Video Abstract

Video Abstract

Close modal
OBJECTIVES

Progress in pediatric transplantation measured in the context of waitlist and posttransplant survival is well documented but falls short of providing a complete perspective for children and their families. An intent-to-treat analysis, in which we measure survival from listing to death regardless of whether a transplant is received, provides a more comprehensive perspective through which progress can be examined.

METHODS

Univariable and multivariable Cox regression was used to analyze factors impacting intent-to-treat survival in 12 984 children listed for heart transplant, 17 519 children listed for liver transplant, and 16 699 children listed for kidney transplant. The Kaplan-Meier method and log-rank test were used to assess change in waitlist, posttransplant, and intent-to-treat survival. Wait times and transplant rates were compared by using χ2 tests.

RESULTS

Intent-to-treat survival steadily improved from 1987 to 2017 in children listed for heart (hazard ratio [HR] 0.96, 95% confidence interval [CI] 0.96–0.97), liver (HR 0.95, 95% CI 0.94–0.97), and kidney (HR 0.97, 95% CI 0.95–0.99) transplant. Waitlist and posttransplant survival also improved steadily for all 3 organs. For heart transplant, the percentage of patients transplanted within 1 year significantly increased from 1987 to 2017 (60.8% vs 68.7%); however, no significant increase was observed in liver (68.9% vs 72.5%) or kidney (59.2% vs 62.7%) transplant.

CONCLUSIONS

Intent-to-treat survival, which is more representative of the patient perspective than individual metrics alone, steadily improved for heart, liver, and kidney transplant over the study period. Further efforts to maximize the donor pool, improve posttransplant outcomes, and optimize patient care while on the waitlist may contribute to future progress.

Subjects:
Transplantation
Topics:
heart, transplantation, waiting lists, liver, renal transplantation

What’s Known on This Subject:

Traditional metrics of transplant outcomes (waitlist and posttransplant survival) have revealed improvement in children listed for heart, liver, and kidney transplant. Unfortunately, persistent organ shortages create a substantial amount of uncertainty for children and their families at listing.

What This Study Adds:

This is an intent-to-treat survival analysis in which we follow patients from listing until death regardless of transplant status. As a function of waitlist and posttransplant survival, in addition to transplant rate, this metric provides a more comprehensive perspective on transplant outcomes.

For decades, transplantation has prolonged and improved quality of life for children with heart, liver, and kidney failure.1–4  Increased center experience, in conjunction with improvements in immunosuppressive therapy, allograft preservation, and patient education, has led to increasingly positive outcomes among children who receive a transplant.5–7  Mortality in children on the various waitlists has also seen significant improvement. For children with liver failure, development of the pediatric end-stage liver disease score created a tool for more efficient allograft allocation that has contributed to a reduction in mortality on the liver waitlist.8,9  Likewise, the development of the ventricular assist device and improvements in dialysis outcomes have contributed to improved waitlist survival among children with heart and kidney failure.10,11 

These efforts, in addition to countless others, have led to well-documented improvement over the years when measured in the context of waitlist and posttransplant survival. The Annual Data Report published in the American Journal of Transplantation provides yearly updates regarding these metrics, as well as trends in waitlist growth and the ongoing organ shortage.12  Alone, however, the currently presented individual metrics fall short of providing a complete picture for children and their families. When the decision is made to list a child for a transplant, there is no guarantee that a suitable donor allograft will become available.12  Furthermore, predicting how long a child will be on the waitlist remains an inexact science despite being one of the most commonly asked questions by children and their parents.13–15  Consequently, it has become increasingly important to understand both waitlist and posttransplant outcomes and how these factors affect each other across time. Intent-to-treat survival, which follows patients from listing to death regardless of whether it occurs on the waitlist or posttransplant, accounts for the ongoing organ shortage by eliminating the bias of different treatment options and is thus informative to children and families on day of listing. As a composite function of transplant rate, in addition to both waitlist and posttransplant survival, intent-to-treat provides a perspective that is more congruent with what a patient experiences when listed for transplant than individual metrics alone.16 

As such, the primary aim of this study is to examine changes in intent-to-treat survival in pediatric heart, liver, and kidney transplant from 1987 to 2017. By examining outcomes in a manner that is more congruent with the true patient experience, this study will contribute to a better patient perspective on progress in pediatric transplant.

A retrospective analysis was performed on deidentified patient-level data from the United Network for Organ Sharing (UNOS), including 47 202 patients aged <18 years at listing for either heart (n =12 984), liver (n = 17 519), or kidney (n = 16 699) transplant between 1987 and 2017. Only first-time transplant candidates were included. Figure 1 depicts patient inclusion and exclusion criteria. Rationale for patient inclusion and exclusion can be found in the Supplemental Information. In this analysis, we used the thoracic, liver, and kidney-pancreas registries with data collected by the Organ Procurement and Transplantation Network (OPTN).17  Information regarding the database and data quality can be found in the Supplemental Information. Institutional review board approval was waived because of use of deidentified, publicly available data. Only patient characteristics reported at the time of listing were used in this analysis.

FIGURE 1
Flowchart depicting patient inclusion and exclusion criteria. aIncludes patients listed for multiple organs (eg, heart-lung, liver-intestine, liver-kidney, kidney-pancreas, etc).
View largeDownload slide

Flowchart depicting patient inclusion and exclusion criteria. aIncludes patients listed for multiple organs (eg, heart-lung, liver-intestine, liver-kidney, kidney-pancreas, etc).

FIGURE 1
Flowchart depicting patient inclusion and exclusion criteria. aIncludes patients listed for multiple organs (eg, heart-lung, liver-intestine, liver-kidney, kidney-pancreas, etc).
View largeDownload slide

Flowchart depicting patient inclusion and exclusion criteria. aIncludes patients listed for multiple organs (eg, heart-lung, liver-intestine, liver-kidney, kidney-pancreas, etc).

Close modal

Of all patients listed, 8667 received heart transplant (66.8%), 12 744 received liver transplant (72.7%), and 14 301 received kidney transplant (85.6%). Patients were followed from the time of listing until death on the waitlist, death posttransplant, or last known follow-up.

Data were analyzed by using Stata 16.1 (Stata Corp, College Station, TX). Continuous variables were reported as mean ± SD and compared by using the Student's t test. Categorical variables were compared by using the χ2 test. Results are considered significant at a P value of <.05, and all reported P values are two-sided.

Intent-to-treat analysis was used to account for disease progression during time spent on the waitlist, organ availability, and outcomes after transplant. Not including intent-to-treat analysis when looking at transplant outcomes might underestimate the effects of disease progression for patients who remain on the waitlist for long periods of time. Additionally, improvements in intent-to-treat outcomes may more accurately highlight the long-term benefit of transplant while balancing the detriment attributable to waitlist mortality.18,19 

For intent-to-treat, waitlist, and posttransplant survival, the Kaplan-Meier method and log-rank test were used for a time-to-event analysis.20  For intent-to-treat survival, the primary outcome measured was patient death on the waitlist or after transplant. Deaths were not restricted to those related to the patient’s primary disease process, and all causes of death included (eg, trauma). For simplicity, removal from the waitlist because of clinical deterioration was considered to be equivalent to death in this analysis. Univariable and multivariable Cox regression were performed for further intent-to-treat analysis. Covariables found to be significant in univariable regression (defined as P < .05) were included in multivariable regression, in addition to listing era. Patients were separated on the basis of era of listing from 1987 to 1992, 1993–1998, 1999–2004, 2005–2010, and 2011–2017. The 2011–2017 era was used as a reference to which previous eras were compared. Multivariable analysis was also completed with listing year as a continuous variable. Listing year did not break assumptions for proportional hazards for all 3 organs (heart: P = .18, liver: P = .27, and kidney: P = .89), thus justifying its use as a continuous variable in Cox regression.21 

For waitlist survival analysis, the outcome measured was death on the waitlist. All patients were followed from the time of listing to death on the waitlist, transplant, or last known follow-up. For posttransplant survival, the outcome measured was death after transplant. Only patients who received a transplant were included in this portion of the analysis.

Risk factors included in this analysis were based on previously established risk factors known to predict waitlist survival, posttransplant survival, and time on the waitlist.5,8,13,14,22–29  Of note, patient race and ethnicity have been shown to be predictors of graft failure and patient survival, depending on the organ, often because of systemic and socioeconomic factors.30–32  Race and ethnicity categories collected by UNOS and included in this analysis were White, Black, Hispanic, Asian American, or other (eg Pacific Islander and American Indian or Alaska Native). Additional socioeconomic variables, such as payment method, UNOS region, and citizenship, were also included. Other risk factors included for all organs were era of listing, age, blood type, dialysis, height deficit, male sex, multiple listing, inactive status, and weight deficit. Height and weight deficits were based on Center for Disease Control growth charts, and SDs were calculated on the basis of published z scores. Other risk factors considered varied slightly by organ and can be found in Supplemental Table 6.

The percentage of all listed patients who received transplant within 3 months, 6 months, and 1 year was calculated for each era and organ. The χ2 test was used to compare each percentage to the 2011–2017 era. Patients who were listed within 3 months, 6 months, and 1 year of the end of the study period were excluded from these calculations, respectively, to eliminate bias caused by less follow-up time.

The total percentage of patients who received transplant and median time to transplant were also calculated for each era and organ. Notably, these 2 measures are biased in more recent eras because these patients did not have as much time to receive transplant as those listed previously. Additionally, a multivariable Cox regression model was used to identify factors that predicted remaining on the waitlist. Eras and listing year were not used as covariables in this model because of recent years having less follow-up time.

All available risk factors identified through literature review have been included. Data entry rates can be found in Supplemental Table 6. Predictive mean matching and logistic regressions were used to impute missing continuous and categorical variables, respectively. Further details regarding imputation methods can be found in the Supplemental Information. A complete-case sensitivity analysis was also performed without imputation (Supplemental Table 7).

The study population consisted of 47 202 first-time transplant candidates listed for heart (n = 12 984), liver (n = 17 519), or kidney (n = 16 699) transplant between 1987 and 2017. Intent-to-treat analysis included 65 512 years at risk for patients listed for heart transplant (6612 waitlist years and 58 900 posttransplant years), 113 821 years at risk for patients listed for liver transplant (14 462 waitlist years and 99 359 posttransplant years), and 114 694 years at risk for patients listed for kidney transplant (17 205 waitlist years and 97 489 posttransplant years). Mean follow-up was 5.0 years, 6.5 years, and 6.9 years for patients listed for heart, liver, and kidney transplant, respectively. Patient demographic and clinical characteristics are summarized in Table 1.

