Abstract
Objectives
This study evaluates changes in centre-level utilization of longer distance donors (LDD) in heart transplantation (HT) before and after the allocation policy change in 2018.
Methods
Adult HT recipients from 2010 to 2023 were identified from the United Network for Organ Sharing registry. Patients were categorized based on donor centre distance and policy change. The Mann-Kendall trend test was utilized for trend analysis. A propensity-matched analysis was performed. Survival analyses were performed using Kaplan-Meier, restricted mean survival time, and multivariable Cox proportional models. Interaction analysis with Bonferroni correction and sensitivity analysis to test the robustness of primary findings were performed.
Results
Among 32 036 recipients from 152 centres, 29 410 from ≤500 miles and 2626 from >500 miles. The mean distance increased from 171 miles to 288 (P < .001) and mean cold ischaemia time from 3.20 to 3.60 h (P < .001) after allocation change. The proportion of recipients with LDD increased from 5.50% in 2010 to 14.00% in 2022, P = .021. In the unmatched cohort, unadjusted 30-day, 1-year, and 5-year survival was comparable between LDD and non-LDD recipients (P > .05). However, risk-adjusted survival in the matched cohort was significantly better with LDD: 30-day (0.60, 0.43-0.82, P = .002), 1-year (0.67, 0.55-0.82, P < .001), and 5-y (0.75, 0.65-0.86, P < .001). Similar findings persisted even after restricted mean survival time analysis. There was a weak correlation between distance and ischaemia time in the matched cohort (r = 0.19).
Conclusions
There has been a substantial increase in the use of LDD following the allocation change. Distance is not a surrogate for ischaemia time. Survival after HT with LDD use is significantly better compared to non-LDD, but further research is warranted.
Keywords: heart transplantation, longer distance donor, allocation change
In the United States, the number of adult heart transplants (HT) surpassed 4000 for the first time in history in 2022.
Graphical abstract
INTRODUCTION
In the United States, the number of adult heart transplants (HT) surpassed 4000 for the first time in history in 2022. Despite this, there remain over 3000 patients on the waitlist for HT, underscoring the continuing mismatch in supply and demand of donor organs. Waitlist mortality has decreased from 15% in 2011 to 8.7% in 2019, but this has remained largely unchanged since then.1
Recently, several substantial changes have happened in the field of HT. First, the 2018 heart allocation policy change which included changing from a 3-tier system to a 6-tier system in adult HT in the United States. It categorizes candidates by predicted waitlist mortality favouring high-priority recipients to decrease waiting times and expand donor heart sharing. Second, there has been increasing use of ex vivo perfusion devices, use of cold storage devices, and utilization of donation after circulatory death (DCD) donor hearts leading to a growing number of longer distance donors (LDD).2–6 Enthusiasm to utilize LDD to meet the growing demand of HT started in 1979 when a team from Stanford published their comparable outcomes to local donors.7,8 Even though HT using LDD has not been widely utilized, it remains a major step to increase the potential donors to minimize the waitlist mortality. The main limitation in using LDD has been increased cold ischaemia time.9 However, prior study has shown the paradoxical correlation between donor distance and survival after HT with 7% reduction in the hazard for 30-day mortality and 6% reduction in the hazard for 1-year mortality in their risk-adjusted analysis for every 100-mile increase in donor distance from the recipient hospital.10 The aim of this study was to evaluate the use of LDD in HT in the United States with particular attention to trends and impact on post-transplant outcomes compared to non-LDD.
MATERIALS AND METHODS
Ethical statement
The Institutional Review Board at the Medical University of South Carolina exempted the study from the review process because of the publicly accessible, deidentified nature of the UNOS database of deceased subjects. The patient consent was waived due to the same reason.
Data source
The United Network for Organ Sharing (UNOS) database contains prospectively collected data on all solid organ transplantation in the United States. Request to access the data set was sent to UNOS from a qualified researcher trained in human subject confidentiality protocols.
Study population and design
The UNOS database was used to identify all adult (≥18 years old) isolated HT recipients between January 2010 and March 2023 from 152 transplant centres. Patients were categorized based on distance from the donor hospital to the transplanting hospital: ≤500 miles (non-LDD) and >500 miles (LDD) and by allocation change on October 18, 2018, pre- vs post. After the 2018 allocation policy change, status 1 and 2 allocated up to 500 and status 3-6 up to 250 miles. Due to the hesitation to go further than 500 miles, we looked at UNOS data registry keeping 500 miles as a cut-off distance to analyse the difference in outcomes. The primary outcome of the study was all-cause mortality post-transplantation, whereas the secondary outcome included: acute renal failure requiring dialysis, stroke, pacemaker implantation, acute rejection, length of hospital stay. Of note, in the United States, there are 50 states and 11 UNOS regions.
