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J Thorac Cardiovasc Surg 2008;135:147-155
© 2008 The American Association for Thoracic Surgery

Cardiothoracic Transplantation

Predicting survival among high-risk pediatric cardiac transplant recipients: An analysis of the United Network for Organ Sharing database

^a Division of Cardiothoracic Surgery, Department of Surgery, Children’s Hospital of New York–Presbyterian and Columbia University College of Physicians and Surgeons, New York, NY
^b International Center for Health Outcomes and Innovation Research, Department of Surgery, Children’s Hospital of New York–Presbyterian and Columbia University College of Physicians and Surgeons, New York, NY
^c Division of Pediatric Cardiology, Hospital for Sick Children, Toronto, Ontario, Canada
^d Department of Cardiothoracic Surgery, Weill Medical College of Cornell University, New York, NY.

Read at the Thirty-third Annual Meeting of the Western Thoracic Surgical Association, Santa Ana Pueblo, NM, June 27–30, 2007.

Received for publication July 3, 2007; revisions received August 21, 2007; accepted for publication September 6, 2007.

^_* Address for reprints: Jonathan M. Chen, MD, Pediatric Cardiac Surgery, Children’s Hospital of New York, 3959 Broadway, Suite 2-273, New York, NY 10032. (Email: jmc23{at}columbia.edu).

	Abstract

Top Abstract Introduction Methods Results Discussion Conclusions Appendix E1 References

 
Objective: Studies of high-risk pediatric cardiac transplant recipients are lacking. The purpose of this study is to evaluate early posttransplant survival in high-risk pediatric patients.

Methods: The United Network for Organ Sharing (UNOS) provided de-identified patient-level data. The study population included 3502 recipients aged less than 21 years who underwent transplantation from January 1, 1995, through December 31, 2005. Recipients were stratified on the basis of the presence or absence of high-risk criteria: pulmonary vascular resistance index greater than 6 Wood units/m² (n = 285, 8.1%), creatinine clearance less than 40 mL/min (308, 8.8%), hepatitis C positivity (33, 0.9%), donor/recipient weight ratio less than 0.7 (80, 2.3%), panel reactive antibody greater than 40% (235, 6.7%), retransplantation (235, 6.7%), and age less than 1 year old (840, 24.0%).

Results: Overall, 1575 (45.0%) patients met at least one high-risk criterion. Higher numbers of high-risk criteria in a patient were correlated with increased 30-day mortality (0 high-risk criteria: 5.2%; 1 criterion: 7.9%; 2 criteria: 12.9%; and 3 or more criteria: 25.0%; P < .0001) and poor long-term survival (P < .0001). Among patients with high-risk criteria, a simplified scoring scale accurately predicts both 30-day and contingent 1-year mortality (P < .0001).

Conclusions: Individually, the effect of high-risk criteria on posttransplant survival varied; however, increasing numbers of criteria in a patient resulted in a cumulative increase in mortality. A scoring scale allows for the prediction of approximate mortality rates after transplantation. These findings suggest that recipient criteria for transplantation should focus on the number of high-risk criteria as well as clinical status, rather than the presence or absence of a single risk factor.

	Introduction

Top Abstract Introduction Methods Results Discussion Conclusions Appendix E1 References

 
Optimal allocation of the limited—and decreasing¹—supply of pediatric donor hearts requires accurate pretransplant assessment of survival and data-derived selection of criteria indicating high risk. Unfortunately, criteria for transplant eligibility remain largely the result of consensus opinion.

In the adult population, continued improvements in posttransplant outcomes (as well as the ongoing shortage of donor organs) have prompted attempts to expand the pools of both donors and recipients. Results from the use of such "alternate-list" strategies have been variable. Some authors have reported results equivalent to those in standard criteria receipients,² whereas others have had less success.³ In some cases, specific traditional high-risk criteria (HRC) have failed to consistently predict poor posttransplant outcome (older age,^4-6 recipient hepatitis C positivity⁷), whereas others clearly increase posttransplant mortality (elevated pulmonary vascular resistance [PVR]⁸).