TABLE 1

Demographic and Clinical Characteristics for Patients Listed for Heart, Liver, or Kidney Transplant

Organ and Characteristic1987–19921993–19981999–20042005–20102011–2017
Heart      
 Listed candidates, n 1629 2523 2411 2643 3778 
 Age, y 5.0a 5.3a 5.7 5.3a 5.6 
 Male sex, % 57.5 57.4 55.4 55.4 55.9 
 Race and ethnicity, %      
  White 71.0a 68.2a 58.7a 53.8 52.4 
  Black 14.9a 16.8a 18.7 21.4 20.0 
  Hispanic 7.8a 11.3a 16.2a 18.2 19.9 
  Asian American 2.0a 2.3a 3.4 3.7 3.8 
  Other (eg, Pacific Islander and American Indian or Alaska Native) 4.4 1.5a 3.0 3.0 3.6 
 Height, cm 99.6a 101.2 104.4 99.4 101.6 
 Wt, kg 20.8a 22.3a 24.2 23.1 24.1 
 Diagnosis: congenital heart defect, % 39.5a 49.2 47.7 47.5a 50.1 
 Life support, % 37.3a 56.4a 63.4 62.1 61.7 
 Dialysis, % 3.9 1.3 1.5 2.3 1.9 
 Insurance, %      
  Private 62.2a 58.1a 55.5a 49.0a 43.6 
  Medicaid 12.2a 32.6a 36.2a 42.1a 46.7 
 Multiorgan transplant, % 6.6a 5.5a 4.0a 1.3a 0.2 
 Status 1, % 53.4a 68.4a 71.1a 78.8a 82.4 
Liver      
 Listed candidates, n 2082 3621 3856 3889 4071 
 Age, y 4.6 5.0 4.8 4.3a 4.7 
 Male sex, % 49.2 49.6 48.0 50.6 49.3 
 Race and ethnicity, %      
  White 64.5a 60.8a 55.0a 50.3 50.6 
  Black 16.4 17.9a 18.3a 17.0a 15.2 
  Hispanic 12.4a 15.1a 20.1a 24.2 23.7 
  Asian American 3.2a 3.6a 4.3a 5.0a 6.6 
  Other (eg, Pacific Islander and American Indian or Alaska Native) 3.5 2.6a 2.4a 3.4 3.9 
 Height, cm 97.5 100.2a 97.0 94.0a 98.1 
 Wt, kg 20.1a 22.7a 22.5 20.7a 21.9 
 Diagnosis, %      
  Biliary atresia 17.2a 26.9 25.1a 24.3a 28.4 
  Hepatoblastoma 0.8a 1.5a 2.4a 5.0a 7.3 
  Metabolic disorder 14.2 10.3a 8.5a 9.9a 14.4 
 Life support, % 18.4a 12.0a 10.9a 9.5a 6.5 
 Dialysis, % n/a n/a 1.5a 2.0 2.5 
 Insurance, %      
  Private 52.2a 53.6a 54.5a 45.2a 42.3 
  Medicaid 37.0a 34.5a 33.4a 42.7 44.1 
 Multiorgan transplant, % 0.7a 8.7 12.3a 16.5a 9.1 
 Status 1, % 19.6 20.9 23.7a 15.7a 20.1 
Kidney      
 Listed candidates, n 1750 2285 3019 4022 5623 
 Age, y 11.3a 11.3a 11.3a 11.2a 10.7 
 Male sex, % 57.0 58.1 57.7 57.5 66.3 
 Race and ethnicity, %      
  White 53.6a 48.0a 44.9 41.8a 45.2 
  Black 25.0a 27.5a 23.3a 22.5a 19.5 
  Hispanic 15.2a 18.5a 25.2a 30.2a 28.0 
  Asian American 4.1 3.6 3.7 3.1a 4.1 
  Other (eg, Pacific Islander and American Indian or Alaska Native) 2.2a 3.1 2.8 2.4a 3.3 
 Height, cm 136.5a 137.2a 136.4a 135.4a 133.3 
 Wt, kg 39.3 40.2 41.5a 42.3a 40.2 
 Diagnosis, %      
  Congenital obstructive uropathy 2.6a 7.0a 8.4a 7.8a 11.5 
  Focal segmental glomerulosclerosis 7.9a 11.1 13.1a 14.2a 11.6 
  Renal hypoplasia, dysplasia, dysgenesis, or agenesis 11.4a 9.1a 12.1a 10.0a 13.7 
 Dialysis, % 0.1a 1.5a 40.9a 62.7a 39.7 
 Insurance, %      
  Private 33.3 37.7 44.3a 40.5 39.7 
  Medicaid 16.7a 25.8a 28.4a 31.6a 34.3 
 Multiorgan transplant, % 0.2 0.3 0.3 0.4a 0.1 
Organ and Characteristic1987–19921993–19981999–20042005–20102011–2017
Heart      
 Listed candidates, n 1629 2523 2411 2643 3778 
 Age, y 5.0a 5.3a 5.7 5.3a 5.6 
 Male sex, % 57.5 57.4 55.4 55.4 55.9 
 Race and ethnicity, %      
  White 71.0a 68.2a 58.7a 53.8 52.4 
  Black 14.9a 16.8a 18.7 21.4 20.0 
  Hispanic 7.8a 11.3a 16.2a 18.2 19.9 
  Asian American 2.0a 2.3a 3.4 3.7 3.8 
  Other (eg, Pacific Islander and American Indian or Alaska Native) 4.4 1.5a 3.0 3.0 3.6 
 Height, cm 99.6a 101.2 104.4 99.4 101.6 
 Wt, kg 20.8a 22.3a 24.2 23.1 24.1 
 Diagnosis: congenital heart defect, % 39.5a 49.2 47.7 47.5a 50.1 
 Life support, % 37.3a 56.4a 63.4 62.1 61.7 
 Dialysis, % 3.9 1.3 1.5 2.3 1.9 
 Insurance, %      
  Private 62.2a 58.1a 55.5a 49.0a 43.6 
  Medicaid 12.2a 32.6a 36.2a 42.1a 46.7 
 Multiorgan transplant, % 6.6a 5.5a 4.0a 1.3a 0.2 
 Status 1, % 53.4a 68.4a 71.1a 78.8a 82.4 
Liver      
 Listed candidates, n 2082 3621 3856 3889 4071 
 Age, y 4.6 5.0 4.8 4.3a 4.7 
 Male sex, % 49.2 49.6 48.0 50.6 49.3 
 Race and ethnicity, %      
  White 64.5a 60.8a 55.0a 50.3 50.6 
  Black 16.4 17.9a 18.3a 17.0a 15.2 
  Hispanic 12.4a 15.1a 20.1a 24.2 23.7 
  Asian American 3.2a 3.6a 4.3a 5.0a 6.6 
  Other (eg, Pacific Islander and American Indian or Alaska Native) 3.5 2.6a 2.4a 3.4 3.9 
 Height, cm 97.5 100.2a 97.0 94.0a 98.1 
 Wt, kg 20.1a 22.7a 22.5 20.7a 21.9 
 Diagnosis, %      
  Biliary atresia 17.2a 26.9 25.1a 24.3a 28.4 
  Hepatoblastoma 0.8a 1.5a 2.4a 5.0a 7.3 
  Metabolic disorder 14.2 10.3a 8.5a 9.9a 14.4 
 Life support, % 18.4a 12.0a 10.9a 9.5a 6.5 
 Dialysis, % n/a n/a 1.5a 2.0 2.5 
 Insurance, %      
  Private 52.2a 53.6a 54.5a 45.2a 42.3 
  Medicaid 37.0a 34.5a 33.4a 42.7 44.1 
 Multiorgan transplant, % 0.7a 8.7 12.3a 16.5a 9.1 
 Status 1, % 19.6 20.9 23.7a 15.7a 20.1 
Kidney      
 Listed candidates, n 1750 2285 3019 4022 5623 
 Age, y 11.3a 11.3a 11.3a 11.2a 10.7 
 Male sex, % 57.0 58.1 57.7 57.5 66.3 
 Race and ethnicity, %      
  White 53.6a 48.0a 44.9 41.8a 45.2 
  Black 25.0a 27.5a 23.3a 22.5a 19.5 
  Hispanic 15.2a 18.5a 25.2a 30.2a 28.0 
  Asian American 4.1 3.6 3.7 3.1a 4.1 
  Other (eg, Pacific Islander and American Indian or Alaska Native) 2.2a 3.1 2.8 2.4a 3.3 
 Height, cm 136.5a 137.2a 136.4a 135.4a 133.3 
 Wt, kg 39.3 40.2 41.5a 42.3a 40.2 
 Diagnosis, %      
  Congenital obstructive uropathy 2.6a 7.0a 8.4a 7.8a 11.5 
  Focal segmental glomerulosclerosis 7.9a 11.1 13.1a 14.2a 11.6 
  Renal hypoplasia, dysplasia, dysgenesis, or agenesis 11.4a 9.1a 12.1a 10.0a 13.7 
 Dialysis, % 0.1a 1.5a 40.9a 62.7a 39.7 
 Insurance, %      
  Private 33.3 37.7 44.3a 40.5 39.7 
  Medicaid 16.7a 25.8a 28.4a 31.6a 34.3 
 Multiorgan transplant, % 0.2 0.3 0.3 0.4a 0.1 

n/a, not applicable.

a

Significance compared with the 2011–2017 era (P < .05).

View Large

For each organ, univariable and multivariable Cox regression was used to determine the effect of various risk factors on intent-to-treat survival. Table 2 lists the risk factors that were included in univariable analysis. Risk factors found to be significant in univariable analysis were included in multivariable analysis with the era of listing. The results of multivariable analysis are presented in Table 3. Among patients listed for heart transplant, risk of death was significantly increased in all eras before the reference and decreased each subsequent era. Among patient listed for liver transplant, risk was significantly increased in all eras before the reference and decreased each era after 1993–1998. Among patients listed for kidney transplant, risk was significantly increased in the 1987–1992, 1993–1998, and 1999–2004 eras relative to the reference and decreased each era after 1993–1998. A nonsignificant increase in risk was observed in the 2005–2010 era relative to the reference (hazard ratio [HR] 1.22, 95% confidence interval [CI] 0.99–1.51). Analysis with listing year as a continuous variable revealed similar results to multivariable analysis with eras included (Supplemental Table 8), and listing year was a significant predictor for all 3 organs (heart: HR 0.96, liver: HR 0.97, and kidney: HR 0.98; P < .001 for all organs).

