Kidney transplant outcomes according to matching of the Kidney Donor Profile Index and Estimated Post-Transplant Survival scores
Article information
Abstract
Background
The efficient allocation of donor kidneys to appropriate candidates is mandated in Korea, with a very long waiting time for deceased donor kidney transplantation (DDKT). This study evaluated the prognostic implications of organ matching using the Korean Kidney Donor Profile Index (K-KDPI) and Korean Estimated Post-Transplant Survival (K-EPTS).
Methods
We analyzed 7,443 DDKT recipients between 2008 and 2022 using national databases: the Korean Network for Organ Sharing and the National Health Insurance Data Sharing Service. Patients were classified into classes 1 (low K-KDPI [<70] to low K-EPTS [<20] score), 2 (low K-KDPI to high K-EPTS [≥20]), 3 (high K-KDPI [≥70] to low K-EPTS), and 4 (high K-KDPI to high K-EPTS). Patient and graft survival rates were compared among the groups.
Results
Compared with class 1, classes 2 and 4 demonstrated a higher risk of graft failure (hazard ratio [HR], 1.94 and 3.04 for classes 2 and 4, respectively). For patient survival, classes 2 (HR, 2.77) and 4 (HR, 4.29) exhibited an increased risk compared with class 1, whereas class 3 (HR, 1.32) did not show significant differences. We developed a predictive model for the survival benefits of DDKT over dialysis based on the K-KDPI and K-EPTS scoring systems.
Conclusion
To enhance efficient utilization, it is desirable to introduce longevity-matching that prioritizes the allocation of donor organs with low K-KDPI to recipients with low K-EPTS. A predictive model of the survival benefits of DDKT over dialysis could guide decisions regarding the acceptance of organ offers.
Introduction
The global demand for kidney transplantation (KT) has long outpaced the available supply [1]. Organ shortages due to the discrepancy between organ supply and need are especially serious in Asian countries, including Korea, because their deceased donation rates are much lower than those in Western countries [2–4]. Therefore, the efficient allocation of deceased donor kidneys is critical in Korea, where the prolonged waiting time for deceased donor kidney transplantation (DDKT) has been aggravated by the increasing incidence of end-stage kidney disease and the growing demand for KT [5,6]. Efficient matching of donor kidneys to suitable recipients is essential for optimizing transplant outcomes and addressing organ shortages [7].
Reliable prognostic indices for donors and recipients are important for the appropriate matching of donor kidneys and recipients [8]. The Korean Kidney Donor Profile Index (K-KDPI) is a prognostic tool developed to enhance the assessment of deceased donor kidney quality and to optimize organ allocation in Korea [9]. The K-KDRI is calculated on the basis of five donor-related factors: age, height, diabetes mellitus (DM) status, serum creatinine level, and hepatitis C virus (HCV) positivity [10]. Recent studies have shown that the K-KDPI outperforms the KDPI from the United States in predicting graft survival in the Korean population, reflecting region-specific donor characteristics [10]. On the other hand, the Korean Estimated Post-Transplant Survival (K-EPTS) score is designed to predict the post-transplant survival of recipients, considering four recipient factors: recipient age, presence of DM, HCV status, and duration of dialysis prior to transplantation. The K-EPTS score was developed on the basis of Korean national data to reflect the characteristics of the Korean population. A recent study found that the K-EPTS score showed good discrimination with a C-statistic of 0.690, indicating its reliability in predicting post-transplant survival [11].
This study aimed to assess the prognostic implications of organ matching using the K-KDPI and K-EPTS scoring systems. Furthermore, we proposed a prediction model for the post-transplant survival of DDKT according to the K-KDPI and K-EPTS scoring systems, and the survival benefit of DDKT over dialysis. This assessment aimed to provide insights into the allocation policy for more efficient use of scarce donor organs and better transplant outcomes.
Methods
Ethics statements
This study was approved by the Institutional Review Board of Severance Hospital (No. 4-2021-1358). Informed consent was waived because of the retrospective nature of the study. This study was conducted in accordance with the Declaration of Helsinki and the Declaration of Istanbul [12].
Study design and population
Retrospective, nationwide cohorts were established using the Korean Network for Organ Sharing (KONOS) and the National Health Insurance Data Sharing Service (NHISS). A total of 53,650 patients were on the waiting list for DDKT between 2008 and 2022. Among the 53,650 patients, 372 aged <19 years and 3,449 with invalid or missing information regarding the serum creatinine level, dialysis duration, or DM status were excluded. The final study cohort included 49,829 adult patients categorized into 7,443 DDKT recipients and 42,386 waitlisted patients who remained on dialysis while waiting for DDKT (Fig. 1).
