatment completion in the full analysis set and per-protocol set, respectively, and the corresponding values for G/P therapy were 89.5% and 99.2%, respectively. The difference between the treatments was probably because 6.2% (59/953) of patients in the SOF + RBV group did not complete the treatment and 9.8% (14/143) in the G/P group did not test HCV RNA after treatment completion. Adverse events (A/Es) were reported in 59.7% (569/953) and 25.9% (37/143) of the SOF + RBV and G/P groups, respectively. In the SOF + RBV group, 12 (1.26%) patients discontinued treatment owing to A/Es, whereas no patients discontinued treatment because of A/Es in the G/P group. Conclusion In both treatment groups, SVR was high when treatment was completed.However, there was a high dropout rate in the SOF + RBV group, and the dropout analysis showed that these were patients with liver cirrhosis (LC; 43/285, 15.1%), especially those with decompensated LC (12/32, 37.5%). Therefore, an early initiation of antiviral therapy is recommended for a successful outcome before liver function declines. Furthermore, patients with decompensated LC who are considered candidates for SOF + RBV treatment should be carefully monitored to ensure that their treatment is completed, especially those with low hemoglobin and high alanine transaminase.

Objective: This study compared the prognostic performance of the following five injury severity scores: the Geriatric Trauma Outcome Score (GTOS), the Injury Severity Score (ISS), the New Injury Severity Score (NISS), the Revised Trauma Score (RTS), and the Trauma and Injury Severity Score (TRISS) for in-hospital mortality in severe geriatric trauma patients. Methods: A retrospective, cross-sectional, observational study was conducted using a database of severe geriatric trauma patients (age ≥65 years and ISS ≥16) who presented to a single regional trauma center between November 2016 and October 2018. We compared the baseline characteristics between the survivor and mortality groups and the predictive ability of the five scoring systems. Results: A total of 402 patients were included in the analysis; the in-hospital mortality rate was 25.6% (n=103). The TRISS had the highest area under the curve of 0.953 (95% confidence interval [CI], 0.927-0.971); followed by RTS, 0.777 (95% CI, 0.733-0.817); NISS, 0.733 (95% CI, 0.687-0.776); ISS, 0.660 (95% CI, 0.612-0.707); and GTOS, 0.660 (95% CI, 0.611-0.706) in severe geriatric trauma. The TRISS also had the highest area under the curve of 0.961 (0.919-0.985) among the injury severity scoring systems in polytrauma. The predictive ability of TRISS was significantly higher than the other four scores with respect to overall trauma and polytrauma (P<0.001). Conclusion The TRISS showed the highest prognostic performance for predicting in-hospital mortality among all the injury severity scoring systems in severe geriatric trauma.