TABLE 2

Univariable Cox Regression for Factors that Predict Death After Listing for Transplant

Organ and VariableHR (95% CI)
Heart  
 Age, ya 0.98 (0.98–0.99) 
 Male sex 0.97 (0.92–1.03) 
 Height deficit  
  1–2 SD 1.02 (0.94–1.11) 
  >2 SDa 1.22 (1.12–1.34) 
 Wt deficit  
  1–2 SD 1.02 (0.95–1.10) 
  >2 SDa 1.21 (1.12–1.31) 
 Diagnosis: congenital heart defecta 1.29 (1.22–1.35) 
 Dialysis 2.19 (1.83–2.63) 
 Status 1 1.28 (1.20–1.36) 
 Inactive 1.27 (1.07–1.51) 
 Life support 1.38 (1.30–1.45) 
 Insurancea  
  Private 0.93 (0.88–0.98) 
  Medicaid 1.08 (1.02–1.15) 
 US citizen 1.41 (1.17–1.71) 
 Race and ethnicitya  
  Black 1.30 (1.22–1.39) 
  Hispanic 0.84 (0.78–0.91) 
  Asian American 0.78 (0.66–0.93) 
  Other (eg, Pacific Islander and American Indian or Alaska Native) 1.35 (1.17–1.56) 
  White 0.91 (0.86–0.96) 
 Region  
  CT, ME, MA, NH, RI 0.92 (0.80–1.07) 
  DE, DC, MD, NJ, PA, N. VA, WVa 1.13 (1.04–1.22) 
  AL, AR, FL, GA, LA, MS, PR 1.02 (0.95–1.09) 
  OK, TX 0.94 (0.85–1.05) 
  AZ, CA, NV, NM, UT 0.95 (0.89–1.02) 
  AK, HI, ID, MT, OR, WAa 0.69 (0.56–0.86) 
  IL, MN, ND, SD, WI 1.00 (0.90–1.10) 
  CO, IA, KS, MO, NE, WYa 0.90 (0.83–0.98) 
  NY, VTa 0.85 (0.76–0.95) 
  IN, MI, OH 1.02 (0.93–1.12) 
  KY, NC, SC, TN, VAa 1.32 (1.21–1.43) 
 Blood type  
  Aa 0.87 (0.83–0.92) 
  B 0.96 (0.89–1.04) 
  AB 0.92 (0.80–1.05) 
  Oa 1.17 (1.11–1.23) 
 Heart-lung multiorgan listinga 2.28 (2.04–2.56) 
Liver  
 Age,a y 0.99 (0.99–1.00) 
 Male sexa 1.06 (1.00–1.13) 
 Height deficit  
  1–2 SD 1.02 (0.90–1.14) 
  >2 SDa 1.30 (1.18–1.42) 
 Wt deficita  
   1–2 SD 1.15 (1.02–1.28) 
   >2 SD 1.43 (1.29–1.59) 
 Diagnosis  
  Biliary atresiaa 0.40 (0.37–0.44) 
  Hepatoblastoma 0.98 (0.82–1.17) 
  Metabolic disordera 0.57 (0.50–0.64) 
 Dialysisa 1.85 (1.46–2.35) 
 Status 1a 2.01 (1.87–2.15) 
 Inactive 1.04 (0.87–1.25) 
 Life supporta 3.16 (2.92–3.42) 
 Insurancea  
  Private 0.79 (0.74–0.85) 
  Medicaid 1.30 (1.21–1.39) 
 US citizena 1.25 (1.06–1.47) 
 Race and ethnicity  
  Blacka 1.26 (1.16–1.36) 
  Hispanic 0.95 (0.88–1.03) 
  Asian Americana 0.79 (0.67–0.93) 
  Other (eg, Pacific Islander and American Indian or Alaska Native)a 1.31 (1.11–1.55) 
  Whitea 0.90 (0.85–0.96) 
 Region  
  CT, ME, MA, NH, RI 0.89 (0.75–1.05) 
  DE, DC, MD, NJ, PA, N. VA, WV 1.04 (0.96–1.13) 
  AL, AR, FL, GA, LA, MS, PRa 1.20 (1.09–1.32) 
  OK, TXa 1.17 (1.06–1.30) 
  AZ, CA, NV, NM, UTa 0.76 (0.70–0.83) 
  AK, HI, ID, MT, OR, WA 0.99 (0.79–1.24) 
  IL, MN, ND, SD, WI 0.92 (0.83–1.02) 
  CO, IA, KS, MO, NE, WYa 1.18 (1.07–1.31) 
  NY, VTa 0.75 (0.65–0.87) 
  IN, MI, OHa 1.14 (1.03–1.26) 
  KY, NC, SC, TN, VA 1.01 (0.87–1.17) 
 Blood type  
  Aa 0.92 (0.87–0.99) 
  B 0.99 (0.91–1.09) 
  AB 0.85 (0.72–1.01) 
  Oa 1.10 (1.03–1.17) 
 Bilirubin (mg/dL)a 1.03 (1.03–1.04) 
 INRa 1.02 (1.01–1.03) 
 Albumin (g/dL)a 0.68 (0.64–0.72) 
 Listing  
  Liver-intestine multiorgana 3.63 (3.30–3.98) 
  Liver-intestine-pancreas multiorgana 2.08 (1.84–2.35) 
  Liver-kidney multiorgan 1.11 (0.89–1.39) 
Kidney  
 Age, y 1.01 (1.00–1.02) 
 Male sexa 0.80 (0.73–0.87) 
 Height deficita  
  1–2 SD 0.88 (0.78–0.99) 
  >2 SD 1.52 (1.36–1.70) 
 Wt deficit  
  1–2 SD 1.05 (0.93–1.19) 
  >2 SDa 1.37 (1.22–1.55) 
 Diagnosis  
  Congenital obstructive uropathya 0.74 (0.60–0.91) 
  Focal segmental glomerulosclerosis 0.89 (0.76–1.04) 
  Renal hypoplasia, dysplasia, dysgenesis, or agenesisa 0.78 (0.66–0.92) 
 Dialysis 0.96 (0.86–1.07) 
 Inactivea 0.65 (0.56–0.76) 
 Insurance  
  Privatea 0.70 (0.63–0.78) 
  Medicaid 1.05 (0.93–1.18) 
 US citizena 2.04 (1.55–2.69) 
 Race and ethnicity  
  Blacka 1.65 (1.49–1.83) 
  Hispanica 0.71 (0.62–0.80) 
  Asian Americana 0.57 (0.42–0.78) 
  Other (eg, Pacific Islander and American Indian or Alaska Native) 1.07 (0.81–1.41) 
  Whitea 0.90 (0.82–0.99) 
 Region  
  CT, ME, MA, NH, RI 0.89 (0.69–1.15) 
  DE, DC, MD, NJ, PA, N. VA, WVa 0.75 (0.65–0.88) 
  AL, AR, FL, GA, LA, MS, PRa 1.44 (1.26–1.63) 
  OK, TXa 1.26 (1.08–1.46) 
  AZ, CA, NV, NM, UTa 0.78 (0.69–0.87) 
  AK, HI, ID, MT, OR, WA 0.77 (0.56–1.07) 
  IL, MN, ND, SD, WI 0.96 (0.80–1.13) 
  CO, IA, KS, MO, NE, WY 1.02 (0.83–1.25) 
  NY, VTa 0.79 (0.65–0.97) 
  IN, MI, OHa 1.22 (1.04–1.42) 
  KY, NC, SC, TN, VAa 1.26 (1.08–1.48) 
 Blood type  
  A 0.93 (0.84–1.03) 
  Ba 1.19 (1.05–1.36) 
  AB 1.13 (0.88–1.46) 
  O 0.96 (0.88–1.06) 
 Albumin (g/dL)a 0.70 (0.65–0.76) 
 Kidney-pancreas multiorgan listinga 9.79 (6.42–14.91) 
Organ and VariableHR (95% CI)
Heart  
 Age, ya 0.98 (0.98–0.99) 
 Male sex 0.97 (0.92–1.03) 
 Height deficit  
  1–2 SD 1.02 (0.94–1.11) 
  >2 SDa 1.22 (1.12–1.34) 
 Wt deficit  
  1–2 SD 1.02 (0.95–1.10) 
  >2 SDa 1.21 (1.12–1.31) 
 Diagnosis: congenital heart defecta 1.29 (1.22–1.35) 
 Dialysis 2.19 (1.83–2.63) 
 Status 1 1.28 (1.20–1.36) 
 Inactive 1.27 (1.07–1.51) 
 Life support 1.38 (1.30–1.45) 
 Insurancea  
  Private 0.93 (0.88–0.98) 
  Medicaid 1.08 (1.02–1.15) 
 US citizen 1.41 (1.17–1.71) 
 Race and ethnicitya  
  Black 1.30 (1.22–1.39) 
  Hispanic 0.84 (0.78–0.91) 
  Asian American 0.78 (0.66–0.93) 
  Other (eg, Pacific Islander and American Indian or Alaska Native) 1.35 (1.17–1.56) 
  White 0.91 (0.86–0.96) 
 Region  
  CT, ME, MA, NH, RI 0.92 (0.80–1.07) 
  DE, DC, MD, NJ, PA, N. VA, WVa 1.13 (1.04–1.22) 
  AL, AR, FL, GA, LA, MS, PR 1.02 (0.95–1.09) 
  OK, TX 0.94 (0.85–1.05) 
  AZ, CA, NV, NM, UT 0.95 (0.89–1.02) 
  AK, HI, ID, MT, OR, WAa 0.69 (0.56–0.86) 
  IL, MN, ND, SD, WI 1.00 (0.90–1.10) 
  CO, IA, KS, MO, NE, WYa 0.90 (0.83–0.98) 
  NY, VTa 0.85 (0.76–0.95) 
  IN, MI, OH 1.02 (0.93–1.12) 
  KY, NC, SC, TN, VAa 1.32 (1.21–1.43) 
 Blood type  
  Aa 0.87 (0.83–0.92) 
  B 0.96 (0.89–1.04) 
  AB 0.92 (0.80–1.05) 
  Oa 1.17 (1.11–1.23) 
 Heart-lung multiorgan listinga 2.28 (2.04–2.56) 
Liver  
 Age,a y 0.99 (0.99–1.00) 
 Male sexa 1.06 (1.00–1.13) 
 Height deficit  
  1–2 SD 1.02 (0.90–1.14) 
  >2 SDa 1.30 (1.18–1.42) 
 Wt deficita  
   1–2 SD 1.15 (1.02–1.28) 
   >2 SD 1.43 (1.29–1.59) 
 Diagnosis  
  Biliary atresiaa 0.40 (0.37–0.44) 
  Hepatoblastoma 0.98 (0.82–1.17) 
  Metabolic disordera 0.57 (0.50–0.64) 
 Dialysisa 1.85 (1.46–2.35) 
 Status 1a 2.01 (1.87–2.15) 
 Inactive 1.04 (0.87–1.25) 
 Life supporta 3.16 (2.92–3.42) 
 Insurancea  
  Private 0.79 (0.74–0.85) 
  Medicaid 1.30 (1.21–1.39) 
 US citizena 1.25 (1.06–1.47) 
 Race and ethnicity  
  Blacka 1.26 (1.16–1.36) 
  Hispanic 0.95 (0.88–1.03) 
  Asian Americana 0.79 (0.67–0.93) 
  Other (eg, Pacific Islander and American Indian or Alaska Native)a 1.31 (1.11–1.55) 
  Whitea 0.90 (0.85–0.96) 
 Region  
  CT, ME, MA, NH, RI 0.89 (0.75–1.05) 
  DE, DC, MD, NJ, PA, N. VA, WV 1.04 (0.96–1.13) 
  AL, AR, FL, GA, LA, MS, PRa 1.20 (1.09–1.32) 
  OK, TXa 1.17 (1.06–1.30) 
  AZ, CA, NV, NM, UTa 0.76 (0.70–0.83) 
  AK, HI, ID, MT, OR, WA 0.99 (0.79–1.24) 
  IL, MN, ND, SD, WI 0.92 (0.83–1.02) 
  CO, IA, KS, MO, NE, WYa 1.18 (1.07–1.31) 
  NY, VTa 0.75 (0.65–0.87) 
  IN, MI, OHa 1.14 (1.03–1.26) 
  KY, NC, SC, TN, VA 1.01 (0.87–1.17) 
 Blood type  
  Aa 0.92 (0.87–0.99) 
  B 0.99 (0.91–1.09) 
  AB 0.85 (0.72–1.01) 
  Oa 1.10 (1.03–1.17) 
 Bilirubin (mg/dL)a 1.03 (1.03–1.04) 
 INRa 1.02 (1.01–1.03) 
 Albumin (g/dL)a 0.68 (0.64–0.72) 
 Listing  
  Liver-intestine multiorgana 3.63 (3.30–3.98) 
  Liver-intestine-pancreas multiorgana 2.08 (1.84–2.35) 
  Liver-kidney multiorgan 1.11 (0.89–1.39) 
Kidney  
 Age, y 1.01 (1.00–1.02) 
 Male sexa 0.80 (0.73–0.87) 
 Height deficita  
  1–2 SD 0.88 (0.78–0.99) 
  >2 SD 1.52 (1.36–1.70) 
 Wt deficit  
  1–2 SD 1.05 (0.93–1.19) 
  >2 SDa 1.37 (1.22–1.55) 
 Diagnosis  
  Congenital obstructive uropathya 0.74 (0.60–0.91) 
  Focal segmental glomerulosclerosis 0.89 (0.76–1.04) 
  Renal hypoplasia, dysplasia, dysgenesis, or agenesisa 0.78 (0.66–0.92) 
 Dialysis 0.96 (0.86–1.07) 
 Inactivea 0.65 (0.56–0.76) 
 Insurance  
  Privatea 0.70 (0.63–0.78) 
  Medicaid 1.05 (0.93–1.18) 
 US citizena 2.04 (1.55–2.69) 
 Race and ethnicity  
  Blacka 1.65 (1.49–1.83) 
  Hispanica 0.71 (0.62–0.80) 
  Asian Americana 0.57 (0.42–0.78) 
  Other (eg, Pacific Islander and American Indian or Alaska Native) 1.07 (0.81–1.41) 
  Whitea 0.90 (0.82–0.99) 
 Region  
  CT, ME, MA, NH, RI 0.89 (0.69–1.15) 
  DE, DC, MD, NJ, PA, N. VA, WVa 0.75 (0.65–0.88) 
  AL, AR, FL, GA, LA, MS, PRa 1.44 (1.26–1.63) 
  OK, TXa 1.26 (1.08–1.46) 
  AZ, CA, NV, NM, UTa 0.78 (0.69–0.87) 
  AK, HI, ID, MT, OR, WA 0.77 (0.56–1.07) 
  IL, MN, ND, SD, WI 0.96 (0.80–1.13) 
  CO, IA, KS, MO, NE, WY 1.02 (0.83–1.25) 
  NY, VTa 0.79 (0.65–0.97) 
  IN, MI, OHa 1.22 (1.04–1.42) 
  KY, NC, SC, TN, VAa 1.26 (1.08–1.48) 
 Blood type  
  A 0.93 (0.84–1.03) 
  Ba 1.19 (1.05–1.36) 
  AB 1.13 (0.88–1.46) 
  O 0.96 (0.88–1.06) 
 Albumin (g/dL)a 0.70 (0.65–0.76) 
 Kidney-pancreas multiorgan listinga 9.79 (6.42–14.91) 

