Abstract
Objectives
Body weight is still the most common metric used for donor-recipient matching in Pediatric Heart Transplantation (PHTX) and its impact on long-term outcomes remains unclear from variable definition and conflicting evidence. In this study, the outcomes of varying weight mismatch on PHTX was analysed.
Methods
The United Network for Organ Sharing (UNOS) database (1984–2025) was retrospectively analysed. Size mismatch as a percentage donor-recipient weight difference was categorized as Mild (≤ 20%), Moderate (20–30%), Extreme (> 30%), and stratified as undersizing or oversizing. Demographics, clinical characteristics, and post-transplant outcomes were compared. One-year and 15-year mortality were analysed using multivariable logistic and Cox regression, respectively.
Results
Oversizing (80%, 9,175/11,583) was more common than undersizing (20%, 2,408/11,583). With increasing oversizing, the recipients were younger, had congenital heart disease (CHD) diagnosis, and elevated pulmonary vascular resistance but with shorter wait-list times. Oversizing had no impact on 30-day, 1-year, or 15-years survival. With increasing undersizing, recipient age was similar, had less restrictive cardiomyopathy diagnosis, and had female donor but with shorter wait-list times. Undersizing had inferior survival till 5-years but not at 15-years. However, on multivariable analysis, undersizing was not predictive of inferior survival anytime as was congenital heart disease diagnosis, Extracorporeal Membrane Oxygenation (ECMO) bridge, elevated pulmonary-vascular resistance, transplantation era, and post-transplant rejection, stroke, and dialysis use.
Conclusions
Oversizing is more likely than undersizing in PHTX. While oversizing had no overall survival impact, undersizing had negative mid-term but not long-term survival impact which disappeared when adjusted for underlying cardiac diagnosis, elevated pulmonary vascular resistance, transplantation era and occurrence of post-transplant complications.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1007/s12055-026-02195-8.
Keywords: Donor-recipient weight mismatch, Pediatric heart transplantation, Survival outcomes
Introduction
Pediatric heart transplantation (PHTX) remains the definitive therapy for children with end-stage heart failure, predominantly resulting from congenital heart disease or cardiomyopathy [1]. The lack of suitable donor organs remains a major obstacle, frequently resulting in longer waitlist times and higher waitlist mortality rates [2, 3].
Traditionally, the primary metric of size compatibility in pediatric heart transplantation has been donor-recipient weight ratio (DRWR) [4, 5]. While a number of alternative indices based on anthropometrics, and imaging data have been described to better match donor to recipients, weight based matching still remains the most common method due to familiarity, unavailability of certain donor data to calculate the aforementioned indices [6, 7].
Donor-recipient size mismatch can influence outcomes beyond the immediate postoperative period. While modest oversizing, especially in infant recipients appears to be well tolerated or even helpful, undersized grafts have been linked with increased risks of graft failure, early rejection, and mortality [8]. Though DRWR is still the clinical norm, rigorous adherence to limited thresholds may unnecessarily restrict donor use, particularly for high acuity candidates on the wait-list. The evidence already in publication is still conflicting: Mismatch has been shown to have negative effects in certain subgroups (such as patients with mechanical circulatory support or elevated pulmonary vascular resistance), but other studies find no discernible long-term effects on survival [8, 9]. Small sample sizes, inconsistent mismatch definitions, and inadequate risk adjustment usually attribute for these discrepancies.
In this study, we examine the relationship between donor-recipient size mismatch and post–pediatric heart transplant outcomes, aiming to clarify the clinical significance of size disparity and determine whether observed outcomes are independently attributable to mismatch or confounded by recipient characteristics. Although alternative measures such estimated total cardiac volume have shown better physiological relevance and predictive value in some populations, as previously alluded, their use is limited in large retrospective datasets due to the lack of uniformly available imaging and anatomical data. In contrast, DRWR is universally recorded, clinically familiar, and readily applicable for large-scale risk stratification. By categorizing the severity of weight mismatch, we seek to assess its independent prognostic impact and contribute to a more evidence-based understanding of acceptable donor-recipient size thresholds in pediatric transplantation.
Materials and methods
Data source and study population
We conducted a retrospective cohort study using data from the Organ Procurement and Transplantation Network (OPTN), United Network for Organ Sharing (UNOS) database, including all pediatric primary heart transplants performed in the United States between 1984 and 2025. Pediatric recipients were defined as individuals under 18 years of age undergoing primary, isolated heart transplantation. Patients were excluded if key data were missing or implausible, including donor or recipient weight and recipient age. After applying these criteria, the final study population included 11,583 pediatric heart transplant recipients.
Mismatch definition and derived metrics
Donor-recipient size compatibility was quantified using the relative weight difference, calculated as the absolute value of the difference between recipient and donor weight divided by the recipient’s weight and expressed as a percentage. This value reflects the extent of undersizing or oversizing, irrespective of direction. Recipients were classified into three categories based on this metric: Mild Mismatch (≤ 20% difference), Moderate Mismatch (20–30%), and Extreme Mismatch (> 30%).
To distinguish the direction of mismatch, recipients were further divided into two groups: undersizing (donor weight less than recipient weight) and oversizing (donor weight greater than recipient weight). Within the oversizing group, 2,415 recipients had a Mild mismatch, 1,118 had a Moderate mismatch, and 5,642 had an Extreme mismatch. For the undersizing group, 1,914 recipients had a Mild mismatch, 352 had a Moderate mismatch, and 142 had an Extreme mismatch (Fig. 1). This approach allowed for the assessment of mismatch magnitude while accounting for directional effects, enabling separate analyses of undersized and oversized transplants across these three strata.
Fig. 1.