Study profile of patients on the waiting list between 2008 and 2022.
DDKT, deceased donor kidney transplantation; DM, diabetes mellitus.
Classification of K-KDPI/K-EPTS score matching
This study compared post-kidney transplant graft and patient survival rates according to matching of the K-KDPI and K-EPTS scores. The DDKT recipients were categorized into groups based on their K-KDPI and K-EPTS scores. Cut-off values of the K-KDPI and K-EPTS scores were set to 70 and 20, respectively, because expanded criteria donors (ECDs) were defined by K-KDPI ≥70 and recipients with good prognosis were defined by K-EPTS <20 in previous studies [10,11]. Based on these values, patients were classified into groups: class 1 (low K-KDPI [<70] to low K-EPTS [<20] score), class 2 (low K-KDPI [<70] to high K-EPTS [≥20] score), class 3 (high K-KDPI [≥70] to low K-EPTS [<20] score), and class 4 (high K-KDPI [≥70] to high K-EPTS [≥20] score). Additionally, patients were also classified into groups by median values of K-KDPI and K-EPTS: class 5 (low K-KDPI [<50] to low K-EPTS [<50] score), class 6 (low K-KDPI [<50] to high K-EPTS [≥50] score), class 7 (high K-KDPI [≥50] to low K-EPTS [<50] score), and class 8 (high K-KDPI [≥50] to high K-EPTS [≥50] score).
Outcomes
The primary outcome was overall graft failure, which was defined as the initiation of dialysis, retransplantation, or patient death. Secondary outcomes were death-censored graft failure and all-cause mortality. Deaths were confirmed using the database from the Ministry of the Interior and Safety and the NHISS. Graft failure was ascertained using the NHISS database.
Development of the prediction model
Using data from the Korean DDKT registry from 2008 to 2022, we developed a predictive model for post-KT patient survival based on the K-KDPI and K-EPTS scoring systems. This model was constructed using a random forest algorithm, following methodologies from a previous United States study [13].
For waitlist survival, the “entry date” was defined as the initial registration date in the KONOS database. The follow-up period for waitlist survival was defined as the time from entry date to death, removal from the waitlist, or the end of the study period. K-EPTS scores were calculated on the basis of the study entry date. We predicted waitlist survival using the K-EPTS score with a Weibull model [13], because previous studies have shown that this model has the advantage of directly estimating the survival function of individual candidates [13,14].
The survival benefit of DDKT over dialysis was assessed by measuring the absolute reduction in the mortality risk. Risk differences, expressed as percentage points, were calculated by subtracting the post-KT mortality rate from the waitlist mortality rate.
Statistical analysis
Continuous variables are expressed as mean ± standard deviation, and categorical variables are expressed as proportion (%) or frequency. Overall graft survival rates were analyzed using conventional survival analyses. Patient survival rates were analyzed using time-dependent survival analysis. Multivariate analysis was adjusted for donor-related factors (sex, body mass index [BMI], hypertension, hepatitis B surface antigen [HBsAg], donation after circulatory death, and death due to cerebrovascular accident), recipient-related factors (sex, BMI, hypertension, and history of KT), and transplantation-related factors (positivity for panel reactive antibody [PRA] and number of human leukocyte antigen [HLA] mismatches). Recipient HBsAg was excluded because of multicollinearity with donor HBsAg. For post-KT survival prediction, the K-KDPI and K-EPTS scoring systems were used in conjunction with a random forest model, whereas for waitlist survival analysis, the K-EPTS score was calculated using a Weibull regression model.
All analyses were conducted using SAS (version 7.1; SAS Institute), R (version 4.4.2; R Core Team, 2022, R Foundation for Statistical Computing), and its packages, including “survival,” “survminer,” “ggplot2,” “randomForestSRC,” “splines,” and “dplyr” within RStudio (version 2024.12.0; RStudio Team, PBC).
Results
Baseline characteristics of the patients who received deceased donor kidney transplantation
Table 1 presents the baseline clinical characteristics of the DDKT subgroups (classes 1–4) according to K-KDPI and K-EPTS score matching. Classes 1 and 2 had favorable profiles for donor-related factors, whereas classes 1 and 3 had favorable profiles for recipient-related factors. No significant differences were found in donor sex distribution or donor hemoglobin levels among the different classes. Regarding transplantation-related factors, the mean number of HLA mismatches and the proportion of PRA were significantly different among the groups (Table 1).