Statistical analysis
The Mann-Kendall trend test was utilized for transplant trend using LDD. Candidate variables were identified based on prior evidence from related studies and clinical reasoning. Baseline characteristics were summarized using median (interquartile range) for continuous variables and frequencies with percentages for categorical variables. Kruskal-Wallis tests were used to compare non-normally distributed continuous variables across groups, while chi-square or Fisher’s exact tests were applied to categorical variables. Normality of each continuous variable was tested with quantile-quantile (Q-Q) plots. Kaplan-Meier method with log-rank test was used for survival analysis. Propensity score matching was employed with recipient, donor and operative variables (see Figure S2) between non-LDD and LDD. Patients were matched in a 1:1 ratio using nearest-neighbour matching and a caliper width of 0.1 of the SD of the logit of the propensity score.
Multivariable Cox-PH and sensitivity analyses were also used. The proportional hazards assumption was evaluated for each Cox model using Schoenfeld residuals. Scatter plot was created, and Pearson correlation coefficient was calculated to check the relationship between distance and ischaemia time on mortality. Restricted mean survival time (RMST) was calculated to complement Kaplan-Meier survival analysis. R-statistic was used to analyse the correlation between distance and ischaemia time. Variables with missing values were assessed and were <2% of the total recipients. A P-value of <0.05 was considered statistically significant. All statistical analyses were conducted using R version 4.4.3.
RESULTS
A total of 32 036 adult patients from 152 transplant centres underwent isolated HT during the study period. There were 29 410 (91.8%) donors from ≤500 miles away from the recipient hospital, whereas 2626 (8.2%) were from >500 miles away. Centres that had at least 1 donor >500 miles away in any year within the study period were 121 (80.1%). The mean distance travelled was 211.50 miles which increased from 171 miles to 288 pre- vs post policy change (P < .001). The mean cold ischaemia time also increased from 3.20 to 3.60 hours following the allocation change (P < .001).
Baseline demographic characteristics of the study population
Baseline characteristics for unmatched HT recipients and donors stratified by donor distance are summarized in Table 1. Recipients of LDD hearts were more frequently female, after policy change, performed at higher volume centres, no use of bridging methods or less use of durable ventricular assist device (VAD), shorter days on waitlist but longer ischaemia time to non-LDD (standardized mean differences [SMD] > 0.2), otherwise most of the demographic and clinical variables are well balanced between the groups. Baseline characteristics of the matched cohort are summarized in Table S1 (SMD < 0.1)
Table 1.
Demographic Characteristics of Unmatched Cohort Stratified by Donor Centre Distance
| N | ≤500 N = 29 410a | >500 N = 2626a | SMD | |
|---|---|---|---|---|
| Centre | ||||
| Centre volume, median (IQR) | 32 036 | 28 (18-44) | 41 (23-59) | −0.43 |
| Recipient | ||||
| Era, n (%) | 32 036 | |||
| Before October 18, 2018 | 19 652 (67%) | 1082 (41%) | 0.53 | |
| After October 18, 2018 | 9758 (33%) | 1544 (59%) | −0.53 | |
| Age (years), median (IQR) | 32 036 | 56 (46, 63) | 57 (47, 64) | 0.05 |
| Gender, n (%) | 32 036 | |||
| Female | 7799 (27%) | 861 (33%) | −0.14 | |
| Male | 21 611 (73%) | 1765 (67%) | 0.14 | |
| Race/ethnicity, n (%) | 32 036 | |||
| White | 18 897 (64%) | 1667 (63%) | 0.02 | |
| Black | 6546 (22%) | 532 (20%) | 0.05 | |
| Hispanic | 2630 (8.9%) | 262 (10.0%) | −0.04 | |