Distribution of weight disparity among pediatric heart TX
Variables and outcomes
Extracted variables included demographic factors (recipient age, sex, race, ethnicity, blood type, and transplant era), clinical status at listing, renal and hepatic function, ischemic time, and waitlist status. Post-transplant complications such as dialysis, stroke, pacemaker placement, and late graft rejection were also recorded. Survival status was determined at multiple time points, including 30 days, 1 year, 5 years, 10 years, and 15 years post-transplant.
The primary endpoints were early mortality (1-year post-transplant mortality) and late mortality (15 years post-transplant mortality). Early mortality was analyzed using multivariable logistic regression, while late mortality was assessed using Cox proportional hazards regression.
Statistical analysis
Descriptive statistics were computed using Python (version 3.11) with the pandas, NumPy, and SciPy libraries. Analyses were performed using complete-case data; observations with missing values in any of the variables under consideration were excluded. No imputation methods were applied. Continuous variables were summarized using means with standard deviations or medians with interquartile ranges, as appropriate. Hemodynamic measures, including Pulmonary Vascular Resistance (PVR) and Transpulmonary Gradient (TPG), were reported as medians with minimum and maximum values. Categorical variables were reported as frequencies and percentages. Comparisons across mismatch groups were made using the Kruskal–Wallis tests for continuous variables and chi-squared or Fisher exact tests for categorical variables, depending on distribution and group size.
Multivariable regression analyses were performed in R (version 4.4.0). Variable selection for multivariable modeling was based on univariate statistical testing and clinical relevance. Candidate covariates were assessed using appropriate tests based on data type and distribution. Variables with a univariate p-value less than 0.05 were selected for inclusion in the multivariable logistic and Cox proportional hazards model. Mismatch was modeled both categorically and continuously using ordinal scores (1 for Mild, 2 for Moderate, 3 for Extreme) to assess linear and quadratic trends. Inclusion of both trend terms allowed for evaluation of non-linear associations, enabling detection of potential U-shaped or J-shaped risk patterns across the Mismatch spectrum. Survival differences were visualized using Kaplan–Meier curves and compared with the log-rank test. Hazard ratios (HR) and odds ratios (OR) were reported with corresponding 95% confidence intervals (CI). Long-term survival was assessed using Cox proportional hazards regression. A two-tailed P value < 0.05 was considered statistically significant for all analyses.
Results
Baseline characteristics
Oversizing
Baseline characteristics differed significantly across oversizing severity groups (Table 1). Recipients in the extreme oversizing group (donor > 30% heavier; n = 5,642) were younger (median age 2.0 years [IQR 0.0–10.0]) compared to moderate (7.0 [1.0–14.0]) and mild (8.0 [1.0–14.0]) mismatch groups (p < 0.001). They also had lower body weight and BMI at transplant (p < 0.001). The median weight disparity across oversizing categories was 10.22% in the mild group, 25.00% in the moderate group, and 66.15% in the extreme group, with a maximum mismatch of 480%. Congenital heart disease (CHD) was more common in the extreme group (53.3%), contributing to significant differences in heart failure etiology across groups (p < 0.001). Donors in this group were younger (median age 5.0 years [1.0–14.0]; p < 0.001), and rates of height mismatch (> ± 5%) and ABO incompatibility were significantly higher (81.7% and 6.2%, respectively; p < 0.001).
Table 1.
Transplant characteristics and demographics
| Variable | Oversizing (0.01%—480.46%) |
Undersizing (0%—79.84%) |
||||||
|---|---|---|---|---|---|---|---|---|