AK, Alaska; AL, Alabama; AR, Arkansas; AZ, Arizona; CA, California; CO, Colorado; CT, Connecticut; DC, District of Columbia; DE, Delaware; FL, Florida; GA, Georgia; HI, Hawaii; IA, Iowa; ID, Idaho; IL, Illinois; IN, Indiana; INR, international normalized ratio; KS, Kansas; KY, Kentucky; LA, Louisiana; MA, Massachusetts; MD, Maryland; ME, Maine; MI, Michigan; MN, Minnesota; MO, Missouri; MS, Mississippi; MT, Montana; NC, North Carolina; ND, North Dakota; NE, Nebraska; NH, New Hampshire; NJ, New Jersey; NM, New Mexico; NV, Nevada; N. VA, Northern Virginia; NY, New York; OH, Ohio; OK, Oklahoma; OR, Oregon; PA, Pennsylvania; PR, Puerto Rico; RI, Rhode Island; SC, South Carolina; SD, South Dakota; TN, Tennessee; TX, Texas; UT, Utah; VA, Virginia; VT, Vermont; WA, Washington; WI, Wisconsin; WV, West Virginia; WY, Wyoming.

a

Factors that are significant (P < .05).

View Large
TABLE 3

Multivariable Cox Regression for Factors That Predict Death After Listing for Transplant