| Other | 1337 (4.5%) | 165 (6.3%) | −0.08 | |
| BMI (kg/m2), median (IQR) | 32 024 | 27.3 (24.0-31.1) | 26.8 (23.5-30.7) | 0.09 |
| Creatinine (mg/dL), median (IQR) | 32 031 | 1.15 (0.91-1.41) | 1.11 (0.90-1.40) | 0.06 |
| Dialysis prior to transplant, n (%) | 32 007 | 611 (2.1%) | 41 (1.6%) | 0.04 |
| Total bilirubin (mg/dL), median (IQR) | 31 968 | 0.70 (0.50-1.10) | 0.70 (0.45-1.10) | 0.01 |
| Diabetes, n (%) | 32 036 | 8118 (28%) | 758 (29%) | 0.03 |
| Causes of heart failure, n (%) | 32 036 | |||
| Nonischaemic dilated cardiomyopathy | 17 081 (58%) | 1524 (58%) | 0.01 | |
| Ischaemic cardiomyopathy | 8882 (30%) | 768 (29%) | 0.02 | |
| Congenital heart disease | 951 (3.2%) | 81 (3.1%) | 0.01 | |
| Valvular heart disease | 272 (0.9%) | 29 (1.1%) | −0.02 | |
| Hypertrophic cardiomyopathy | 802 (2.7%) | 75 (2.9%) | −0.01 | |
| Restrictive cardiomyopathy | 494 (1.7%) | 58 (2.2%) | −0.04 | |
| Failed primary heart transplant | 641 (2.2%) | 60 (2.3%) | −0.01 | |
| Other cause | 287 (1.0%) | 31 (1.2%) | −0.02 | |
| ICU at time of transplant, n (%) | 32 036 | 11 212 (38%) | 1120 (43%) | 0.09 |
| Mechanical ventilation, n (%) | 32 036 | 478 (1.6%) | 46 (1.8%) | 0.01 |
| Inotropes, n (%) | 32 036 | 10 920 (37%) | 1035 (39%) | 0.05 |
| Bridging method, n (%) | 32 036 | |||
| None | 12 132 (41%) | 1254 (48%) | −0.13 | |
| IABP | 4269 (15%) | 517 (20%) | −0.14 | |
| Temp VAD | 522 (1.8%) | 74 (2.8%) | −0.07 | |
| Durable VAD | 12 019 (41%) | 741 (28%) | 0.27 | |
| ECMO | 468 (1.6%) | 40 (1.5%) | 0.01 | |
| Karnofsky index, n (%) | 26 645 | |||
| Symptomatic (≥80%) | 2502 (10%) | 217 (10.0%) | 0.01 | |
| Requires assistance (50%-70%) | 5783 (24%) | 538 (25%) | −0.02 | |
| Disabled in bed >50% time (<40%) | 16 181 (66%) | 1424 (65%) | 0.02 | |
| Cardiac index, median (IQR) | 30 751 | 2.20 (1.82-2.65) | 2.16 (1.78-2.60) | 0.06 |
| PAP (mm Hg), median (IQR) | 30 944 | 26 (19-34) | 26 (19-34) | 0.01 |
| Panel reactive antibodies, median (IQR) | 17 035 | 0 (0-7) | 0 (0-3) | 0.02 |
| Days on waitlist, median (IQR) | 32 036 | 74 (19-258) | 32 (9-125) | 0.24 |
| Heart ischaemia time (h), median (IQR) | 31 892 | 3.19 (2.50-3.77) | 4.55 (4.07-5.28) | −1.36 |
| Donors | ||||
| Age, median (IQR) | 32 036 | 31 (23-40) | 32 (24-41) | −0.09 |
| Gender, n (%) | 32 036 | |||
| Female | 8433 (29%) | 1014 (39%) | −0.21 | |
| Male | 20 977 (71%) | 1612 (61%) | 0.21 | |
| Race, n (%) | 32 036 | |||
| White | 18 721 (64%) | 1652 (63%) | 0.02 | |
| Black | 4916 (17%) | 356 (14%) | 0.09 | |
| Hispanic | 4948 (17%) | 509 (19%) | −0.07 | |
| Other | 825 (2.8%) | 109 (4.2%) | −0.07 | |
| BMI (kg/m²) | 32 025 | 26.6 (23.4-30.8) | 26.1 (22.8-30.8) | 0.02 |
| Mechanism of death, n (%) | 32 028 | |||
| Trauma | 13 389 (46%) | 1030 (39%) | 0.13 | |
| Cerebrovascular | 5358 (18%) | 478 (18%) | 0.00 | |
| Drug overdose | 5080 (17%) | 527 (20%) | −0.07 | |
| Other | 5576 (19%) | 590 (22%) | −0.09 | |
| Diabetes, n (%) | 31 783 | 1101 (3.8%) | 115 (4.4%) | 0.03 |
| Recipient-donor matching | ||||
| Donor/recipient predicted heart mass ratio, median (IQR) | 32 026 | 1.01 (0.92-1.13) | 1.00 (0.90-1.13) | 0.06 |
| Sex-matched, n (%) | 32 036 | 22 728 (77%) | 1967 (75%) | 0.06 |
| Race-matched, n (%) | 32 036 | 14 767 (50%) | 1235 (47%) | 0.06 |
| HLA-matched, n (%) | 29 222 | 3861 (14%) | 329 (14%) | 0.03 |
| ABO-identical, n (%) | 32 036 | 25 138 (85%) | 2329 (89%) | 0.10 |
| CMV-matched, n (%) | 31 879 | 12 626 (43%) | 1048 (40%) | 0.06 |
Abbreviations: ABO, blood type; BMI, body mass index; CMV, cytomegalovirus; ECMO, extracorporeal membrane oxygenation; HLA, human leukocyte antigen; IABP, intraaortic balloon pump; PAP, pulmonary artery pressure; SMD, standardized mean differences; VAD, ventricular assist device.