Baseline characteristics of DDKT patients according to K-KDPI/K-EPTS score matching
Supplementary Table 1 (available online) presents the baseline clinical characteristics of DDKT recipients stratified into classes 5 to 8 according to alternative K-KDPI/K-EPTS score matching. Overall, the clinical characteristics of classes 5 to 8 seemed to be similar to those of classes 1 to 4.
Comparison of graft survival according to K-KDPI/K-EPTS score matching
Over a median follow-up period of 5.2 years, graft failure occurred in 1,432 patients (19.2%), with an incidence of 3.57 per 100 person-years (Table 2). Graft survival rates were compared among the four groups with class 1 (low K-KDPI to low K-EPTS score) as the reference group by multivariate analysis adjusted for donor-related, recipient-related, transplantation-related factors, and the transplant-center effect. Class 2 (the adjusted hazard ratios [HR], 1.94; 95% confidence interval [CI], 1.48–2.53; p < 0.001) and class 4 (HR, 3.04; 95% CI, 2.35–3.93; p < 0.001) had significantly higher risks for graft failure than class 1, whereas class 3 did not show a significantly high risk (HR, 1.23; 95% CI, 0.85–1.78; p = 0.26) (Table 3, Fig. 2A). These results indicate that higher K-EPTS scores are associated with poorer graft survival outcomes, particularly in class 4, which reflects the highest risk for graft failure risk in the high K-KDPI/high K-EPTS group. However, the absence of a statistically significant difference in graft survival between class 3 and class 1 suggests that graft survival rates were comparable among low K-EPTS groups irrespective of the K-KDPI scores.
Outcome event rates according to K-KDPI/K-EPTS score matching
Comparison of graft and patient survival rates according to K-KDPI/K-EPTS score matching
Comparison of graft and patient survival curves according to K-KDPI/K-EPTS score matching.
(A) Graft survival rate, (B) patient survival rate, and (C) death-censored graft survival rate.
K-EPTS, Korean Estimated Post-Transplant Survival; K-KDPI, Korean Kidney Donor Profile Index.
Comparison of patient and death-censored graft survival according to K-KDPI/K-EPTS score matching
Over a median follow-up period of 5.5 years, 989 patients (13.3%) died, with an incidence of 2.37 per 100 person-years (Table 2). Regarding patient survival, classes 2 (HR, 2.77; 95% CI, 1.93–3.96; p < 0.001) and 4 (HR, 4.29; 95% CI, 3.02–6.10; p < 0.001) showed significantly higher risks of mortality than class 1. In contrast, class 3 did not demonstrate a statistically significant difference in patient survival (HR, 1.32; 95% CI, 0.80–2.19; p = 0.27) (Table 3, Fig. 2B).
Death-censored graft failure occurred in 443 patients (6.0%), with an incidence of 1.10 per 100 person-years (Table 2). Compared to class 1, there were no statistically significant differences in death-censored graft failure for classes 2 and 3. Only class 4 had a significantly higher risk than class 1 (HR, 1.67; 95% CI, 1.13–2.48; p = 0.01) (Table 3, Fig. 2C). The competing risk analysis shows the 1 – cumulative incidence function curves for death-censored graft failure across four patient classes (classes 1–4) (Supplementary Fig. 1, available online).
Comparison of graft and patient survival according to alternative K-KDPI/K-EPTS score matching
The incidence rates of graft failure, death, and death-censored graft failure when K-KDPI and K-EPTS scores were alternatively matched using 50% values are summarized in Supplementary Table 2 (available online). Supplementary Table 3 (available online) compares graft, patient, and death-censored graft survival rates according to alternative K-KDPI/K-EPTS score matching.