| Mild (≤ 20%) n = 2415, 26.3% |
Moderate (20–30%) n = 1118, 12.2% |
Extreme (> 30%) n = 5642, 61.5% |
P-value | Mild (≤ 20%) n = 1914, 79.5% |
Moderate (20–30%) n = 352, 14.6% |
Extreme (> 30%) n = 142, 5.9% |
P-value | |
| Weight Disparity (%) [Median (Range)] | 10.22 (0.01–20.00) | 25.00 (20.00–30.00) | 66.15 (30.00–480.46) | < 0.001 | 7.89 (0.0–20.00) | 23.84 (20.10–30.00) | 36.55(30.13–79.84) | < 0.001 |
| Recipient Characteristics | ||||||||
| Age (years) | 8.00 [1.00–14.00] | 7.00 [1.00–14.00] | 2.00 [0.00–10.00] | < 0.001 | 8.00 [1.00–14.00] | 7.00 [0.00–15.00] | 8.50 [0.00–14.75] | 0.417 |
| Female | 1048 (43.4%) | 481 (43.0%) | 2514 (44.6%) | 0.475 | 808 (42.2%) | 148 (42.0%) | 57 (40.1%) | 0.890 |
| Race | 0.003 | 0.536 | ||||||
| Caucasian | 1367 (56.6%) | 612 (54.7%) | 3237 (57.4%) | 1062 (55.5%) | 196 (55.7%) | 71 (50.0%) | ||
| African American | 487 (20.2%) | 240 (21.5%) | 985 (17.5%) | 401 (21.0%) | 82 (23.3%) | 40 (28.2%) | ||
| Hispanic | 426 (17.6%) | 196 (17.5%) | 1046 (18.5%) | 329 (17.2%) | 59 (16.8%) | 22 (15.5%) | ||
| Asian | 72 (3.0%) | 31 (2.8%) | 229 (4.1%) | 69 (3.6%) | 11 (3.1%) | 4 (2.8%) | ||
| Other | 60 (2.5%) | 35 (3.1%) | 133 (2.4%) | 51 (2.7%) | 4 (1.1%) | 5 (3.5%) | ||
| Body Mass Index (kg/m2) | 17.53 [15.19–21.21] | 17.24 [15.12–20.00] | 15.80 [14.14–17.78] | < 0.001 | 17.98 [15.38–23.91] | 18.81 [16.05–28.10] | 21.15 [16.69–31.74] | < 0.001 |
| Blood Type | 0.664 | 0.774 | ||||||
| O | 1060 (43.9%) | 512 (45.8%) | 2547 (45.1%) | 887 (46.3%) | 167 (47.4%) | 68 (47.9%) | ||
| B | 333 (13.8%) | 141 (12.6%) | 718 (12.7%) | 242 (12.6%) | 45 (12.8%) | 23 (16.2%) | ||
| A | 915 (37.9%) | 426 (38.1%) | 2140 (37.9%) | 705 (36.8%) | 122 (34.7%) | 45 (31.7%) | ||
| AB | 107 (4.4%) | 39 (3.5%) | 237 (4.2%) | 80 (4.2%) | 18 (5.1%) | 6 (4.2%) | ||
| Heart Failure Etiology | < 0.001 | 0.029 | ||||||
| Congenital Heart Disease | 1094 (45.3%) | 523 (46.8%) | 3009 (53.3%) | 813 (42.5%) | 145 (41.2%) | 58 (40.8%) | ||
| Restrictive Cardiomyopathy | 241 (10.0%) | 105 (9.4%) | 421 (7.5%) | 167 (8.7%) | 38 (10.8%) | 6 (4.2%) | ||
| Dilated Cardiomyopathy | 981 (40.6%) | 445 (39.8%) | 2031 (36.0%) | 867 (45.3%) | 161 (45.7%) | 66 (46.5%) | ||
| Valvular | 10 (0.4%) | 3 (0.3%) | 18 (0.3%) | 6 (0.3%) | 2 (0.6%) | 1 (0.7%) | ||
| Ischemic | 4 (0.2%) | 2 (0.2%) | 15 (0.3%) | 5 (0.3%) | 0 (0.0%) | 0 (0.0%) | ||
| Other | 85 (3.5%) | 40 (3.6%) | 148 (2.6%) | 56 (2.9%) | 6 (1.7%) | 11 (7.7%) | ||
| Cerebrovascular accident prior transplant | 73 (3.0%) | 29 (2.6%) | 138 (2.4%) | 0.434 | 52 (2.7%) | 9 (2.6%) | 3 (2.1%) | 0.977 |
| Prior cardiac surgery | 119 (4.9%) | 47 (4.2%) | 278 (4.9%) | 0.790 | 69 (3.6%) | 11 (3.1%) | 7 (4.9%) | 0.430 |
| ECMO Bridge | 97 (4.0%) | 39 (3.5%) | 279 (4.9%) | 0.038 | 82 (4.3%) | 21 (6.0%) | 6 (4.2%) | 0.372 |
| Recipient on VAD | 288 (11.9%) | 151 (13.5%) | 569 (10.1%) | < 0.001 | 216 (11.3%) | 27 (7.7%) | 12 (8.5%) | 0.089 |
| Prior Dialysis History | 52 (2.2%) | 31 (2.8%) | 129 (2.3%) | 0.523 | 44 (2.3%) | 5 (1.4%) | 4 (2.8%) | 0.474 |
| Total bilirubin (mg/dL) | 0.60 [0.40–1.10] | 0.70 [0.40–1.20] | 0.60 [0.40–1.20] | 0.298 | 0.60 [0.40–1.10] | 0.60 [0.30–1.20] | 0.60 [0.40–0.90] | 0.546 |
| Serum creatinine (mg/dL) | 0.50 [0.32–0.70] | 0.50 [0.30–0.70] | 0.40 [0.30–0.60] | < 0.001 | 0.50 [0.30–0.71] | 0.60 [0.34–0.80] | 0.60 [0.40–0.90] | 0.005 |
| Positive CMV serology | 748 (31.0%) | 355 (31.8%) | 1570 (27.8%) | 0.291 | 578 (30.2%) | 99 (28.1%) | 33 (23.2%) | 0.546 |
| Cardiac index (L/min/m2) [mean ± SD] | 3.12 ± 1.93 | 3.25 ± 2.13 | 3.62 ± 2.79 | < 0.001 | 3.09 ± 2.29 | 2.73 ± 1.88 | 2.49 ± 1.53 | 0.003 |
| Mean pulmonary artery pressure (mm Hg) [mean ± SD] | 24.86 ± 11.23 | 24.63 ± 11.00 | 25.04 ± 11.71 | 0.959 | 25.27 ± 11.19 | 25.95 ± 11.17 | 27.73 ± 12.16 | 0.171 |
| Mean pulmonary capillary wedge pressure (mm Hg) [mean ± SD] | 16.60 ± 8.14 | 16.28 ± 7.36 | 15.76 ± 7.46 | 0.014 | 16.95 ± 8.38 | 17.28 ± 8.05 | 17.65 ± 8.73 | 0.688 |