Median (Q1, Q3); n (%).
Trend and regional variation of LDD utilization
During the study period, HT using LDD increased from 5.46% in 2010 to 14.03% in 2022 (P = .016) (Figure 1). The UNOS regional variation of LDD utilization pre- and post-allocation policy change increased by 12.82% in region 1 but decreased by 5.79% in region 3, whereas the change was negligible in region 8 (0.06%). The overall average change in utilization of LDD at regional level was 4.58% (see Table S2).
Figure 1.
The Trend of Heart Transplantation Using Longer Distance Donor in Percentage
LDD utilization in the state-level
The state-level changes in LDD utilization are shown in Figure 2. Positive change is seen in several Northeast states such as Maine, New Hampshire, and Vermont, whereas negative change is seen in several Southeast states such as Florida, Georgia, Alabama, and Mississippi (P = .079).
Figure 2.
Heat Map of Heart Transplantation Volume Change by Longer Distance Donors by State after Allocation Policy Change. Map of the United States showing the change in number of heart transplantation with longer distance donors post policy change (dark red—positive change, light white—negative change)
Centre-level LDD utilization
However, the centre-level utilization of LDD comprising with >10% of total transplants 18 (12.9%) pre- vs 50 (35.5%) post policy change (P < .001) whereas, LDD comprising with >25% of total transplants 4 (2.9%) pre- vs 12 (8.5%) post-allocation policy change (P = .04). (see Table S3). The average centre-level change in LDD following the policy change was 4.82% of their overall HTs, with 6.2% of centres having a > 5% decrease in proportion of LDD, 49.20% having between a 5% increase or decrease in proportion of LDD, and 44.60% having a > 5% increase in proportion of LDD following the allocation change (see Figure S1A and B).
Survival analysis
Unadjusted survival among unmatched non-LDD and LDD groups for 30-day (98.7% vs 98.8%, P = .05), 1-year (91.4% vs 92.4%, P = .06), and 5-year (79.6% vs 79.0%, P = .76) (see Figure S3). RMST analysis in unmatched cohort, 30-day RMST is 0.32 day higher in LDD vs non-LDD (95% CI, 0.16-0.48, P < .001); 1-year RMST is 10.6 days higher in LDD (95% CI, 7.23-13.99, P < .001); and 5-year RMST is 81.4 days higher in LDD (95% CI, 56.3-106.4, P < .001) (see Table S4).
Interaction of distance and ischaemia time on mortality
Scatter plot showing the Pearson correlation coefficient between the ischaemia time and distance in the unmatched and matched cohorts is shown in Figure 3. Upon directly examining the association between distance and ischaemia time using r-statistic, we found that distance and ischaemia time are strongly correlated in the unmatched cohort (r = 0.63, r2 = 0.4, β = 0.003), but there is only a weak correlation after matching the cohorts (r = 0.19, r2 = 0.04, β = 0.001).
Figure 3.
Scatter Plot Showing the Pearson Correlation Coefficient Between the Ischemia Time and Distance in Unmatched and Matched Cohort
Matched cohort
Unadjusted Kaplan-Meier survival analysis at 30-day, 1-year, and 5-year non-LDD versus LDD was as follows: 95.8% vs 97.4%, P = .002; 89.6% vs 92.6%, P < .001; and 75.5% vs 79.7%, P < .001, respectively (Figure S3). RMST analysis in a matched cohort showed that the 30-day RMST was 0.36 days higher in LDD (95% CI, 0.13-0.59, P = .002); the 1-year RMST was 10.4 days higher in LDD (95% CI, 5.73-15.09, P < .001); and the 5-year RMST was 56.9 days higher in LDD (95% CI, 22.8-90.9, P = .001) (see Table S4).