Organ and VariableHR (95% CI)
Heart  
 Age, ya 0.99 (0.99–1.00) 
 Height deficit >2 SDa 1.17 (1.06–1.30) 
 Wt deficit >2 SD 1.08 (0.98–1.18) 
 Diagnosis: congenital heart defecta 1.34 (1.26–1.41) 
 Dialysisa 2.06 (1.72–2.47) 
 Status 1a 1.18 (1.09–1.27) 
 Inactive 1.16 (0.96–1.40) 
 Life supporta 1.39 (1.30–1.49) 
 Insurance  
  Private 0.95 (0.86–1.05) 
  Medicaid 1.03 (0.93–1.14) 
 US citizen 1.20 (0.98–1.47) 
 Race and ethnicity  
  Blacka 1.38 (1.28–1.48) 
  Hispanic 0.98 (0.90–1.07) 
  Asian American 0.99 (0.83–1.19) 
  Other (eg, Pacific Islander and American Indian or Alaska Native)a 1.52 (1.31–1.76) 
  White (reference) — 
 Region  
  DE, DC, MD, NJ, PA, N. VA, WV 1.05 (0.96–1.14) 
  AK, HI, ID, MT, OR, WA 0.87 (0.70–1.08) 
  CO, IA, KS, MO, NE, WYa 0.88 (0.80–0.96) 
  NY, VTa 0.86 (0.77–0.97) 
  KY, NC, SC, TN, VAa 1.21 (1.11–1.32) 
 Blood type  
  A 0.95 (0.88–1.03) 
  Oa 1.13 (1.05–1.22) 
 Heart-lung multiorgan listinga 2.26 (2.00–2.56) 
 Era  
  1987–1992a 2.51 (2.28–2.77) 
  1993–1998a 2.20 (2.02–2.41) 
  1999–2004a 1.84 (1.68–2.02) 
  2005–2010a 1.42 (1.30–1.56) 
  2011–2017 (reference) — 
Liver  
 Age, ya 0.99 (0.98–1.00) 
 Male sex 0.97 (0.91–1.04) 
 Height deficit >2 SD 1.03 (0.93–1.15) 
 Wt deficita  
  1–2 SD 1.20 (1.06–1.35) 
  >2 SD 1.36 (1.20–1.53) 
 Diagnosisa  
  Biliary atresia 0.50 (0.45–0.56) 
  Metabolic disorder 0.64 (0.56–0.73) 
 Dialysisa 1.91 (1.49–2.45) 
 Status 1a 1.42 (1.30–1.55) 
 Life supporta 2.06 (1.88–2.26) 
 Insurance  
  Privatea 0.90 (0.81–0.99) 
  Medicaid 1.08 (0.97–1.20) 
 US citizena 1.29 (1.09–1.53) 
 Race and ethnicity  
  Black 1.11 (1.00–1.24) 
  Asian American 1.18 (0.99–1.42) 
  Other (eg, Pacific Islander and American Indian or Alaska Native)a 1.42 (1.18–1.70) 
  White 1.06 (0.97–1.17) 
 Regiona  
  AL, AR, FL, GA, LA, MS, PR 1.15 (1.03–1.28) 
  OK, TX 1.38 (1.23–1.55) 
  AZ, CA, NV, NM, UT 0.82 (0.74–0.91) 
  CO, IA, KS, MO, NE, WY 0.76 (0.68–0.85) 
  NY, VT 0.79 (0.67–0.93) 
  IN, MI, OH 1.26 (1.12–1.41) 
 Blood type  
  A 0.99 (0.90–1.09) 
  Oa 1.10 (1.01–1.21) 
 Bilirubin (mg/dL)a 1.02 (1.02–1.03) 
 INR 1.01 (1.00–1.02) 
 Albumin (g/dL)a 0.76 (0.71–0.81) 
 Listinga  
  Liver-intestine multiorgan listing 2.72 (2.43–3.04) 
  Liver-intestine-pancreas multiorgan listing 2.09 (1.81–2.42) 
 Era  
  1987–1992a 2.19 (1.92–2.50) 
  1993–1998a 2.23 (1.98–2.51) 
  1999–2004a 1.76 (1.56–1.98) 
  2005–2010a 1.40 (1.24–1.58) 
  2011–2017 (reference) — 
Kidney  
 Male sexa 0.81 (0.74–0.89) 
 Height deficit  
  1–2 SD 1.05 (0.92–1.20) 
  >2 SDa 1.49 (1.29–1.72) 
 Wt deficit >2 SD 1.12 (0.97–1.30) 
 Diagnosisa  
  Congenital obstructive uropathy 0.76 (0.61–0.95) 
  Renal hypoplasia, dysplasia, dysgenesis, or agenesis 0.77 (0.65–0.91) 
 Inactive 0.96 (0.81–1.13) 
 Insurance: privatea 0.76 (0.67–0.85) 
 US citizena 1.73 (1.30–2.29) 
 Race and ethnicity  
  Black 1.33 (0.99–1.78) 
  Hispanic 0.77 (0.57–1.03) 
  Asian Americana 0.60 (0.40–0.90) 
  White 0.96 (0.72–1.28) 
 Region  
  DE, DC, MD, NJ, PA, N. VA, WVa 0.76 (0.63–0.92) 
  AL, AR, FL, GA, LA, MS, PR 1.15 (0.98–1.36) 
  OK, TXa 1.31 (1.09–1.57) 
  AZ, CA, NV, NM, UT 0.98 (0.83–1.15) 
  NY, VT 0.86 (0.69–1.07) 
  IN, MI, OH 1.19 (0.99–1.44) 
  KY, NC, SC, TN, VA 1.08 (0.89–1.31) 
 Blood type: B 1.13 (0.99–1.29) 
 Albumin (g/dL)a 0.74 (0.68–0.80) 
 Kidney-pancreas multiorgan listinga 9.06 (5.78–14.19) 
 Era  
  1987–1992a 1.88 (1.50–2.34) 
  1993–1998a 2.02 (1.62–2.51) 
  1999–2004a 1.80 (1.45–2.24) 
  2005–2010 1.22 (0.99–1.51) 
  2011–2017 (reference) — 
Organ and VariableHR (95% CI)
Heart  
 Age, ya 0.99 (0.99–1.00) 
 Height deficit >2 SDa 1.17 (1.06–1.30) 
 Wt deficit >2 SD 1.08 (0.98–1.18) 
 Diagnosis: congenital heart defecta 1.34 (1.26–1.41) 
 Dialysisa 2.06 (1.72–2.47) 
 Status 1a 1.18 (1.09–1.27) 
 Inactive 1.16 (0.96–1.40) 
 Life supporta 1.39 (1.30–1.49) 
 Insurance  
  Private 0.95 (0.86–1.05) 
  Medicaid 1.03 (0.93–1.14) 
 US citizen 1.20 (0.98–1.47) 
 Race and ethnicity  
  Blacka 1.38 (1.28–1.48) 
  Hispanic 0.98 (0.90–1.07) 
  Asian American 0.99 (0.83–1.19) 
  Other (eg, Pacific Islander and American Indian or Alaska Native)a 1.52 (1.31–1.76) 
  White (reference) — 
 Region  
  DE, DC, MD, NJ, PA, N. VA, WV 1.05 (0.96–1.14) 
  AK, HI, ID, MT, OR, WA 0.87 (0.70–1.08) 
  CO, IA, KS, MO, NE, WYa 0.88 (0.80–0.96) 
  NY, VTa 0.86 (0.77–0.97) 
  KY, NC, SC, TN, VAa 1.21 (1.11–1.32) 
 Blood type  
  A 0.95 (0.88–1.03) 
  Oa 1.13 (1.05–1.22) 
 Heart-lung multiorgan listinga 2.26 (2.00–2.56) 
 Era  
  1987–1992a 2.51 (2.28–2.77) 
  1993–1998a 2.20 (2.02–2.41) 
  1999–2004a 1.84 (1.68–2.02) 
  2005–2010a 1.42 (1.30–1.56) 
  2011–2017 (reference) — 
Liver  
 Age, ya 0.99 (0.98–1.00) 
 Male sex 0.97 (0.91–1.04) 
 Height deficit >2 SD 1.03 (0.93–1.15) 
 Wt deficita  
  1–2 SD 1.20 (1.06–1.35) 
  >2 SD 1.36 (1.20–1.53) 
 Diagnosisa  
  Biliary atresia 0.50 (0.45–0.56) 
  Metabolic disorder 0.64 (0.56–0.73) 
 Dialysisa 1.91 (1.49–2.45) 
 Status 1a 1.42 (1.30–1.55) 
 Life supporta 2.06 (1.88–2.26) 
 Insurance  
  Privatea 0.90 (0.81–0.99) 
  Medicaid 1.08 (0.97–1.20) 
 US citizena 1.29 (1.09–1.53) 
 Race and ethnicity  
  Black 1.11 (1.00–1.24) 
  Asian American 1.18 (0.99–1.42) 
  Other (eg, Pacific Islander and American Indian or Alaska Native)a 1.42 (1.18–1.70) 
  White 1.06 (0.97–1.17) 
 Regiona  
  AL, AR, FL, GA, LA, MS, PR 1.15 (1.03–1.28) 
  OK, TX 1.38 (1.23–1.55) 
  AZ, CA, NV, NM, UT 0.82 (0.74–0.91) 
  CO, IA, KS, MO, NE, WY 0.76 (0.68–0.85) 
  NY, VT 0.79 (0.67–0.93) 
  IN, MI, OH 1.26 (1.12–1.41) 
 Blood type  
  A 0.99 (0.90–1.09) 
  Oa 1.10 (1.01–1.21) 
 Bilirubin (mg/dL)a 1.02 (1.02–1.03) 
 INR 1.01 (1.00–1.02) 
 Albumin (g/dL)a 0.76 (0.71–0.81) 
 Listinga  
  Liver-intestine multiorgan listing 2.72 (2.43–3.04) 
  Liver-intestine-pancreas multiorgan listing 2.09 (1.81–2.42) 
 Era  
  1987–1992a 2.19 (1.92–2.50) 
  1993–1998a 2.23 (1.98–2.51) 
  1999–2004a 1.76 (1.56–1.98) 
  2005–2010a 1.40 (1.24–1.58) 
  2011–2017 (reference) — 
Kidney  
 Male sexa 0.81 (0.74–0.89) 
 Height deficit  
  1–2 SD 1.05 (0.92–1.20) 
  >2 SDa 1.49 (1.29–1.72) 
 Wt deficit >2 SD 1.12 (0.97–1.30) 
 Diagnosisa  
  Congenital obstructive uropathy 0.76 (0.61–0.95) 
  Renal hypoplasia, dysplasia, dysgenesis, or agenesis 0.77 (0.65–0.91) 
 Inactive 0.96 (0.81–1.13) 
 Insurance: privatea 0.76 (0.67–0.85) 
 US citizena 1.73 (1.30–2.29) 
 Race and ethnicity  
  Black 1.33 (0.99–1.78) 
  Hispanic 0.77 (0.57–1.03) 
  Asian Americana 0.60 (0.40–0.90) 
  White 0.96 (0.72–1.28) 
 Region  
  DE, DC, MD, NJ, PA, N. VA, WVa 0.76 (0.63–0.92) 
  AL, AR, FL, GA, LA, MS, PR 1.15 (0.98–1.36) 
  OK, TXa 1.31 (1.09–1.57) 
  AZ, CA, NV, NM, UT 0.98 (0.83–1.15) 
  NY, VT 0.86 (0.69–1.07) 
  IN, MI, OH 1.19 (0.99–1.44) 
  KY, NC, SC, TN, VA 1.08 (0.89–1.31) 
 Blood type: B 1.13 (0.99–1.29) 
 Albumin (g/dL)a 0.74 (0.68–0.80) 
 Kidney-pancreas multiorgan listinga 9.06 (5.78–14.19) 
 Era  
  1987–1992a 1.88 (1.50–2.34) 
  1993–1998a 2.02 (1.62–2.51) 
  1999–2004a 1.80 (1.45–2.24) 
  2005–2010 1.22 (0.99–1.51) 
  2011–2017 (reference) — 

AK, Alaska; AL, Alabama; AR, Arkansas; AZ, Arizona; CA, California; CO, Colorado; CT, Connecticut; DC, District of Columbia; DE, Delaware; FL, Florida; GA, Georgia; HI, Hawaii; IA, Iowa; ID, Idaho; IL, Illinois; IN, Indiana; INR, international normalized ratio; KS, Kansas; KY, Kentucky; LA, Louisiana; MA, Massachusetts; MD, Maryland; ME, Maine; MI, Michigan; MN, Minnesota; MO, Missouri; MS, Mississippi; MT, Montana; NC, North Carolina; ND, North Dakota; NE, Nebraska; NH, New Hampshire; NJ, New Jersey; NM, New Mexico; NV, Nevada; N. VA, Northern Virginia; NY, New York; OH, Ohio; OK, Oklahoma; OR, Oregon; PA, Pennsylvania; PR, Puerto Rico; RI, Rhode Island; SC, South Carolina; SD, South Dakota; TN, Tennessee; TX, Texas; UT, Utah; VA, Virginia; VT, Vermont; WA, Washington; WI, Wisconsin; WV, West Virginia; WY, Wyoming; —, reference variable.

a

Factors that are significant (P < .05).

View Large

The Kaplan-Meier and long-rank tests were used to analyze the effect of era on waitlist, posttransplant, and to intent-to-treat survival. In the heart and liver portions of the analysis, waitlist, posttransplant, and intent-to-treat survival were significantly reduced in all previous eras relative to 2011–2017 (P < .05). In the kidney portion of the analysis, waitlist, posttransplant, and intent-to-treat survival of patients listed from 1987 to 1992, 1993–1998, and 1999–2004, were also found to be significantly different compared with 2011–2017 (P < .05), except for waitlist survival from 1987 to 1992 (P = .11). However, survival of patients listed from 2005 to 2010 was not found to be significantly different from those listed from 2011 to 2017 in any of the 3 categories (P = .07, P = .26, and P = .05). The corresponding Kaplan-Meier curves can be seen in Figs 2–4.

FIGURE 2
Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for heart transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.
View largeDownload slide

Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for heart transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.

FIGURE 2
Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for heart transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.
View largeDownload slide

Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for heart transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.

Close modal
FIGURE 3
Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for liver transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.
View largeDownload slide

Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for liver transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.

FIGURE 3
Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for liver transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.
View largeDownload slide

Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for liver transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.

Close modal
FIGURE 4
Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for kidney transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.
View largeDownload slide

Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for kidney transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.

FIGURE 4
Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for kidney transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.
View largeDownload slide

Kaplan-Meier survival function over 10 years for patient survival in pediatric patients listed for kidney transplant. A, Intent-to-treat survival. B, Waitlist survival. C, Posttransplant survival.

Close modal

The χ2 test was used to compare the percentage of patients transplanted within 3 months, 6 months, and 1 year. The results can be seen in Table 4, along with median wait times among patients who received a transplant. For heart transplant, the percentage of patients transplanted within 3 months was lowest in the 2011–2017 era. The percentage of patients transplanted within 6 months was significantly greater in the 2011–2017 era than in the 1993–1998 and 1999–2004 eras. The percentage of patients who received heart transplant within 1 year was significantly greater in 2011–2017 than in all previous eras except 2005–2010. Median wait time steadily increased each era.

TABLE 4

Percentage of Patients Who Received a Transplant and Median Wait Time Among Those Who Received Transplant

OrganEraWithin 3 Mo, %Within 6 Mo, %Within 1 Y, %Total, %Median Wait Time (25th to 75th Percentile), d
Heart 1987–1992 54.0a 58.3 60.8a 62.4a 21 (7–54) 
 1993–1998 48.0 54.7a 59.0a 62.1a 37 (14–86) 
 1999–2004 46.9 55.5a 60.4a 64.1a 39 (14–101) 
 2005–2010 52.1a 62.8 68.7 71.9 42 (14–104) 
 2011–2017 46.8 61.0 68.7 69.9 59 (23–125) 
Liver 1987–1992 50.0 60.9 68.9 74.9 49 (11–133) 
 1993–1998 37.2a 49.6a 59.2a 67.8a 75 (16–202) 
 1999–2004 40.1a 52.0a 60.8a 68.6a 66 (14–179) 
 2005–2010 50.9 62.2 69.8 76.4 50 (13–135) 
 2011–2017 49.0 63.3 72.5 76.4 55 (15–142) 
Kidney 1987–1992 24.1a 40.5a 59.2 86.6a 206 (79–437) 
 1993–1998 26.2 42.0 60.4 91.2a 211 (76–474) 
 1999–2004 22.7a 37.5a 56.1a 91.5a 246 (94–538) 
 2005–2010 32.1a 49.3a 67.9a 91.9a 162 (59–382) 
 2011–2017 28.0 44.8 62.7 75.5 148 (55–332) 
OrganEraWithin 3 Mo, %Within 6 Mo, %Within 1 Y, %Total, %Median Wait Time (25th to 75th Percentile), d
Heart 1987–1992 54.0a 58.3 60.8a 62.4a 21 (7–54) 
 1993–1998 48.0 54.7a 59.0a 62.1a 37 (14–86) 
 1999–2004 46.9 55.5a 60.4a 64.1a 39 (14–101) 
 2005–2010 52.1a 62.8 68.7 71.9 42 (14–104) 
 2011–2017 46.8 61.0 68.7 69.9 59 (23–125) 
Liver 1987–1992 50.0 60.9 68.9 74.9 49 (11–133) 
 1993–1998 37.2a 49.6a 59.2a 67.8a 75 (16–202) 
 1999–2004 40.1a 52.0a 60.8a 68.6a 66 (14–179) 
 2005–2010 50.9 62.2 69.8 76.4 50 (13–135) 
 2011–2017 49.0 63.3 72.5 76.4 55 (15–142) 
Kidney 1987–1992 24.1a 40.5a 59.2 86.6a 206 (79–437) 
 1993–1998 26.2 42.0 60.4 91.2a 211 (76–474) 
 1999–2004 22.7a 37.5a 56.1a 91.5a 246 (94–538) 
 2005–2010 32.1a 49.3a 67.9a 91.9a 162 (59–382) 
 2011–2017 28.0 44.8 62.7 75.5 148 (55–332) 
a

Significance compared to the 2011–2017 era (P < .05).