Regarding the graft survival rate, class 6 and 8 exhibited 2.256-fold and 3.090-fold higher risks, respectively, than to class 5 (class 6: HR, 2.26; 95% CI, 1.77–2.88; p < 0.001/class 8: HR, 3.09; 95% CI, 2.50–3.81; p < 0.001) (Supplementary Table 3, available online). Class 7 had a 1.322-fold higher risk of graft failure than class 5 (HR, 1.32; 95% CI, 1.05–1.67; p = 0.02) (Supplementary Table 3, available online). A similar trend was observed for patient survival, with classex 6 and 8 showing the higher risk of mortality (class 6: HR, 3.20; 95% CI, 2.37–4.33; p ≤ 0.001/class 8: HR, 4.09; 95% CI, 3.12–5.36; p ≤ 0.001) (Supplementary Table 3, available online). Regarding death-censored survival rates, only class 8 was associated with an increased risk compared with class 5 (HR, 1.70; 95% CI, 1.20–2.41; p = 0.002) (Supplementary Table 3, available online). Taken together, the graft and patient outcomes according to the alternative K-KDPI/K-EPTS score matching showed results similar to those of the original K-KDPI/K-EPTS score matching.
Baseline clinical characteristics and outcomes of deceased donor kidney transplantation and waitlisted patients
Table 4 presents the baseline clinical characteristics of the DDKT and waitlisted patients undergoing dialysis. Regarding recipient-related factors, the mean age of DDKT recipients was significantly lower (51.18 ± 10.71 years) than that of waitlisted patients (57.06 ± 10.86 years, p < 0.001). The mean dialysis duration was longer in DDKT recipients than in waitlist candidates (6.51 ± 3.96 years vs. 5.45 ± 5.37 years, p < 0.001). Notably, the proportion of patients with positive PRA results was significantly higher among waitlisted candidates than among DDKT recipients (41.41% vs. 23.51%, p < 0.001).
Baseline characteristics of the patient between DDKT recipients and waitlist candidates
The mortality rate was significantly lower in DDKT recipients than in waitlist candidates (13.29% vs. 25.23%, p < 0.001). The median follow-up duration was shorter in waitlisted candidates than in DDKT recipients (4.98 years vs. 5.50 years, p < 0.001).
Development of the prediction model for post-kidney transplantation patient survival and waitlist survival
In DDKT patients, the estimated 10-year patient survival rate varied according to the K-KDPI and K-EPTS scores, ranging from 40.36% for patients with a K-KDPI of 100 and a K-EPTS score of 100 to 99.37% for those with a K-KDPI of 1 and a K-EPTS score of 1. The contour plot in Fig. 3A illustrates post-KT patient survival as a function of the K-EPTS score and K-KDPI. The post-KT patient survival prediction model achieved an area under the curve (AUC) score of 0.701 and a cross-validated AUC of 0.688.
Predicted survival and benefit of kidney transplantation based on EPTS and KDPI.
(A) Predicted survival with waitlisted vs. deceased donor kidney transplantation (DDKT) patients at 10 years based on EPTS and KDPI scores. (B) Survival benefit of receiving a kidney transplantation vs. remaining on the waitlist based on EPTS and KDPI scores.
EPTS, Estimated Post-Transplant Survival; KDPI, Kidney Donor Profile Index.
In waitlisted patients, the projected 10-year patient survival rate showed a consistent decline as the K-EPTS score increased, starting at 87.1% for candidates with a K-EPTS score of 1 and decreasing to 36.8% for those with a K-EPTS score of 100 (Fig. 3A). The survival prediction model for waitlisted candidates yielded an AUC of 0.631 and a Harrell’s C-index of 0.603. After cross-validation, the mean AUC (5-fold) and mean Harrell’s C-index (5-fold) were 0.631 and 0.603, respectively.
We developed a predictive model to assess the survival differences between DDKT patients and the patients remaining on the waitlist under dialysis (Fig. 3B). The difference in the 10-year mortality risk varied from 0 percentage points for candidates with a K-EPTS score exceeding 89 to a maximum reduction of 24.53 percentage points for those with a K-EPTS score of 51 and a K-KDPI of 1. The survival benefit was more pronounced in candidates with K-EPTS scores between 30 and 70, particularly when receiving kidneys with a K-KDPI <71. Even in candidates with a kidney that had the highest K-KDPI of 100, the 10-year mortality was reduced by 12.94% for those with a K-EPTS score of 60 and by 7.62% for those with a K-EPTS score of 70.