Risk-adjusted survival analysis with multivariable Cox proportional hazards model (Table 2) revealed the hazard of mortality with LDD at 30-day (HR 0.60, 95% CI 0.43-0.82, P = .002), 1-year (HR 0.67, 95% CI 0.55-0.82, P < .001), and 5-year (HR 0.75, 95% CI 0.65-0.86, P < .001). Additionally, adjusted survival with the ABC test between non-LDD and LDD was 30-day 97.9% vs 98.7%, P = .008; 1-year 89.5% vs 92.7%, P < .001 and 5-year 75.2% vs 79.8%, P < .001 (Figure 4).
Table 2.
Multivariable Cox Proportional Analysis in Matched Cohort: Factors Affecting the Mortality
| 30 days HR (95% CI; P-value) | 1 year HR (95% CI); P-value | 5 years HR (95% CI); P-value | |
|---|---|---|---|
| Donor distance | |||
| ≤500 | Reference | Reference | Reference |
| >500 | 0.60 (0.43-0.82; P = .002) | 0.67 (0.55-0.82; P < .001) | 0.75 (0.65-0.86; P < .001) |
| Centre | |||
| Centre volume, median (IQR) | 1.00 (0.99-1.00; P = .155) | 1.00 (0.99-1.00; P = .544) | 1.00 (1.00-1.00; 0.926) |
| Recipient | |||
| Age (years), median (IQR) | 1.02 (1.01-1.03; P = .022) | 1.01 (1.00-1.02; P = .015) | 1.01 (1.00-1.01; P = .058) |
| Gender, n (%) | |||
| Female | Reference | Reference | Reference |
| Male | 0.65 (0.46-0.95; P = .022) | 0.87 (0.69-1.10; P = .255) | 0.93 (0.78-1.1; P = .378) |
| BMI (kg/m2), median (IQR) | 1.02 (0.98-1.06; P = .363) | 1.02 (1.00-1.05; P = .047) | 1.03 (1.01-1.04; P = .003) |
| Creatinine (mg/dL), median (IQR) | 1.37 (1.10-1.69; P = .004) | 1.24 (1.05-1.46; P = .012) | 1.26 (1.11-1.43; P < .001) |
| Dialysis prior to transplant, n (%) | 2.51 (1.13-5.6; P = .024) | 1.83 (1.03-3.24; P = .038) | 1.55 (1.00-2.42; 0.052) |
| Inotropes | 0.69(0.49-0.98; P = .039) | 0.89(0.72-1.10; P = .291) | 1.00 (0.86-1.17; P = .979) |
| ICU at time of transplant, n (%) | 0.86 (0.62-1.20; P = .382) | 1.08 (0.87-1.33; P = .488) | 1.05 (0.89-1.23; 0.568) |
| Days on waitlist, median (IQR) | 1.00 (1.00-1.00; P = .172) | 1.00 (1.00-1.00; P = .220) | 1.00 (1.00-1.00; P = .281) |
| Heart ischaemia time (h), median (IQR) | 1.20 (1.09-1.32; P < .001) | 1.12 (1.05-1.19; P = .001) | 1.13 (1.07-1.19; P < .001) |
| Donors | |||
| Age, median (IQR) | 1.01 (1.00-1.03; P = .103) | 1.01 (1.00-1.02; P = .027) | 1.01 (1-1.02; P < .001) |
| Recipient-donor matching | |||
| Donor/recipient predicted heart mass ratio, median (IQR) | 0.25 (0.09-0.69; P = .008) | 0.67 (0.34-1.29; P = .228) | 0.84 (0.53-1.32; P = .443) |
Figure 4.
Adjusted Survival Analysis of Heart Transplant Recipients Non-LDD vs LDD: Matched Cohort. Abbreviation: LDD, Long-Distance Donor
Cox proportional hazard modelling distance as a continuous variable scaled to 100 miles revealed that at 30-day HR 0.89, 95% CI 0.84-0.95, P < .001; at 1-year HR 0.93, 95% CI 0.90-0.96, P < .001; and at 5-year HR 0.94, 95% CI 0.92-0.96, P < .001 (see Table S5).
Interaction analysis with Bonferroni correction for policy change-distance and ischaemia time-distance to mortality revealed improved survival with LDD use as well.
Secondary outcomes
Post-transplant complications in the matched cohort are summarized in Table 3. It showed acute renal failure requiring dialysis (P < .001) and the rate of stroke (P = .007) were significantly higher in the non-LDD group. Secondary outcomes of the unmatched cohort are summarized in Table S6.
Table 3.