| Pulmonary vascular resistance (Woods units) [Median (min – max)] | 2.40 (0.13–98.00) | 2.31 (0.18–62.22) | 3.04 (0.10–115.00) | < 0.001 | 2.31 (0.10–32.14) | 2.19 (0.30–48.65) | 2.17 (0.23–27.66) | 0.946 |
| Trans-pulmonary gradient (mm Hg) [Median (min – max)] | 7.00 (1.00–73.00) | 7.00 (1.00–57.00) | 8.00 (1.00–59.00) | 0.001 | 7.00 (1.00–58.00) | 7.00 (1.00–68.70) | 8.00 (1.00–40.00) | 0.292 |
| Donor Characteristics | ||||||||
| Age (years) | 7.00 [1.00–16.00] | 7.00 [1.00–17.00] | 5.00 [1.00–14.00] | < 0.001 | 6.00 [0.00–16.00] | 5.00 [0.00–16.00] | 5.50 [0.00–15.00] | 0.051 |
| Female | 969 (40.1%) | 448 (40.1%) | 2315 (41.0%) | 0.681 | 760 (39.7%) | 166 (47.2%) | 65 (45.8%) | 0.017 |
| Race | 0.036 | 0.312 | ||||||
| Caucasian | 1379 (57.1%) | 650 (58.1%) | 3141 (55.7%) | 1089 (56.9%) | 220 (62.5%) | 90 (63.4%) | ||
| African American | 521 (21.6%) | 234 (20.9%) | 1190 (21.1%) | 397 (20.7%) | 65 (18.5%) | 33 (23.2%) | ||
| Hispanic | 419 (17.3%) | 199 (17.8%) | 1153 (20.4%) | 367 (19.2%) | 60 (17.0%) | 17 (12.0%) | ||
| Asian | 49 (2.0%) | 19 (1.7%) | 87 (1.5%) | 31 (1.6%) | 4 (1.1%) | 0 (0.0%) | ||
| Other | 43 (1.8%) | 15 (1.3%) | 65 (1.2%) | 29 (1.5%) | 3 (0.9%) | 2 (1.4%) | ||
| Body mass index (kg/m2) | 18.55 [15.87–22.23] | 18.94 [16.13–22.37] | 18.56 [16.12–21.97] | 0.253 | 18.19 [15.55–22.03] | 17.26 [14.54–20.94] | 16.29 [13.44–20.70] | < 0.001 |
| Blood type | 0.210 | 0.642 | ||||||
| O | 1426 (59.0%) | 671 (60.0%) | 3260 (57.8%) | 1151 (60.1%) | 217 (61.6%) | 92 (64.8%) | ||
| B | 226 (9.4%) | 99 (8.9%) | 502 (8.9%) | 163 (8.5%) | 29 (8.2%) | 14 (9.9%) | ||
| A | 729 (30.2%) | 336 (30.1%) | 1770 (31.4%) | 579 (30.3%) | 104 (29.5%) | 36 (25.4%) | ||
| AB | 34 (1.4%) | 12 (1.1%) | 110 (1.9%) | 21 (1.1%) | 2 (0.6%) | 0 (0.0%) | ||
| Mechanism of death | 0.224 | 0.352 | ||||||
| Head trauma | 1193 (49.4%) | 575 (51.4%) | 2805 (49.7%) | 991 (51.8%) | 167 (47.4%) | 70 (49.3%) | ||
| Anoxia | 886 (36.7%) | 387 (34.6%) | 2033 (36.0%) | 624 (32.6%) | 129 (36.6%) | 41 (28.9%) | ||
| CVA | 197 (8.2%) | 68 (6.1%) | 418 (7.4%) | 170 (8.9%) | 27 (7.7%) | 17 (12.0%) | ||
| CNS tumor | 16 (0.7%) | 8 (0.7%) | 35 (0.6%) | 15 (0.8%) | 2 (0.6%) | 1 (0.7%) | ||
| Other | 120 (5.0%) | 79 (7.1%) | 346 (6.1%) | 114 (6.0%) | 27 (7.7%) | 13 (9.2%) | ||
| Mismatch and Transplant Characteristics | ||||||||
| Gender Mismatch | 1125 (46.6%) | 533 (47.7%) | 2731 (48.4%) | 0.323 | 844 (44.1%) | 150 (42.6%) | 70 (49.3%) | 0.394 |
| Height Mismatch (> ± 5%) | 1333 (55.2%) | 697 (62.3%) | 4611 (81.7%) | < 0.001 | 1041 (54.4%) | 202 (57.4%) | 82 (57.7%) | 0.465 |
| Donor Weight (kg) | 26.90 [10.50–59.00] | 26.90 [10.43–60.30] | 20.00 [11.00–52.70] | < 0.001 | 22.00 [8.17–59.25] | 18.00 [5.27–55.08] | 20.20 [4.00–54.00] | < 0.001 |
| Recipient Weight (kg) | 24.00 [9.70–53.90] | 21.20 [8.43–48.97] | 12.00 [6.10–30.40] | < 0.001 | 23.50 [9.08–63.58] | 23.85 [7.14–74.03] | 35.25 [6.98–85.70] | 0.501 |
| ABO incompatibility | < 0.001 | 0.317 | ||||||
| Compatible | 2328 (96.4%) | 1076 (96.2%) | 5290 (93.8%) | 1839 (96.1%) | 344 (97.7%) | 137 (96.5%) | ||
| Incompatible | 87 (3.6%) | 42 (3.8%) | 352 (6.2%) | 75 (3.9%) | 8 (2.3%) | 5 (3.5%) | ||
| HLA mismatch (> 3 loci mismatch) | 1796 (74.4%) | 806 (72.1%) | 4022 (71.3%) | 0.018 | 1372 (71.7%) | 237 (67.3%) | 106 (74.6%) | 0.164 |
| Donor distance to transplanting center (nautical miles) | 292.00 [119.75–444.00] | 299.00 [120.00–460.50] | 305.00 [130.00–473.00] | 0.008 | 281.00 [102.00–450.00] | 290.00 [93.00–474.00] | 317.00 [125.00–496.00] | 0.123 |
| Ischemic Time (hours) | 3.58 [2.93–4.20] | 3.57 [2.92–4.20] | 3.62 [2.97–4.28] | 0.026 | 3.53 [2.88–4.18] | 3.56 [2.92–4.20] | 3.58 [2.98–4.35] | 0.637 |
| Heart Listing Status at the time of transplant | ||||||||
| Before Allocation Change (n) | 527 | 188 | 1245 | < 0.001 | 449 | 110 | 69 | < 0.001 |
| Old Status 1 | 243 (46%) | 99 (53%) | 807 (65%) | 230 (51%) | 71 (64%) | 50 (72%) | ||