Development of the survival benefit estimator
On the basis of a predictive model to assess survival differences between DDKT recipients and those remaining on the waitlist under dialysis, we developed an online patient survival benefit estimator (https://kidneysurvival.github.io/kesbe/). When users enter the candidate’s K-EPTS score and the K-KDPI score of the offered kidney on the website, the survival benefit estimator generates predicted 10-year survival outcomes under two scenarios: 1) if the candidate remains on the waitlist under dialysis and 2) if the candidate undergoes DDKT with the offered kidney. Additionally, the tool calculates the risk difference in percentages, providing an objective comparison between the two options. This online estimator generates a graphical output, as shown in Supplementary Fig. 2 (available online). Among candidates with a K-EPTS score of 20, receiving a kidney with a K-KDPI of 20 reduced the 10-year mortality by 14.83%, whereas a K-KDPI of 80 led to a reduction of 11.11%. For candidates with a K-EPTS score of 80, the reductions in 10-year mortality were 6.99% with a KDPI of 20 and 3.27% with a K-KDPI of 80 (Supplementary Table 4, available online).
Discussion
This nationwide study demonstrated that graft and patient survival outcomes were significantly influenced by K-KDPI and K-EPTS score matching. The low K-KDPI to low K-EPTS and high K-KDPI to high K-EPTS groups exhibited the lowest and highest risks of graft failure and mortality, respectively. However, the high K-KDPI to low K-EPTS group did not show a statistically significant difference in patient survival compared to the low K-KDPI to low K-EPTS group. We developed a prediction model for post-KT patient survival and waitlist survival according to the K-KDPI and K-EPTS scores. Furthermore, we developed an online survival benefit estimator to quantify the potential survival benefits of DDKT over remaining on the waitlist under dialysis for each patient with specific K-EPTS and K-KDPI scores, therefore providing a more tailored approach to organ allocation and decision-making in clinical practice.
Efficient matching of scarce donor kidneys with suitable recipients is important for optimizing transplant outcomes and resolving the problem of organ shortage, especially in Korea, which has a very long waiting time for DDKT [15]. Longevity-matching, especially young-to-young matching that prioritizes the allocation of organs from young-aged donors to young recipients, increases overall graft life years and cost savings [16–18]. Therefore, this young-to-young allocation policy has been used in the United Kingdom, Canada, France, Scandia transplant, Israel, and Saudi Arabia [16,19,20]. Moreover, the United States adopted longevity-matching that prioritizes the allocation of donor organs with a low KDPI score (<20%) to recipients with a low K-EPTS score (≤20%) [16]. Since the low K-KDPI to low K-EPTS group (class 1) exhibited the best outcomes for grafts and patients in this study, it is advisable to implement a similar longevity-matching strategy in Korea that prioritizes the allocation of donor organs with a low K-KDPI score to recipients with a low K-EPTS score.
Interestingly, the differences in the graft or patient survival rates between class 3 (high K-KDPI to low K-EPTS) and class 1 were not significant. This finding indicates that a higher K-KDPI alone does not necessarily translate into poorer patient survival when matched with a low K-EPTS recipient. While a higher K-KDPI negatively affects graft survival, its effect on patient survival may be mitigated when transplanted into recipients with a low K-EPTS score. The Eurotransplant Senior program is an old-to-old allocation policy that prioritizes the allocation of organs from older donors to older recipients to reduce waiting times while achieving comparable short-term outcomes [21,22]. However, the survival benefits of old-to-old allocations remain controversial. Some European and the United States studies supported this policy, whereas others did not demonstrate any survival benefits [23–27]. The old-to-old policy has been adopted in Eurotransplant, the United Kingdom, and Saudi Arabia, but not commonly as a young-to-young policy [19]. Given the lack of differences in graft and patient survival between the high K-KDPI to low K-EPTS group and the low K-KDPI to low K-EPTS group, along with the critical organ shortage in Korea, a more effective allocation strategy would involve offering kidneys with a high K-KDPI score to recipients with a low K-EPTS score without strict restrictions, rather than limiting their use primarily to candidates with a high K-EPTS score.
When we additionally analyzed impact of interaction between K-KDPI and K-EPTS on primary and secondary outcomes in the entire cohort, the interaction between two indices was not statistically significant for graft survival (HR, 1.00; 95% CI, 1.00–1.00; p = 0.11), patient survival (HR, 1.00; 95% CI, 0.99–1.00; p = 0.75), or death-censored graft survival (HR, 1.00; 95% CI, 0.99–1.00; p = 0.27), suggesting that the combined effect of these two indices does not exert a synergistic or multiplicative influence on graft survival, patient survival, or death-censored graft failure.