Post-Transplant Complications: Secondary Outcomes in Matched Cohort
| Outcomes | N | ≤500 N = 2367a | >500 N = 2367a | P-value b |
|---|---|---|---|---|
| Acute renal failure dialysis, n (%) | 4724 | 378 (14.3%) | 276 (11.7%) | <.001 |
| Stroke, n (%) | 4698 | 103 (4.4%) | 66 (2.8%) | .007 |
| Pacemaker, n (%) | 4700 | 56 (2.4%) | 57 (2.4%) | .9 |
| Acute rejection, n (%) | 4734 | 452 (19.2%) | 465 (19.8%) | .7 |
| Length of stay, median (IQR) | 4630 | 17 (12-26) | 16 (12-25) | .1 |
Median (Q1, Q3); n (%).
Wilcoxon signed-rank test (paired); McNemar’s test (paired).
DISCUSSION
A major finding of our study was that utilization of LDDs has increased since the allocation policy change in 2018. In fact, the proportion increased by 2.5-fold. This finding is reassuring in that the main goals in the allocation change were to encourage broader sharing of donor organs and decrease the waitlist mortality. This finding is corroborated by other series that have demonstrated longer average distances between donors and recipient hospitals following the allocation change.2–3,11–13
Despite the increase, another finding of our analysis was that the proportion of overall HTs using LDDs still remains low at <15%. This suggests that although in aggregate the use of LDDs is increasing, there remains an opportunity to increase this proportion further. Nearly half of the centres in our analysis had a >5% increase in the use of LDDs, but the remaining half had either no significant change or, in a small minority, a decrease of >5%. Although our study was not designed to specifically analyse donor acceptance rates or use of machine perfusion technology or donor types (DCD vs donation after brain death [DBD]). The centres remaining stagnant or decreasing their use of LDDs may represent target centres for improvement and sharing of practices, for example, to ensure utilization of technologies that can expand distances travelled.
It is well established that a correlation exists between centre volume and aggressiveness in donor acceptance.14,15 Therefore, expectedly, in our analysis, the average centre volume was higher in the LDD unmatched group. However, in the multivariable analysis of the matched cohort, the centre volume was not associated with short and long-term mortality.
Another major finding of the study is that distance is not a surrogate of ischaemia time in the matched group (r = 0.19, r2 = 0.04, β = 0.001). The decision-making regarding pairing a donor with a potential recipient is multifactorial and complex. In our matched cohort, even though ischaemia time was associated with worse survival but LDD use improved the survival. Heart failure clinicians typically quote donor age and ischaemia time as the 2 key factors in considering a donor, although other variables are important as well, and some are considered absolute contraindications such as severe left ventricular hypertrophy or severely reduced ejection fraction.16 One of the major findings of our study was significantly improved survival in the short and long-term periods with LDD use in the matched cohort. LDD is placed in the context of other donor risk factors as well as recipient risk factors, and this finding underscores the hesitation some programs may have in extended distance and ischaemia times for recipients that have risk factors for primary graft dysfunction.17–22 Our additional analysis has shown significant survival benefit with a 11% reduction in the hazard of 30-day mortality, 7% reduction in the hazard of 1-year mortality, and a 6% reduction in 5-year mortality for every 100 mile of distance between donor and recipient centre (Table S5) which is similar to the previous study.10
In early 2022, only 20 of 121 transplant centres in the US were performing the DCD HT using the perfusion technology.23 Continued education of non-utilizing programs, continued sharing of multicentre registry and trial data, and development and democratization of best practices remain prudent to expanding the use of LDDs.
Limitations
This study has several limitations. As with most multicentre registries, the UNOS registry is susceptible to errors in data entry and missing data. There might be confounders which were not captured during the initial data collection. This registry does not contain cost data which could be useful to analyse the broader use of newer technologies to expand the donor pool. The biases that exist in decision-making in accepting donors were not able to be accounted for in this analysis. Distance was dichotomized into ≤500 and >500 miles as UNOS prioritizes status 1-2 up to 500 miles and status 3-6 up to 250 miles. Some other limitations of the study are lack of information on transportation mechanism, preservation techniques, and also the centre variation.
CONCLUSIONS
The proportion of LDDs used in the United States has increased 2.5-fold following the 2018 allocation change with improved post-transplant survival. However, only about 15% of HTs use LDDs in the modern era. Short- and long-term survival outcomes with LDD use in spite of longer ischaemia time are not adversely affected by their selected and appropriate use. Distance is not a surrogate for ischaemia time (r = 0.19). The availability of newer technologies can facilitate even more use of LDDs. It appears that sharing of best practices and continued education of non-utilizing or lower-utilizing LDD programs is prudent.