The applicability of any of these data to pediatric recipients is unclear. Although less well studied in children, some results suggest that pediatric HRC recipients have acceptable outcomes and that our criteria remain too strict.^9,10 Evidence-based estimates of posttransplant survival in a pediatric population would allow for better stratification of potential recipients and optimization of transplant selection criteria.

This report uses data from the United Network for Organ Sharing (UNOS) database to assess posttransplant outcomes in patients meeting traditional HRC. Our goals were (1) to estimate the impact of HRC on posttransplant outcomes and (2) to develop a simplified scoring system to predict short-term posttransplant survival.

	Methods

Top Abstract Introduction Methods Results Discussion Conclusions Appendix E1 References

 
Data Collection
UNOS provided de-identified patient level data from the Thoracic Registry (data source #021606-4). Use of these data is consistent with the regulations of our university’s institutional review board. Records with incomplete data were excluded from analyses requiring those data points.

Study Population
The study population consists of 3502 transplants performed on patients less than 21 years of age between January 1, 1995, and December 31, 2005. Recipients were stratified on the basis of the presence or absence of traditional HRC: PVR index greater than 6 Wood units/m² (n = 285, 8.1%), creatinine clearance (CrCl) less than 40 mL/min (308, 8.8%), hepatitis C positivity (33, 0.9%), donor/recipient weight ratio less than 0.7 (80, 2.3%), panel reactive antibody greater than 40% (235, 6.7%), retransplantation (235, 6.7%), and age less than 1 year old (840, 24.0%). These criteria were established before data analysis and are based on internal criteria for transplantation at our institution, as well as a review of published reports.¹¹ Congenital heart disease (CHD) was not considered as one of the HRC because previous work had suggested that it is complex rather than any CHD which indicated elevated risk,⁹ and the UNOS database does not contain detailed data regarding the CHD diagnosis that would enable such stratification. In addition, patients were grouped by the number of HRC present: one (n = 1189, 34.0%), two (n = 334, 9.5%), and three or more (n = 52, 1.5%) Overall, 1575 (45.0%) patients met at least one HRC.

Data Analysis
Data were analyzed by SAS 9.13 for Windows software (SAS Institute, Inc, Cary, NC). The primary outcomes were 30-day and contingent (on 30-day survival) 1-year mortality; other outcomes included long-term survival (time to death) and in-hospital complications. All variables analyzed are available in Table E1; only significant variables are reported. Continuous variables are reported as means ± standard deviation and were compared by the Student t test (with Bonferroni correction). Ordinal variables were compared by the ² test. All P values are 2-sided. Multivariate regression (stepwise, P < .05) was also performed. Kaplan–Meier analysis and Cox proportional hazards regression (stepwise, P < .05) were used for time-to-event analysis; patients without accurate follow-up times were excluded from these analyses. Risk, odds (OR), and hazard ratios are reported with 95% confidence intervals (95% CI) in parentheses. Simplified predictive scores were developed for both 30-day and contingent 1-year mortality. Data from both multivariate and univariate analysis were used to assign points on the basis of the presence of specific comorbidities. The predictive value of the scores was assessed by the ² test and logistic regression.

	Results

Top Abstract Introduction Methods Results Discussion Conclusions Appendix E1 References

Multivariate predictors of the need for postoperative dialysis in HRC patients included preoperative CrCl less than 40 mL/min (OR 4.03, 95% CI 2.54–6.42), redo transplantation (OR 2.32, 95% CI 1.28–4.18), and the need for mechanical ventilation (OR 2.54, 95% CI 1.57–4.09); age less than 1 year was protective (OR 0.56, 95% CI 0.34–0.94). Infections occurred with greater frequency in patients with poor pretransplant clinical status as indicated by CrCl less than 40 mL/min (OR 1.44, 95% CI 1.02–2.03), the need for mechanical ventilation (OR 2.47, 95% CI 1.74–3.50), intensive care support (OR 1.59, 95% CI 1.11–2.26), as well as in younger patients between 2 and 5 years old (OR 1.55, 95% CI 1.06–2.26) and those with CHD (OR 1.38, 95% CI 1.03–1.86).