| Status 2 | 284 (54%) | 89 (47%) | 438 (35%) | 219 (49%) | 39 (36%) | 19 (28%) | ||
| After Allocation Change (n) | 863 | 445 | 2026 | 0.001 | 613 | 79 | 28 | < 0.001 |
| Status 1A | 676 (78%) | 367 (82%) | 1701 (84%) | 499 (81%) | 63 (80%) | 26 (92.9%) | ||
| Status 1B | 157 (18%) | 71 (16%) | 277 (14%) | 96 (16%) | 13 (16%) | 1 (3.6%) | ||
| Status 2 | 30 (4%) | 7 (2%) | 48 (2%) | 18 (3%) | 3 (4%) | 1 (3.6%) | ||
| Transplantation year | 2012.00 [2003.00–2018.00] | 2013.00 [2004.00–2019.00] | 2011.00 [2001.00–2019.00] | < 0.001 | 2011.00 [2001.00–2018.00] | 2007.00 [1998.00–2015.00] | 2003.00 [1995.00–2013.00] | < 0.001 |
| Transplantation Era | < 0.001 | < 0.001 | ||||||
| 1987–1997 | 343 (14.2%) | 127 (11.4%) | 989 (17.5%) | 305 (15.9%) | 77 (21.9%) | 57 (40.1%) | ||
| 1998–2007 | 529 (21.9%) | 234 (20.9%) | 1292 (22.9%) | 442 (23.1%) | 101 (28.7%) | 32 (22.5%) | ||
| 2008–2017 | 837 (34.7%) | 399 (35.7%) | 1678 (29.7%) | 677 (35.4%) | 109 (31.0%) | 33 (23.2%) | ||
| 2018–2025 | 706 (29.2%) | 358 (32.0%) | 1683 (29.8%) | 490 (25.6%) | 65 (18.5%) | 20 (14.1%) | ||
| Days on Waitlist (median [IQR]) | 55.00 [17.00–139.00] | 50.00 [18.00–123.00] | 46.00 [17.00–112.00] | < 0.001 | 53.00 [19.00–127.00] | 46.00 [15.00–104.00] | 39.50 [16.25–81.50] | 0.028 |
| Post-transplant outcomes | ||||||||
| 30 Day Survival | 2301 (95.3%) | 1070 (95.7%) | 5377 (95.3%) | 0.829 | 1832 (95.7%) | 330 (93.8%) | 124 (87.3%) | < 0.001 |
| 90 Day Survival | 2253 (93.3%) | 1053 (94.2%) | 5223 (92.6%) | 0.119 | 1783 (93.2%) | 324 (92.0%) | 121 (85.2%) | 0.002 |
| 1 Year Survival | 2175 (90.1%) | 989 (88.5%) | 4999 (88.6%) | 0.135 | 1722 (90.0%) | 314 (89.2%) | 113 (79.6%) | < 0.001 |
| 5 Year Survival | 1983 (82.1%) | 898 (80.3%) | 4530 (80.3%) | 0.151 | 1550 (81.0%) | 281 (79.8%) | 98 (69.0%) | 0.003 |
| Treated Acute Rejection- 1 year | 437 (18.1%) | 200 (17.9%) | 848 (15.0%) | 0.042 | 375 (19.6%) | 72 (20.5%) | 31 (21.8%) | 0.020 |
| Dialysis | 147 (6.1%) | 70 (6.3%) | 307 (5.4%) | 0.543 | 118 (6.2%) | 13 (3.7%) | 11 (7.7%) | 0.093 |
| Stroke | 66 (2.7%) | 27 (2.4%) | 195 (3.5%) | 0.043 | 53 (2.8%) | 3 (0.9%) | 4 (2.8%) | 0.105 |
| Permanent Pacemaker | 15 (0.6%) | 13 (1.2%) | 46 (0.8%) | 0.248 | 16 (0.8%) | 2 (0.6%) | 1 (0.7%) | 0.888 |
| Hospital Length of Stay (days) | 18.00 [12.00–33.00] | 19.00 [13.00–33.00] | 20.00 [13.00–35.00] | 0.007 | 19.00 [12.00–35.00] | 18.00 [12.00–33.00] | 22.00 [14.00–33.00] | 0.392 |
Continuous variables were compared using the Kruskal–Wallis test; categorical variables were compared using Chi-square or Fisher’s exact tests, as appropriate
Pre-transplant hemodynamic parameters also varied by oversizing severity (Table 1). Cardiac index increased with mismatch severity (extreme: 3.62 ± 2.79 vs. mild: 3.12 ± 1.93 L/min/m2; p < 0.001). The extreme group had lower pulmonary capillary wedge pressures (p = 0.014) and higher pulmonary vascular resistance (Median 3.04, Woods units; p < 0.001) and transpulmonary gradient (Median 8 mmHg, p = 0.001), indicating elevated pulmonary arterial pressure independent of the higher cardiac output. The Extracorporeal Membrane Oxygenation (ECMO) bridge was not different between the groups but Ventricular Assist Device (VAD) utilization as a bridge to transplant was lower in the extreme group. The donor hospital to transplant hospital distance was higher in the extreme oversize group as were the ischemic times (p < 0.05). The wait-list times were times were lower in the extreme mismatch group. While the extreme group enjoyed higher listing status with older listing status, this was not the case with the newer listing status (Table 1).
Survival outcomes at 30 days, 90 days, 1 year, and 5 years were comparable across oversizing groups (p > 0.05; Table 1). However, the extreme group had lower rates of treated acute rejection at 1 year (15.0% vs. 17.9–18.1%; p = 0.042), but higher stroke incidence (3.5% vs. 2.4–2.7%; p = 0.043). Median hospital length of stay increased with mismatch severity (p = 0.007).