Supplementary Material
Contributor Information
Krishna Bhandari, Division of Cardiothoracic Surgery, Medical University of South Carolina, Charleston, SC 29425, United States.
Khaled Shorbaji, Division of Cardiothoracic Surgery, Medical University of South Carolina, Charleston, SC 29425, United States.
Akinwale Victor Famotire, Division of Cardiothoracic Surgery, Medical University of South Carolina, Charleston, SC 29425, United States.
Brett Welch, Division of Cardiothoracic Surgery, Medical University of South Carolina, Charleston, SC 29425, United States.
Lucas Witer, Division of Cardiothoracic Surgery, Medical University of South Carolina, Charleston, SC 29425, United States.
Nicolas Pope, Division of Cardiothoracic Surgery, Medical University of South Carolina, Charleston, SC 29425, United States.
Arman Kilic, Division of Cardiothoracic Surgery, Medical University of South Carolina, Charleston, SC 29425, United States.
AUTHOR CONTRIBUTIONS
K.B.: methodology, formal analysis interpretation, original draft writing—review, editing. K.S.: statistical analysis. A.V.F.: statistical analysis. B.W.: data management and coordination. L.W.: writing—editing. N.P.: writing—editing. A.K.: project concept, editing, supervision.
SUPPLEMENTARY MATERIAL
Supplementary material is available at ICVTS online.
FUNDING
None declared.
CONFLICTS OF INTEREST
A.K. serves as a speaker and consultant for 3ive, LivaNova, Abiomed and Abbott. N.P. serves as a proctor for Tendyne TMVR, Abbott. All other authors declare that they have no conflict of interest to disclose.
DATA AVAILABILITY
The data used for this analysis are available from the United Network for Organ Sharing/Organ Procurement Transplant Network. Because of the sensitive nature of the data accessed for this study, requests to access the dataset from qualified researchers trained in human subject confidentiality protocols may be sent to United Network for Organ Sharing/Organ Procurement Transplant Network.
REFERENCES
- 1. Colvin MM, Smith JM, Ahn YS, et al. OPTN/SRTR 2022 annual data report: heart. Am J Transplant. 2024;24:S305-S393. 10.1016/j.ajt.2024.01.016 [DOI] [PubMed] [Google Scholar]
- 2. Siddiqi U, Lirette S, Hoang R, et al. Ischemic time and patient outcomes after the 2018 UNOS donor heart allocation system change. J Card Surg. 2022;37:2685-2690. 10.1111/jocs.16668 [DOI] [PubMed] [Google Scholar]
- 3. Kilic A, Mathier MA, Hickey GW, et al. Evolving trends in adult heart transplant with the 2018 heart allocation policy change. JAMA Cardiol. 2021;6:159-167. 10.1001/jamacardio.2020.4909 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Dhital KK, Iyer A, Connellan M, et al. Adult heart transplantation with distant procurement and ex vivo preservation of donor hearts after circulatory death: a case series. Lancet. 2015;385:2585-2591. 10.1016/S0140-6736(15)60038-1 [DOI] [PubMed] [Google Scholar]
- 5. Iyer A, Gao L, Doyle A, et al. Normothermic ex vivo perfusion provides superior organ preservation and enables viability assessment of hearts from DCD donors. Am J Transplant. 2015;15:371-380. 10.1111/ajt.12994 [DOI] [PubMed] [Google Scholar]
- 6. Silvestry S, Leacche M, Meyer DM, et al. Outcomes in heart transplant recipients by bridge to transplant strategy when using the SherpaPak cardiac transport system. ASAIO J. 2024;70:388-395. 10.1097/MAT.0000000000002137 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Billingham ME, Baumgartner WA, Watson DC, et al. Distant heart procurement for human transplantation. Ultrastructural studies. Circulation. 1980;62(2 Pt 2):I11-9. [PubMed] [Google Scholar]
- 8. Watson DC, Reitz BA, Baumgartner WA, et al. Distant heart procurement for transplantation. Surgery. 1979;86:56-59. [PubMed] [Google Scholar]
- 9. Lund LH, Khush KK, Cherikh WS, et al. ; International Society for Heart and Lung Transplantation. The registry of the international society for heart and lung transplantation: thirty-fourth adult heart transplantation report-2017; focus theme: allograft ischemic time. J Heart Lung Transplant. 2017;36:1037-1046. 10.1016/j.healun.2017.07.019 [DOI] [PubMed] [Google Scholar]