View Large

For liver transplant, the percentage of listed patients transplanted at 3 months, 6 months, and 1 year was significantly higher in the 2011–2017 era compared with 1993–1998 and 1999–2004. No significant difference was found between 2011–2017 and 1987–1992 or 2005–2010. Median wait time did not consistently increase or decreases across eras.

For kidney transplant, the percentage of listed patients transplanted at 3 months, 6 months, and 1 year was significantly lower in the 2011–2017 era compared with 2005–2010 but significantly higher in the 2011–2017 era compared with 1999–2004. Median wait time steadily decreased after the 1999–2004 era.

The multivariable Cox model with transplant as an outcome over time (Table 5) showed that factors predictive of mortality, such as geographic region, diagnosis, multiorgan listing, and race and ethnicity, also predict remaining on the waitlist.

TABLE 5

Multivariable Cox Regression for Factors That Predict Remaining on the Transplant Waitlist

Organ and VariableHR (95% CI)
Heart  
 Age, ya 0.95 (0.95–0.96) 
 Diagnosis: congenital heart defecta 1.10 (1.03–1.17) 
 Dialysisa 2.23 (1.83–2.71) 
 Status 1 1.07 (0.97–1.18) 
 Inactivea 1.38 (1.12–1.69) 
 Life supporta 1.40 (1.30–1.52) 
 US citizena 1.27 (1.01–1.60) 
 Race and ethnicity  
  Asian American 1.00 (0.82–1.22) 
  Other (eg, Pacific Islander and American Indian or Alaska Native)a 1.42 (1.21–1.66) 
 Regiona  
  DE, DC, MD, NJ, PA, N. VA, WV 1.29 (1.18–1.42) 
  AK, HI, ID, MT, OR, WA 0.70 (0.54–0.90) 
  CO, IA, KS, MO, NE, WY 0.87 (0.79–0.96) 
  NY, VT 0.86 (0.75–0.99) 
  KY, NC, SC, TN, VA 1.31 (1.19–1.44) 
 Blood typea  
  A 0.82 (0.74–0.90) 
  AB 0.76 (0.63–0.92) 
  O 1.17 (1.06–1.29) 
 Heart-lung multiorgan listinga 2.62 (2.29–3.01) 
Liver  
 Age, ya 1.02 (1.01–1.02) 
 Male sex 1.00 (0.95–1.06) 
 Diagnosisa  
  Biliary atresia 0.57 (0.52–0.63) 
  Hepatoblastoma 0.58 (0.47–0.72) 
  Metabolic disorder 0.61 (0.54–0.69) 
 Dialysis 1.18 (0.94–1.48) 
 Status 1a 1.67 (1.54–1.80) 
 Life supporta 1.48 (1.35–1.62) 
 Race and ethnicity: Other (eg, Pacific Islander and American Indian or Alaska Native)a 1.37 (1.18–1.59) 
 Regiona  
  DE, DC, MD, NJ, PA, N. VA, WV 1.20 (1.12–1.30) 
  AL, AR, FL, GA, LA, MS, PR 0.74 (0.66–0.82) 
  CO, IA, KS, MO, NE, WY 0.63 (0.56–0.71) 
  IN, MI, OH 0.84 (0.75–0.94) 
 Blood type  
  Aa 0.85 (0.78–0.93) 
  ABa 0.69 (0.57–0.83) 
  O 1.02 (0.94–1.11) 
 Bilirubin (mg/dL) 1.00 (1.00–1.00) 
 INR 1.01 (0.99–1.02) 
 Albumin (g/dL) 0.96 (0.91–1.00) 
 Listing  
  Liver-intestine multiorgana 2.71 (2.42–3.03) 
  Liver-pancreas multiorgana 1.91 (1.66–2.19) 
  Liver-kidney multiorgan 1.05 (0.84–1.31) 
Kidney  
 Age, ya 1.02 (1.01–1.03) 
 Diagnosis: congenital obstructive uropathy 1.07 (0.93–1.23) 
 Dialysisa 0.66 (0.60–0.73) 
 Inactivea 2.08 (1.89–2.30) 
 Insurance  
  Privatea 0.79 (0.70–0.88) 
  Medicaid 0.98 (0.88–1.10) 
 Race and ethnicitya  
  White 0.86 (0.78–0.95) 
  Hispanic 0.86 (0.77–0.96) 
 Region  
  AL, AR, FL, GA, LA, MS, PRa 0.87 (0.75–1.00) 
  AZ, CA, NV, NM, UT 1.07 (0.97–1.19) 
  CO, IA, KS, MO, NE, WYa 0.82 (0.67–1.00) 
  NY, VTa 1.20 (1.04–1.40) 
  IN, MI, OHa 0.79 (0.66–0.95) 
 Kidney-pancreas multiorgan listinga 8.04 (5.22–12.39) 
Organ and VariableHR (95% CI)
Heart  
 Age, ya 0.95 (0.95–0.96) 
 Diagnosis: congenital heart defecta 1.10 (1.03–1.17) 
 Dialysisa 2.23 (1.83–2.71) 
 Status 1 1.07 (0.97–1.18) 
 Inactivea 1.38 (1.12–1.69) 
 Life supporta 1.40 (1.30–1.52) 
 US citizena 1.27 (1.01–1.60) 
 Race and ethnicity  
  Asian American 1.00 (0.82–1.22) 
  Other (eg, Pacific Islander and American Indian or Alaska Native)a 1.42 (1.21–1.66) 
 Regiona  
  DE, DC, MD, NJ, PA, N. VA, WV 1.29 (1.18–1.42) 
  AK, HI, ID, MT, OR, WA 0.70 (0.54–0.90) 
  CO, IA, KS, MO, NE, WY 0.87 (0.79–0.96) 
  NY, VT 0.86 (0.75–0.99) 
  KY, NC, SC, TN, VA 1.31 (1.19–1.44) 
 Blood typea  
  A 0.82 (0.74–0.90) 
  AB 0.76 (0.63–0.92) 
  O 1.17 (1.06–1.29) 
 Heart-lung multiorgan listinga 2.62 (2.29–3.01) 
Liver  
 Age, ya 1.02 (1.01–1.02) 
 Male sex 1.00 (0.95–1.06) 
 Diagnosisa  
  Biliary atresia 0.57 (0.52–0.63) 
  Hepatoblastoma 0.58 (0.47–0.72) 
  Metabolic disorder 0.61 (0.54–0.69) 
 Dialysis 1.18 (0.94–1.48) 
 Status 1a 1.67 (1.54–1.80) 
 Life supporta 1.48 (1.35–1.62) 
 Race and ethnicity: Other (eg, Pacific Islander and American Indian or Alaska Native)a 1.37 (1.18–1.59) 
 Regiona  
  DE, DC, MD, NJ, PA, N. VA, WV 1.20 (1.12–1.30) 
  AL, AR, FL, GA, LA, MS, PR 0.74 (0.66–0.82) 
  CO, IA, KS, MO, NE, WY 0.63 (0.56–0.71) 
  IN, MI, OH 0.84 (0.75–0.94) 
 Blood type  
  Aa 0.85 (0.78–0.93) 
  ABa 0.69 (0.57–0.83) 
  O 1.02 (0.94–1.11) 
 Bilirubin (mg/dL) 1.00 (1.00–1.00) 
 INR 1.01 (0.99–1.02) 
 Albumin (g/dL) 0.96 (0.91–1.00) 
 Listing  
  Liver-intestine multiorgana 2.71 (2.42–3.03) 
  Liver-pancreas multiorgana 1.91 (1.66–2.19) 
  Liver-kidney multiorgan 1.05 (0.84–1.31) 
Kidney  
 Age, ya 1.02 (1.01–1.03) 
 Diagnosis: congenital obstructive uropathy 1.07 (0.93–1.23) 
 Dialysisa 0.66 (0.60–0.73) 
 Inactivea 2.08 (1.89–2.30) 
 Insurance  
  Privatea 0.79 (0.70–0.88) 
  Medicaid 0.98 (0.88–1.10) 
 Race and ethnicitya  
  White 0.86 (0.78–0.95) 
  Hispanic 0.86 (0.77–0.96) 
 Region  
  AL, AR, FL, GA, LA, MS, PRa 0.87 (0.75–1.00) 
  AZ, CA, NV, NM, UT 1.07 (0.97–1.19) 
  CO, IA, KS, MO, NE, WYa 0.82 (0.67–1.00) 
  NY, VTa 1.20 (1.04–1.40) 
  IN, MI, OHa 0.79 (0.66–0.95) 
 Kidney-pancreas multiorgan listinga 8.04 (5.22–12.39) 

AK, Alaska; AL, Alabama; AR, Arkansas; AZ, Arizona; CA, California; CO, Colorado; CT, Connecticut; DC, District of Columbia; DE, Delaware; FL, Florida; GA, Georgia; HI, Hawaii; IA, Iowa; ID, Idaho; IL, Illinois; IN, Indiana; INR, international normalized ratio; KS, Kansas; KY, Kentucky; LA, Louisiana; MA, Massachusetts; MD, Maryland; ME, Maine; MI, Michigan; MN, Minnesota; MO, Missouri; MS, Mississippi; MT, Montana; NC, North Carolina; ND, North Dakota; NE, Nebraska; NH, New Hampshire; NJ, New Jersey; NM, New Mexico; NV, Nevada; N. VA, Northern Virginia; NY, New York; OH, Ohio; OK, Oklahoma; OR, Oregon; PA, Pennsylvania; PR, Puerto Rico; RI, Rhode Island; SC, South Carolina; SD, South Dakota; TN, Tennessee; TX, Texas; UT, Utah; VA, Virginia; VT, Vermont; WA, Washington; WI, Wisconsin; WV, West Virginia; WY, Wyoming.

a

Factors that are significant (P < .05).