Undersizing
In the undersizing cohort, recipient age did not differ significantly across groups (p = 0.417), though BMI was highest in the extreme group (Table 1). Conversely, the BMI was the lowest in the donors in the extreme mismatch group. Median weight disparity in the undersizing groups was 7.89% in the mild category, 23.84% in the moderate category, and 36.55% in the extreme category, with a maximum mismatch of 79.84%. Heart failure etiology varied by mismatch severity (p = 0.029), with restrictive cardiomyopathy being least frequent in the extreme group (4.2%). The proportion of female donors was higher in the extreme (45.8%) and moderate (47.2%) groups compared to mild (39.7%) (p = 0.017).
Hemodynamic data revealed a decreasing cardiac index with increasing undersizing severity (extreme: 2.49 ± 1.53 vs. mild: 3.09 ± 2.29 L/min/m2; p = 0.003; Table 1). Other hemodynamic parameters—including pulmonary artery pressure, wedge pressure, pulmonary vascular resistance, and transpulmonary gradient—did not differ significantly (p > 0.17). The use of ECMO or VAD as a bridge to transplant was not different. The extreme group enjoyed higher listing status with the older listing status but not with the newer listing status (Table 1). While the donor to recipient hospital distance was not different, the wait-list times was lower in the extreme mismatch group (p−0.03).
Survival progressively declined with increasing undersizing: 30-day (87.3% vs. 95.7%), 1-year (79.6% vs. 90.0%), and 5-year survival (69.0% vs. 81.0%) were lowest in the extreme mismatch group (p < 0.005; Table 1). Treated rejection at 1 year was more common in this group (21.8% vs. 19.6%; p = 0.020), while stroke, pacemaker use, and hospital stay were similar across groups (p > 0.10).
Temporal trends
Oversizing
Extreme oversizing increased over time, peaking in 2008–2017 (~ 30%) before a slight decline in 2018–2025. Moderate oversizing followed a similar pattern, while mild mismatches (≤ 20%) remained stable (Supplementary Fig. 1a). Age-wise, extreme oversizing was most frequent in infants (< 1 year; > 20%) and declined with age, whereas mild mismatches were more common in older recipients (11–17 years; ~ 11%) (Supplementary Fig. 2a).
Undersizing
Extreme undersizing declined steadily from ~ 40% in 1987–1997 to ~ 14% in 2018–2025. Mild undersizing increased over time (~ 16% to ~ 25%), while moderate undersizing peaked in 2008–2017 (~ 31%) (Supplementary Fig. 1b). Across ages, mild undersizing was most common, especially in recipients aged 11–17 years (~ 35%). Extreme undersizing remained rare, peaking at ~ 2% in infants (Supplementary Fig. 2b).
Kaplan–Meier survival
Survival over 15 years did not differ significantly among oversizing groups (log-rank p = 0.85), with comparable hazard ratios: moderate vs. mild (HR 0.96, p = 0.57), extreme vs. mild (HR 0.99, p = 0.83), and extreme vs. moderate (HR 1.03, p = 0.64) (Fig. 2). In undersized recipients, long-term survival was also similar across groups (log-rank p = 0.32), with no statistically significant differences observed: moderate vs. mild (HR 1.07, 95% CI: 0.89–1.28, p = 0.49), extreme vs. mild (HR 1.21, 95% CI: 0.94–1.57, p = 0.14), and extreme vs. moderate (HR 1.14, 95% CI: 0.84–1.53, p = 0.41) (Fig. 3a). However, 5-year Kaplan–Meier analysis revealed significantly worse early survival in extreme undersizing: survival at 60 months was 81–82% in mild, 64–65% in moderate, and 58–59% in extreme groups (log-rank p < 0.001). Early mortality risk was notably higher in extreme vs. mild (HR 2.38, p < 0.001) and extreme vs. moderate (HR 1.93, p < 0.001), while moderate vs. mild showed a non-significant trend (HR 1.23, p = 0.11) (Fig. 3b).
Fig. 2.
Kaplan Meier curves: 15-year post-transplant survival by oversizing weight disparity group
Fig. 3.
Kaplan Meier curves: post-transplant survival by undersizing weight disparity group
Mortality predictors
Early mortality
Multivariable logistic regression models identified key predictors of 1-year mortality in both oversizing and undersizing cohorts (Figs. 4a and 5a). In neither group was weight disparity—whether categorized or modeled as a linear or quadratic trend—significantly associated with early mortality. In the oversizing group, independent predictors included higher recipient BMI, Congenital heart disease, ECMO bridge to transplant [OR: 2.56; 95%CI: (1.94—3.34)], elevated total bilirubin [OR: 1.03; 95%CI: (1.01—1.05)], increased cardiac index [OR: 1.06; 95%CI: (1.02—1.10)] and pulmonary vascular resistance [OR: 1.02; 95%CI: (1.00—1.03)], earlier transplant era [OR: 0.95; 95%CI: (0.94—0.96)], older [OR: 1.03; 95%CI: (1.01—1.04)] and smaller donors [OR: 0.98; 95%CI: (0.97–0.99)], and adverse post-transplant outcomes such as pacemaker requirement [OR: 1.96; 95%CI: (1.01—3.62)], dialysis [OR: 10.06; 95%CI: (8.09—12.54)] and stroke [OR: 2.56; 95%CI: (1.89—3.43)]. Similarly, in the undersizing cohort, significant predictors of early mortality included congenital heart disease [OR: 2.54; 95% CI: (1.74—3.75)], ECMO bridge to transplant [OR: 3.65; 95% CI: (2.11—6.20)], earlier transplant era [OR: 0.95; 95% CI: (0.93—0.98)], older UNOS Status 1 [OR: 1.93; 95% CI: (1.12—3.35)], and adverse post-transplant outcomes such as dialysis [OR: 10.42; 95% CI: (6.80—16.06)], and stroke [OR: 2.91; 95% CI: (1.46—5.58)].
Fig. 4.
Oversizing group: Predictors of mortality regression analysis forest plot
Fig. 5.