- 10. Crawford TC, Magruder JT, Grimm JC, et al. The paradoxical relationship between donor distance and survival after heart transplantation. Ann Thorac Surg. 2017;103:1384-1391. 10.1016/j.athoracsur.2017.01.055 [DOI] [PubMed] [Google Scholar]
- 11. Ganapathi AM, Lampert BC, Mokadam NA, et al. Allocation changes in heart transplantation: what has really changed? J Thorac Cardiovasc Surg. 2023;165:724-733.e7. 10.1016/j.jtcvs.2021.03.031 [DOI] [PubMed] [Google Scholar]
- 12. Hoffman JRH, Larson EE, Rahaman Z, et al. Impact of increased donor distances following adult heart allocation system changes: a single center review of 1-year outcomes. J Card Surg. 2021;36:3619-3628. 10.1111/jocs.15795 [DOI] [PubMed] [Google Scholar]
- 13. Goff RR, Uccellini K, Lindblad K, et al. A change of heart: preliminary results of the US 2018 adult heart allocation revision. Am J Transplant. 2020;20:2781-2790. 10.1111/ajt.16010 [DOI] [PubMed] [Google Scholar]
- 14. Rajagopalan N, Shafii AE, Dennis DR, Charnigo R, Sekela ME. Increasing heart transplant volume by expansion of donor heart selection criteria: a single-center analysis. Transplant Proc. 2020;52:949-953. 10.1016/j.transproceed.2020.01.029 [DOI] [PubMed] [Google Scholar]
- 15. Mori M, Wilson L, Ali A, et al. Evaluation of case volumes of a heart transplant program and short-term outcomes after changes in the united network for organ sharing donor heart allocation system. JAMA Netw Open. 2020;3:e2017513. 10.1001/jamanetworkopen.2020.17513 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Weiss ES, Allen JG, Kilic A, et al. Development of a quantitative donor risk index to predict short-term mortality in orthotopic heart transplantation. J Heart Lung Transplant. 2012;31:266-273. 10.1016/j.healun.2011.10.004 [DOI] [PubMed] [Google Scholar]
- 17. Smith NF, Salehi Omran S, Genuardi MV, et al. Primary graft dysfunction in heart transplant Recipients-Risk factors and longitudinal outcomes. ASAIO J. 2022;68:394-401. 10.1097/MAT.0000000000001469 [DOI] [PubMed] [Google Scholar]
- 18. Singh SSA, Dalzell JR, Berry C, Al-Attar N. Primary graft dysfunction after heart transplantation: a thorn amongst the roses. Heart Fail Rev. 2019;24:805-820. 10.1007/s10741-019-09794-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Truby LK, Takeda K, Topkara VK, et al. Risk of severe primary graft dysfunction in patients bridged to heart transplantation with continuous-flow left ventricular assist devices. J Heart Lung Transplant. 2018;37:1433-1442. 10.1016/j.healun.2018.07.013 [DOI] [PubMed] [Google Scholar]
- 20. Lerman JB, Patel CB, Casalinova S, et al. Early outcomes in patients with LVAD undergoing heart transplant via use of the SherpaPak cardiac transport system. Circ Heart Fail. 2024;17:e010904. 10.1161/CIRCHEARTFAILURE.123.010904 [DOI] [PubMed] [Google Scholar]
- 21. Schroder JN, D'Alessandro D, Esmailian F, et al. Successful utilization of extended criteria donor (ECD) hearts for transplantation—results of the OCS heart EXPAND trial to evaluate effectiveness and safety of the OCS heart system to preserve and assess ECD hearts for transplantation. J Heart Lung Transplant. 2019;38:S42. 10.1016/j.healun.2019.01.088 [DOI] [Google Scholar]
- 22. Rylski B, Berchtold-Herz M, Olschewski M, et al. Reducing the ischemic time of donor hearts will decrease morbidity and costs of cardiac transplantations. Interact CardioVasc Thorac Surg. 2010;10:945-947. 10.1510/icvts.2009.223719 [DOI] [PubMed] [Google Scholar]
- 23. Kwon JH, Ghannam AD, Shorbaji K, et al. Early outcomes of heart transplantation using donation after circulatory death donors in the United States. Circ Heart Fail. 2022;15:e009844. 10.1161/CIRCHEARTFAILURE.122.009844 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data used for this analysis are available from the United Network for Organ Sharing/Organ Procurement Transplant Network. Because of the sensitive nature of the data accessed for this study, requests to access the dataset from qualified researchers trained in human subject confidentiality protocols may be sent to United Network for Organ Sharing/Organ Procurement Transplant Network.