A United States group developed a prediction model for post-KT patient survival and waitlist survival and a survival benefit estimator based on the prediction model to calculate survival benefits of DDKT over remaining on the waitlist under dialysis using the Scientific Registry of Transplant Recipients data [13]. Another group applied this United States-derived estimator to a Colombian cohort and found that the estimator-derived survival rates overestimated the actual survival rates in Colombia, suggesting that a survival benefit estimator should be developed from its own dataset for each country [28]. This study developed an online tool that enables users to explore the impact of the K-KDPI on the survival benefit of DDKT over remaining on the waitlist under dialysis for a specific candidate with a given K-EPTS score based on a prediction model based on Korean data. A key consideration in this process is whether accepting the offered kidney would lead to better survival outcomes for the candidate than waiting for a kidney with a lower K-KDPI score. This tool was designed to support transplant providers and DDKT candidates when evaluating ECD kidney offers. If transplantation with the offered kidney is predicted to yield a survival benefit, that is, a higher survival probability than remaining on the waitlist, this information may assist healthcare providers in counseling patients regarding the potential benefits of accepting the organ or help candidates decide to accept the organ offer [29].
Our findings suggest that rejecting an offering of kidney with a high K-KDPI score is generally not advantageous in Korea. As demonstrated in Fig. 3B, K-KDPI had a minimal influence on survival benefit among candidates with a low to medium range of K-EPTS scores (e.g., less than 60) when the K-KDPI score was less than 80. For instance, a candidate with a K-EPTS score of 60 is projected to have a 17.29% reduction in 10-year mortality with a K-KDPI 80 kidney and a 19.83% reduction with a K-KDPI 50 kidney. The small difference in survival benefits between these two options suggests that accepting a KDPI 80 kidney may be preferable to remaining on dialysis while waiting for kidney with a lower KDPI score. Even for kidneys with a K-KDPI of 100, candidates with a K-EPTS score of 60 had a survival benefit of 12.94%. In this sense, the allocation of high K-KDPI kidneys to low K-EPTS recipients without strict restrictions is appropriate in Korea, with a very long waiting time.
This study has several limitations. First, its retrospective design inherently carries the risk of selection and information biases. Although adjustments were made using a multivariate analysis, unmeasured or unknown factors may have still influenced the results. Second, this nationwide study, based on national registry data, lacks detailed information on potential confounding factors, such as immunosuppressive regimens and transplantation center-specific practices. However, when the main analyses were adjusted for transplantation center, the variance of the random effect for the transplant center was less than 0.1, suggesting that center effects on the outcomes were negligible. Third, this study excluded pediatric cases because the K-KDPI and K-EPTS scores were calculated from data without pediatric cases. Future prospective studies with more comprehensive datasets and subgroups are required to confirm our findings.
Nevertheless, this nationwide study can contribute to the Korean transplantation community by providing evidence and future perspectives for policy making. Furthermore, this study conducted in Korea, with lower deceased donation rates and longer waiting times, could contribute to a balanced view and generalization of longevity-matching that previous studies in Western countries have reported.
In conclusion, a longevity-matching strategy that prioritizes the allocation of low K-KDPI donor kidneys to low K-EPTS recipients would be advisable to optimize graft survival and the efficient use of scarce resources. Furthermore, the broader allocation of high K-KDPI kidneys to low and high K-EPTS recipients may enhance organ use while ensuring acceptable post-transplant outcomes. The new online survival benefit estimator, which estimates the 10-year survival probabilities of KT and dialysis for each candidate based on the K-KDPI and K-EPTS scoring systems, could help clinicians and patients assess the survival benefits of DDKT over remaining on the waitlist and make informed decisions to accept kidney offers.
Supplementary Materials
Supplementary data are available at Kidney Research and Clinical Practice online (https://doi.org/10.23876/j.krcp.25.083).
Notes
Conflicts of interest
All authors have no conflicts of interest to declare.
Funding
This study was supported by a grant from the National Institute of Organ, Tissue and Blood Management (20232400C1B-00), which was not involved in the design or analysis of the study.
Acknowledgments
We thank the Korean Network for Organ Sharing (KONOS) and the National Health Insurance Data Sharing Service (NHISS) for sharing their database.
Data sharing statement
The data presented in this study are available from the corresponding author upon reasonable request.
Authors’ contributions
Conceptualization: ON, JY
Data curation, Investigation, Methodology: ON, TYK
Formal analysis: All authors
Funding acquisition, Project administration, Supervision: JY
Visualization: ON
Writing–original draft: ON, JY
Writing–review & editing: JY
All authors read and approved the final manuscript.