View Large

This study contributes an additional frame of reference for assessing progress in pediatric transplantation. Waitlist and posttransplant survival are well studied in children listed for heart, liver, and kidney transplant and have demonstrated significant improvement over time.33–35  In intent-to-treat survival, we follow patients from listing to death regardless of transplant status and thus provide a more comprehensive perspective that allows us to further assess our progress.

Intent-to-treat survival of pediatric patients listed for heart transplant has seen dramatic improvements over the past 3 decades. These improvements can be attributed to improvement in survival among children on the waitlist for a heart allograft, which has led to an increase in the percentage of children who are able to receive a transplant. The steady increase in median wait time across eras observed among children who received heart transplant is likely a reflection of the fact that these children are increasingly becoming able to survive on the waitlist, thus increasing time available to receive a life-saving donor organ. Improved mechanical circulatory support of children with heart failure seen with increased usage of ventricular assist devices in the pediatric population has played a crucial role in improving these outcomes.36  Increasing center experience and knowledge gained on the impact of geographic factors and donor acceptance have likely also contributed.27,37,38  Steady increase in posttransplant survival also gave rise to improved intent-to-treat outcomes over the study period, in part because of improved surgical techniques, in conjunction with advancements in immunosuppressive therapy and treatment of graft rejection.39 

Similarly, intent-to-treat outcomes in pediatric patients listed for liver transplant have seen improvements. Increasing knowledge regarding the etiologies of liver failure in pediatric patients, combined with improved identification and management of concurrent multisystem organ dysfunction has increased waitlist survival and the number of children that live to receive a life-saving transplant.40–42  Posttransplant survival has also steadily increased with improved surgical techniques, use of living donors, treatment of graft rejection, and knowledge of patient characteristics that increase risk of adverse outcomes after transplant.28,43  Increase in the percentage of children listed for liver transplant who receive a donor organ has also contributed to this improvement.

Children listed for kidney transplant have enjoyed excellent outcomes relative to both their adult counterparts and children listed for other organs.44  The efficacy of dialysis as organ replacement therapy and decreased concern for donor-recipient size mismatch relative to other organs are major contributing factors.45  Future studies in which researchers look at changes in rates of preemptive kidney transplants, rates of life versus death from cardiac donors, and outcomes in status-7 patients may broaden generalizability in intent-to-treat outcomes for patients waitlisted for kidney transplant.23,24  As in heart and liver transplant, increased knowledge of disease etiology and experience in management of renal failure in pediatric patients combined with improved surgical techniques and immunosuppressive therapy contributed to this success.46 

Notably, allocation policy varies across time, center, and organ. Generally, the sickest patients are prioritized; however, specifics on prioritization vary by organ. For example, for liver, national allocation prioritization policy relies on a numerical score measuring severity of organ disease, whereas for heart and kidney transplants, allocation is prioritized by medical urgency.47–49  Furthermore, the decision to list a child for an organ transplant or to remove a child from the waitlist is complex and influenced by numerous clinical and social factors that care teams must consider.50  As such, the percentage of listed patients who received a transplant provides a limited picture of allograft availability and allocation efficacy. Our analysis of factors predicting transplant (Table 5) was consistent with the literature: however, a more in-depth analysis of those factors may provide a better perspective on allocation.13,51–53 

The scope of the study includes pediatric patients without a previous history of transplant. As such, the results of this study are not generalizable to children with a previous history of transplant at subsequent listings. Because allocation policy has varied across time and by organ, era cutoffs were selected arbitrarily rather than on the basis of organ-specific allocation practices. Although major world events, such as the 2009 influenza pandemic, may contribute to abnormalities in yearly patient morbidity, a test of nonproportional hazards revealed that epidemic spikes in mortality across years did not affect the proportionality of the relationship between listing year and survival.54 

Patients with multiple entries in the database (heart: 1142, liver: 2499, and kidney: 1807) and patients without recorded follow-up (heart: 32, liver: 62, and kidney: 13) were excluded from the study to reduce bias. Analysis was performed before and after exclusion of patients with multiple entries, and the findings were not significantly altered (Supplemental Table 9). Additionally, sensitivity analysis excluding patients lost to follow-up yielded no major difference in results (Supplemental Table 10). Although transplant rejection is an important metric, it had poor entry in the databases used for this study and, thus, was not an outcome of interest. Additional information regarding patients lost to follow-up and transplant rejection can be found in the Supplemental Information.

The spectrum of pediatric patients listed for solid organ transplant is vast and represents children with widely varying backgrounds. This study included 47 202 pediatric first-time transplant candidates listed for 3 different organs and spanned across 30 years. Intent-to-treat analysis of pediatric patients listed for heart, liver, and kidney transplant provides a more comprehensive perspective on changing outcomes in pediatric transplant and has shown improvements over the last 3 decades. Countless efforts have contributed to these improvements, but ample room for growth remains. This includes but is not limited to efforts to expand the donor pool, improve posttransplant outcomes, and reduce disparities in outcomes between geographic regions and socioeconomic groups to improve waitlist care. By doing this, we will be able to further capitalize on previous advancements and continue progress into the future.

Mr Hickner, Mr Anand, and Drs Goss and Rana assisted in conceptualization and design, analysis, interpretation of data, and drafting of the manuscript; Dr Godfrey, Mr Dunson, and Mr Reul assisted in interpretation of the data and drafting and critical revision of the manuscript; Drs Galvan, Cotton, and O’Mahony assisted in conceptualization and design of the study and critical revision of the manuscript; and all authors approved the final manuscript as submitted.

FUNDING: No external funding.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2021-054099.

CI

confidence interval

HR

hazard ratio

OPTN

Organ Procurement and Transplantation Network

SSADMF

Social Security Administration Death Master File

UNOS

United Network for Organ Sharing

1
Mendeloff
EN
.
The history of pediatric heart and lung transplantation
.
Pediatr Transplant
.
2002
;
6
(
4
):
270
–
279
Google Scholar
Crossref
Search ADS
PubMed
 
2
Cuenca
AG
,
Kim
HB
,
Vakili
K
.
Pediatric liver transplantation
.
Semin Pediatr Surg
.
2017
;
26
(
4
):
217
–
223
Google Scholar
Crossref
Search ADS
PubMed
 
3
Papalois
VE
,
Najarian
JS
.
Pediatric kidney transplantation: historic hallmarks and a personal perspective
.
Pediatr Transplant
.
2001
;
5
(
4
):
239
–
245
Google Scholar
Crossref
Search ADS
PubMed
 
4
Czyżewski
L
,
Sańko-Resmer
J
,
Wyzgał
J
,
Kurowski
A
.
Assessment of health-related quality of life of patients after kidney transplantation in comparison with hemodialysis and peritoneal dialysis
.
Ann Transplant
.
2014
;
19
(
1
):
576
–
585
Google Scholar
PubMed
 
5
Chin
C
,
Naftel
DC
,
Singh
TP
, et al;
Pediatric Heart Transplant Study Group
.
Risk factors for recurrent rejection in pediatric heart transplantation: a multicenter experience
.
J Heart Lung Transplant
.
2004
;
23
(
2
):
178
–
185
Google Scholar
Crossref
Search ADS
PubMed
 
6
Postalcioglu
M
,
Kaze
AD
,
Byun
BC
, et al.
Association of cold ischemia time with acute renal transplant rejection
.
Transplantation
.
2018
;
102
(
7
):
1188
–
1194
Google Scholar
Crossref
Search ADS
PubMed
 
7
Suhling
H
,
Rademacher
J
,
Zinowsky
I
, et al.
Conventional vs. tablet computer-based patient education following lung transplantation--a randomized controlled trial
.
PLoS One
.
2014
;
9
(
6
):
e90828
Google Scholar
PubMed
 
8
McDiarmid
SV
,
Anand
R
,
Lindblad
AS
;
Principal Investigators and Institutions of the Studies of Pediatric Liver Transplantation (SPLIT) Research Group
.
Development of a pediatric end-stage liver disease score to predict poor outcome in children awaiting liver transplantation
.
Transplantation
.
2002
;
74
(
2
):
173
–
181
Google Scholar
Crossref
Search ADS
PubMed
 
9
McDiarmid
SV
,
Merion
RM
,
Dykstra
DM
,
Harper
AM
.
Selection of pediatric candidates under the PELD system
.
Liver Transpl
.
2004
;
10
(
10
Suppl 2
):
S23
–
S30
Google Scholar
PubMed
 
10
Zafar
F
,
Castleberry
C
,
Khan
MS
, et al.
Pediatric heart transplant waiting list mortality in the era of ventricular assist devices
.
J Heart Lung Transplant
.
2015
;
34
(
1
):
82
–
88
Google Scholar
Crossref
Search ADS
PubMed
 
11
Weaver
DJ
Jr,
Somers
MJG
,
Martz
K
,
Mitsnefes
MM
.
Clinical outcomes and survival in pediatric patients initiating chronic dialysis: a report of the NAPRTCS registry
.
Pediatr Nephrol
.
2017
;
32
(
12
):
2319
–
2330
Google Scholar
Crossref
Search ADS
PubMed
 
12
OPTN/SRTR 2018 annual data report: preface
.
Am J Transplant
.
2020
;
20
(
Suppl 1
):
1
–
10
13
Joyce
DL
,
Lahr
BD
,
Joyce
LD
,
Kushwaha
SS
,
Daly
RC
.
Prediction model for wait times in cardiac transplantation
.
ASAIO J
.
2018
;
64
(
5
):
680
–
685
Google Scholar
Crossref
Search ADS
PubMed
 
14
Luo
X
,
Mogul
DB
,
Massie
AB
,
Ishaque
T
,
Bridges
JFP
,
Segev
DL
.
Predicting chance of liver transplantation for pediatric wait-list candidates
.
Pediatr Transplant
.
2019
;
23
(
7
):
e13542
Google Scholar
Crossref
Search ADS
PubMed
 
15
Schaffhausen
CR
,
Bruin
MJ
,
Chesley
D
, et al.
What patients and members of their support networks ask about transplant program data
.
Clin Transplant
.
2017
;
31
(
12
):
e13125
Google Scholar
Crossref
Search ADS
 
16
McCoy
CE
.
Understanding the intention-to-treat principle in randomized controlled trials
.
West J Emerg Med
.
2017
;
18
(
6
):
1075
–
1078
Google Scholar
Crossref
Search ADS
PubMed
 
17
US Department of Health and Human Services
.
Citing data
.
Available at: https://optn.transplant.hrsa.gov/data/citing-data/. Accessed June 2, 2021
18
Kwong
AJ
,
Flores
A
,
Saracino
G
, et al.
Center variation in intention-to-treat survival among patients listed for liver transplant
.
Liver Transpl
.
2020
;
26
(
12
):
1582
–
1593
Google Scholar
Crossref
Search ADS
PubMed
 