Undersizing group: Predictors of mortality regression analysis forest plot
Late mortality
Multivariable Cox regression models (Figs. 4b and 5b) revealed no significant association between weight disparity and 15-year mortality in either cohort, regardless of whether it was modeled categorically or continuously. In the oversizing group, independent predictors of late mortality included non-Caucasian race [HR: 1.34; 95% CI: (1.24—1.45)], higher BMI [HR: 1.02; 95% CI: (1.01—1.04)], valvular [HR: 1.83; 95% CI: (1.10—3.06)] and congenital heart failure etiologies [HR: 1.32; 95% CI: (1.21—1.44)], ECMO bridge [HR: 1.53; 95% CI: (1.30—1.79)], elevated bilirubin [HR: 1.01; 95% CI: (1.00—1.02)], increased pulmonary vascular resistance [HR: 1.01; 95% CI: (1.00—1.02)], gender mismatch [HR: 1.10; 95% CI: (1.02—1.18)], earlier transplant era [HR: 0.95; 95% CI: (0.94—0.95)], older donors [HR: 1.01; 95% CI: (1.00—1.02)], post-transplant dialysis [HR: 3.22; 95% CI: (2.84—3.65)], treated acute rejection history [HR: 1.14; 95% CI: (1.05—1.24)], and stroke [HR: 1.39; 95% CI: (1.17—1.65)]. In the undersizing group, significant predictors included non-caucasian race [HR: 1.62; 95% CI: (1.40—1.88)], congenital heart disease [HR: 1.48; 95% CI: (1.25—1.75)], ECMO bridge [HR: 1.71; 95% CI: (1.26—2.31)], earlier transplant era [HR: 0.95; 95% CI: (0.94—0.96)], older donors [HR: 1.02; 95% CI: (1.01—1.04)], and adverse post-transplant outcomes such as dialysis [HR: 3.51; 95% CI: (2.75—4.46)], and acute rejection treatment history [HR: 1.17; 95% CI: (1.01—1.35)]. Weight disparity—whether modeled as a linear or quadratic trend—did not demonstrate a statistically significant effect on long-term mortality.
Discussion
Body weight is commonly used as a surrogate to represent cardiac mass and size, particularly in the donor where normal anatomical and physiological constraints are expected to be present [4, 5]. Hence, weight based matching of donor to recipient is still the most commonly used method in pediatric heart transplantation. This is despite other methods such as volumetric matching based on cross sectional imaging such as using computed tomography or various nomograms based on a combination of height, age, gender and weight metrics and is due to familiarity and consistent availability of weight as a metric both in the donor and recipient and the converse in the other methods of matching as previously alluded [6, 7].
Of the 11,583 patients transplanted during the study period, 9175 (80%) had donor oversizing with 62% having extreme donor weight oversizing (> 30% mismatch) (Fig. 1). Donor to recipient oversizing is used when the recipient heart size is larger than expected based on recipient body weight and size such as in dilated cardiomyopathy where a larger heart from a bigger donor can be accommodated, thus expanding the donor pool and helping with the wait-list times [10, 11]. This was seen in this study where wait-list times were lower with increasing size mismatch (Table 1).
Donor to recipient oversizing is also used when an increased physiological demand is expected in the recipient. This could be due to higher afterload from increased pulmonary vascular resistance or the need for increased cardiac out output due to wastage from aortopulmonary collaterals as seen as in CHD, especially of the cyanotic type [12]. This was reflected in this study by the higher prevalence of primary cardiac diagnosis of CHD in the extreme oversizing group as was the presence of elevated cardiac output and higher pulmonary vascular resistance noted in the hemodynamic data in this group, reflecting the need for oversizing (Table 1).
The extreme oversizing group recipients were also younger in age. This is likely from correlation of CHD as the most common cause for cardiac transplantation in this age group and the need to oversize based on diagnosis as previously alluded [12]. Besides, the probability to oversize is higher with recipients of lower weight and age due to a larger donor pool with increasing age and hence weight in pediatric transplantation. Further, the increase in donor to recipient hospital distance and its resulting increase in the ischemic times seen in extreme oversizing group further supports the idea of oversizing as a measure to expand the donor pool in the younger age recipients and the resultant decrease the wait-list times with increasing extent of oversizing as seen in this study (Table 1).
Another reason for oversizing is the presence of a VAD as a bridge to transplantation. The VAD, besides expanding the mediastinal space, may justify oversizing due to the occurrence of elevated pulmonary vascular resistance [13]. However, the use of VAD was actually lower in extreme oversizing group and likely reflects the younger age group where VAD utilization and successful outcomes of a bridge to transplantation are generally lower [14].
Post transplant survival was similar both in the short-term and long-term till 15-years of follow-up in the oversizing group. Lack of effect of oversizing on post-transplant survival was further confirmed using multivariable models for at 1- year and 15-years survival. Previously described negative prognostic factors such as CHD diagnosis, ECMO bridge, elevated PVR, and occurrence post operative complications such need for dialysis and stroke were predictive of short-term and long-term mortality and not oversizing [4, 15, 16]. Extreme oversizing, defined as > 30% donor to recipient weight mismatch occurred in 62% of the patients in this study. This again suggests that oversizing, by cutting down wait-list times due to donor pool expansion, especially in the younger patients where donor scarcity is more likely may be only limited by the ability of the recipient mediastinal volume to accommodate the donor heart without any adverse consequence due to the mismatch on post-transplantation survival [12].
Interestingly, the treated rejection in the first year was lower in the extreme oversizing group. This likely due to the younger age of the recipient with higher immune tolerance. This was further corroborated by higher use of ABO transplant in this group. In addition, the extent of Human Leukocyte Antigen (HLA) mismatch was lower in this group [16].
As compared to oversizing, undersizing was less commonly utilized at 20% (2408) of the 11,583 patients transplanted in the study. Of this, only 6% were of the extreme undersizing group (> 30%).
The extreme undersizing group were less likely to have restrictive cardiomyopathy as the underlying diagnosis. While a smaller recipient heart size for a given body weight in restrictive cardiomyopathy maybe conducive for undersizing, the increased association of elevated pulmonary vascular in restrictive cardiomyopathy generally prevents such undersizing and may have been a plausible cause in this study [16]. While the transpulmonary gradient was higher in the extreme mismatch group in this study, it did not reach statistical significance (Table 1).
The undersizing group also had a lower cardiac listing status with the newer listing status as UNOS Status 2. Thus undersizing could have been a measure to increase the donor pool especially in this group. This was reflected by the decreased waitlist times in the extreme in oversizing group (Table 1). The 30-day, 1 year and 5-year survival was low, particularly in the extreme undersizing group (Table 1, Fig. 3). However, a follow-up to 15 years did not show a survival difference. The early survival penalty may reflect physiologic mismatch in select recipients, especially those with elevated pulmonary vascular resistance or higher transpulmonary gradients, where a smaller allograft could have limited hemodynamic reserve. Over time, graft adaptation may mitigate this disadvantage, resulting in convergence of survival curves in the long term [17, 18]. However, as seen in multivariable analysis, the only independent predictors of short and long-term mortality were CHD diagnosis, elevated pulmonary vascular resistance, occurrence of elevated pulmonary vascular resistance, older era of transplantation and occurrence of post-transplant complication such as dialysis use, stroke and treated rejection suggesting these were stronger drivers of a negative survival outcomes than undersizing itself (Fig. 6) [4, 15, 16].
Fig. 6.
Implications of donor-recipient size matching in pediatric heart transplantation
Limitations
The retrospective nature of this study and its dependence on the UNOS registry, which provides robust follow-up data at the national level but lacks specific clinical variables like imaging data, perioperative management, and surgical technique, are its main limitations. Besides clinical data such as mediastinal compromise such as airway compression, inability to close the chest, and cardiac compression after implantation of an oversized allograft are not available. Similarly, clinical considerations that may support the use of an undersized donor allograft such as precise pressure volume relationships, right-sided loading conditions, or individualized hemodynamic tolerance are not recorded. The absence of postoperative imaging further limits assessment of graft adaptation over time, precluding evaluation of ventricular remodeling in either oversizing or undersizing scenarios. Although DRWR is a universally accessible and clinically pragmatic metric, it fails to capture the anatomical or physiological subtleties of size matching. More precise volumetric assessments, such as estimated total cardiac volume (eTCV), provide enhanced physiological significance; however, they are impractical for extensive datasets due to the lack of standardized imaging data. Finally, unmeasured confounding, including center-level practices such as bias on donor preference based on weight might exist may limit generalizability and bias outcome associations.
Conclusion
Oversizing of the donor to the recipient is more common in pediatric heart transplantation than undersizing. Oversizing, especially extreme oversizing is more common in younger age recipients with congenital heart disease. Overall oversizing has no impact of short-term and long-term survival post transplantation. Undersizing was uncommonly used in pediatric transplantation, particularly extreme undersizing. While undersizing showed early to mid-term but not long-term survival disadvantage, this disappeared when corrected for factors such as underlying cardiac diagnosis, presence of elevated pulmonary vascular resistance, transplantation era, and occurrence of post-transplant complications such as stroke, rejection and dialysis use.
Supplementary Information
Era trends of weight disparity among heart TX. (PNG 111 kb)
Trends of weight disparity among Heart TX per Age group. (PNG 82.3 kb)
(PNG 91.3 KB)
Author contributions
Conceptualization: Rohit Ganduboina, Sandeep Sainathan; Methodology: Rohit Ganduboina, Sandeep Sainathan; Validation: Rohit Ganduboina, Sandeep Sainathan; Formal analysis, Investigation: Rohit Ganduboina; Data curation: Rohit Ganduboina; Writing-Original draft: Rohit Ganduboina, John Karamichalis, Xander Jacquemyn, Michel P. Sá, Leonardo Mulinari, Sandeep Sainathan; Visualization: Rohit Ganduboina; Writing- Review and editing: Rohit Ganduboina, John Karamichalis, Xander Jacquemyn, Michel P. Sá, Leonardo Mulinari, Sandeep Sainathan; Editing: Rohit Ganduboina, John Karamichalis, Xander Jacquemyn, Sandeep Sainathan; Supervision, Project administration: Sandeep Sainathan.
Funding
None.
Data availability
This study utilized data from the United Network for Organ Sharing (UNOS) registry. The dataset is publicly available upon application to UNOS/OPTN. All relevant derived data are presented within the article and Supplementary Materials.
Declarations
Ethical Committee Approval
This study utilized a publicly available, de-identified administrative database. As the dataset contains no direct patient identifiers, the study was deemed exempt from Institutional Review Board approval in accordance with institutional and federal guidelines.
Informed Consent
Individual informed consent was not required due to the use of de-identified secondary data.
Statement of Human and Animal Rights
The study was conducted in accordance with applicable ethical standards. As this analysis involved secondary use of de-identified administrative data, it did not involve direct interaction with human participants. No animals were involved in this study.
Conflicts of Interest
No conflicts of interest to declare.
Footnotes
Video abstract available as Electronic supplementary material.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Era trends of weight disparity among heart TX. (PNG 111 kb)
Trends of weight disparity among Heart TX per Age group. (PNG 82.3 kb)
(PNG 91.3 KB)
Data Availability Statement
This study utilized data from the United Network for Organ Sharing (UNOS) registry. The dataset is publicly available upon application to UNOS/OPTN. All relevant derived data are presented within the article and Supplementary Materials.