19
McDonald
SP
,
Craig
JC
;
Australian and New Zealand Paediatric Nephrology Association
.
Long-term survival of children with end-stage renal disease
.
N Engl J Med
.
2004
;
350
(
26
):
2654
–
2662
Google Scholar
Crossref
Search ADS
PubMed
 
20
Goel
MK
,
Khanna
P
,
Kishore
J
.
Understanding survival analysis: Kaplan-Meier estimate
.
Int J Ayurveda Res
.
2010
;
1
(
4
):
274
–
278
Google Scholar
PubMed
 
21
Heinzl
H
,
Kaider
A
.
Gaining more flexibility in Cox proportional hazards regression models with cubic spline functions
.
Comput Methods Programs Biomed
.
1997
;
54
(
3
):
201
–
208
Google Scholar
Crossref
Search ADS
PubMed
 
22
Almond
CS
,
Gauvreau
K
,
Canter
CE
,
Rajagopal
SK
,
Piercey
GE
,
Singh
TP
.
A risk-prediction model for in-hospital mortality after heart transplantation in US children
.
Am J Transplant
.
2012
;
12
(
5
):
1240
–
1248
Google Scholar
Crossref
Search ADS
PubMed
 
23
Kasiske
BL
,
Snyder
JJ
,
Matas
AJ
,
Ellison
MD
,
Gill
JS
,
Kausz
AT
.
Preemptive kidney transplantation: the advantage and the advantaged
.
J Am Soc Nephrol
.
2002
;
13
(
5
):
1358
–
1364
Google Scholar
Crossref
Search ADS
PubMed
 
24
Kataria
A
,
Gowda
M
,
Lamphron
BP
,
Jalal
K
,
Venuto
RC
,
Gundroo
AA
.
The impact of systematic review of status 7 patients on the kidney transplant waitlist
.
BMC Nephrol
.
2019
;
20
(
1
):
174
Google Scholar
Crossref
Search ADS
PubMed
 
25
Koh
LJ
,
Martz
K
,
Blydt-Hansen
TD
;
NAPRTCS Registry Investigators
.
Risk factors associated with allograft failure in pediatric kidney transplant recipients with focal segmental glomerulosclerosis
.
Pediatr Transplant
.
2019
;
23
(
5
):
e13469
Google Scholar
Crossref
Search ADS
PubMed
 
26
Norman
SP
,
Kommareddi
M
,
Luan
FL
.
Inactivity on the kidney transplant wait-list is associated with inferior pre- and post-transplant outcomes
.
Clin Transplant
.
2013
;
27
(
4
):
E435
–
E441
Google Scholar
Crossref
Search ADS
PubMed
 
27
Rana
A
,
Fraser
CD
,
Scully
BB
, et al.
Inferior outcomes on the waiting list in low-volume pediatric heart transplant centers
.
Am J Transplant
.
2017
;
17
(
6
):
1515
–
1524
Google Scholar
Crossref
Search ADS
PubMed
 
28
Rana
A
,
Pallister
ZS
,
Guiteau
JJ
, et al.
Survival outcomes following pediatric liver transplantation (Pedi-SOFT) score: a novel predictive index
.
Am J Transplant
.
2015
;
15
(
7
):
1855
–
1863
Google Scholar
Crossref
Search ADS
PubMed
 
29
Tancredi
DJ
,
Butani
L
.
Pretransplant serum albumin is an independent predictor of graft failure in pediatric renal transplant recipients
.
J Pediatr
.
2014
;
164
(
3
):
602
–
606
Google Scholar
Crossref
Search ADS
PubMed
 
30
Anand
A
,
Malik
TH
,
Dunson
J
, et al.
Factors associated with long-term graft survival in pediatric kidney transplant recipients
.
Pediatr Transplant
.
2021
;
25
(
4
):
e13999
Google Scholar
Crossref
Search ADS
PubMed
 
31
Kemmer
N
.
Ethnic disparities in liver transplantation
.
Gastroenterol Hepatol (N Y)
.
2011
;
7
(
5
):
302
–
307
Google Scholar
PubMed
 
32
Morris
AA
,
Kransdorf
EP
,
Coleman
BL
,
Colvin
M
.
Racial and ethnic disparities in outcomes after heart transplantation: a systematic review of contributing factors and future directions to close the outcomes gap
.
J Heart Lung Transplant
.
2016
;
35
(
8
):
953
–
961
Google Scholar
Crossref
Search ADS
PubMed
 
33
Dipchand
AI
.
Current state of pediatric cardiac transplantation
.
Ann Cardiothorac Surg
.
2018
;
7
(
1
):
31
–
55
Google Scholar
Crossref
Search ADS
PubMed
 
34
Capone
K
,
Amirikian
K
,
Azzam
RK
.
Pediatric liver transplantation: an update for the pediatrician
.
Pediatr Ann
.
2016
;
45
(
12
):
e439
–
e445
Google Scholar
Crossref
Search ADS
PubMed
 
35
Winterberg
PD
,
Garro
R
.
Long-term outcomes of kidney transplantation in children
.
Pediatr Clin North Am
.
2019
;
66
(
1
):
269
–
280
Google Scholar
Crossref
Search ADS
PubMed
 
36
Shugh
SB
,
Riggs
KW
,
Morales
DLS
.
Mechanical circulatory support in children: past, present and future
.
Transl Pediatr
.
2019
;
8
(
4
):
269
–
277
Google Scholar
Crossref
Search ADS
PubMed
 
37
Nichols
TJ
,
Price
MB
,
Villarreal
JA
, et al.
Most pediatric transplant centers are low volume, adult-focused, and in proximity to higher volume pediatric centers
.
J Pediatr Surg
.
2020
;
55
(
8
)
1667
–
1672
Google Scholar
Crossref
Search ADS
PubMed
 
38
Baez Hernandez
N
,
Kirk
R
,
Davies
R
, et al.
A comprehensive strategy in donor acceptance: impact on pediatric waitlist and heart transplant outcomes
.
Pediatr Transplant
.
2020
;
24
(
6
):
e13764
Google Scholar
Crossref
Search ADS
PubMed
 
39
Reinhardt
Z
.
Paediatric heart transplantation: an update
.
Arch Dis Child
.
2019
;
104
(
12
):
1216
–
1222
Google Scholar
Crossref
Search ADS
PubMed
 
40
Taylor
SA
,
Whitington
PF
.
Neonatal acute liver failure
.
Liver Transpl
.
2016
;
22
(
5
):
677
–
685
Google Scholar
Crossref
Search ADS
PubMed
 
41
Deep
A
,
Saxena
R
,
Jose
B
.
Acute kidney injury in children with chronic liver disease
.
Pediatr Nephrol
.
2019
;
34
(
1
):
45
–
59
Google Scholar
Crossref
Search ADS
PubMed
 
42
Thalji
L
,
Thalji
NM
,
Heimbach
JK
, et al.
Renal function parameters and serum sodium enhance prediction of waitlist outcomes in pediatric liver transplantation
.
Hepatology
.
2021
;
73
(
3
):
1117
–
1131
Google Scholar
Crossref
Search ADS
PubMed
 
43
Firl
DJ
,
Sasaki
K
,
McVey
J
, et al.
Improved survival following living donor liver transplantation for pediatric acute liver failure: analysis of 20 years of US National Registry data
.
Liver Transpl
.
2019
;
25
(
8
):
1241
–
1250
Google Scholar
PubMed
 
44
Verghese
PS
.
Pediatric kidney transplantation: a historical review
.
Pediatr Res
.
2017
;
81
(
1-2
):
259
–
264
Google Scholar
Crossref
Search ADS
PubMed
 
45
Amesty
MV
,
Fernandez
C
,
Espinosa
L
, et al.
Long-term outcomes of adult-size and size-matched kidney transplants in small pediatric recipients
.
J Pediatr Urol
.
2020
;
16
(
4
):
481.e1
–
481.e8
Google Scholar
Crossref
Search ADS
 
46
Hebert
SA
,
Swinford
RD
,
Hall
DR
,
Au
JK
,
Bynon
JS
.
Special considerations in pediatric kidney transplantation
.
Adv Chronic Kidney Dis
.
2017
;
24
(
6
):
398
–
404
Google Scholar
Crossref
Search ADS
PubMed
 
47
Chopra
B
,
Sureshkumar
KK
.
Changing organ allocation policy for kidney transplantation in the United States
.
World J Transplant
.
2015
;
5
(
2
):
38
–
43
Google Scholar
Crossref
Search ADS
PubMed
 
48
Estep
JD
,
Soltesz
E
,
Cogswell
R
.
The new heart transplant allocation system: early observations and mechanical circulatory support considerations
.
J Thorac Cardiovasc Surg
.
2021
;
161
(
5
):
1839
–
1846
Google Scholar
Crossref
Search ADS
 
49
Leventhal
TM
,
Florek
E
,
Chinnakotla
S
.
Changes in liver allocation in United States
.
Curr Opin Organ Transplant
.
2020
;
25
(
1
):
52
–
58
Google Scholar
Crossref
Search ADS
PubMed
 
50
Charpentier
KP
,
Mavanur
A
.
Removing patients from the liver transplant wait list: a survey of US liver transplant programs
.
Liver Transpl
.
2008
;
14
(
3
):
303
–
307
Google Scholar
Crossref
Search ADS
PubMed
 
51
Trieu
JA
,
Bilal
M
,
Hmoud
B
.
Factors associated with waiting time on the liver transplant list: an analysis of the United Network for Organ Sharing (UNOS) database
.
Ann Gastroenterol
.
2018
;
31
(
1
):
84
–
89
Google Scholar
PubMed
 
52
Schulze
PC
,
Kitada
S
,
Clerkin
K
,
Jin
Z
,
Mancini
DM
.
Regional differences in recipient waitlist time and pre- and post-transplant mortality after the 2006 United Network for Organ Sharing policy changes in the donor heart allocation algorithm
.
JACC Heart Fail
.
2014
;
2
(
2
):
166
–
177
Google Scholar
Crossref
Search ADS
PubMed
 
53
Hart
A
,
Salkowski
N
,
Snyder
JJ
,
Israni
AK
,
Kasiske
BL
.
Beyond “median waiting time”: development and validation of a competing risk model to predict outcomes on the kidney transplant waiting list
.
Transplantation
.
2016
;
100
(
7
):
1564
–
1570
Google Scholar
Crossref
Search ADS
PubMed
 
54
Kumar
D
,
Michaels
MG
,
Morris
MI
, et al;
American Society of Transplantation H1N1 Collaborative Study Group
.
Outcomes from pandemic influenza A H1N1 infection in recipients of solid-organ transplants: a multicentre cohort study
.
Lancet Infect Dis
.
2010
;
10
(
8
):
521
–
526
Google Scholar
Crossref
Search ADS
PubMed
 

Competing Interests

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

Copyright © 2022 by the American Academy of Pediatrics
2022