Impact of Multiple HRC
An increasing number of HRC present in a single patient resulted in cumulative higher mortality, particularly within the first year (Figure 1). Cox regression analysis of patients within the HRC group demonstrated that the need for extracorporeal membrane oxygenation (ECMO) (hazard ratio 2.26, 95% CI 1.60–3.18), CrCl less than 40 mL/min (hazard ratio 1.73, 95% CI 1.37–2.20), the presence of three or more HRC (hazard ratio 2.05, 95% CI 1.33–3.17), and age between 13 and 18 years old (hazard ratio 1.59, 95% CI 1.18–2.14) were all associated with poor long-term survival.

View larger version (14K): [in this window] [in a new window]   Figure 1. Five-year Kaplan–Meier survival estimates are illustrated as a function of the presence and number of high-risk criteria (HRC) present at the time of transplantation. (P < .0001, Wilcoxon test.)

View larger version (8K): [in this window] [in a new window]   Figure E1. Receiver operating curves are shown for both 30-day (A) and overall 1-year survival (B) based on the scoring scale. In both cases, as demonstrated by the c score, the scoring scale adequately predicts survival.

View larger version (34K): [in this window] [in a new window]   Figure 2. Observed 30-day (black) and 1-year (slanted brick pattern) mortality for each risk score in patients with at least one HRC (P < 0.0001).

View larger version (14K): [in this window] [in a new window]   Figure 3. Thirty-day (black) and 1-year (slanted brick pattern) mortality stratified based on risk category (low = score 0-5, moderate = 6–10, high = 11–15). P < .0001.

	Discussion

Top Abstract Introduction Methods Results Discussion Conclusions Appendix E1 References

In adults, alternate-list strategies have resulted in acceptable outcomes with suboptimal donor organs, including similar rates of primary graft dysfunction¹² and only slightly decreased survival¹³ compared with standard recipient/donor pairs. Expansion of such policies to the pediatric population may result in a more optimal distribution of organs. The present study analyzes a set of traditional HRC to examine their impact on early mortality after cardiac transplantation in a pediatric population and to develop a more accurate pretransplant assignment of patients to posttransplant risk groups.

Are HRC Truly High Risk?
We found that several criteria traditionally used to identify "high-risk" patients had poor correlation with early transplant mortality. Elevated PVR index was not associated with decreased survival. This is consonant with other studies that have demonstrated the impact of elevated PVR on right ventricular dysfunction, but not on survival.^14,15 It is likely that current management of right-sided heart dysfunction, including nitric oxide, has largely mitigated its impact on survival.^14-17 Although this analysis demonstrates a survival advantage to patients with elevated PVR index as well as those undergoing retransplantation, it should be emphasized that this is true in comparison with the high-risk patients and likely reflects a survival in these populations equivalent to the standard risk patients.

Similar negative results were obtained with several other measures of "high-risk" transplant recipients, although the evaluation of hepatitis C positivity and low donor/recipient weight ratio as risk factors is constrained by the small sample sizes in those populations.

Pretransplant renal failure (CrCl less than 40 mL/min) was the only risk factor consistently predictive of poor postoperative survival; the outcomes of patients requiring dialysis (an 8-fold increase in 1-year mortality) reinforce the relationship between renal function and survival. In young patients without intrinsic kidney disease, renal failure may simply act as a surrogate of poorly compensated heart failure. Therefore, these patients should benefit from early mechanical circulatory support and optimization of systemic perfusion.

Because of the usual lack of intrinsic renal disease and the possibility of return of renal function, some have argued that simultaneous renal transplants should be avoided in children.¹⁷ However, combined transplants have acceptable outcomes in adults and appear to decrease the risk of allograft rejection.^18,19 Furthermore, simultaneous heart/kidney transplantation may confer a survival advantage over heart transplant alone in patients with dialysis-dependent renal failure.²⁰ Sample sizes are limited, but a brief review of 24 simultaneous pediatric heart/kidney transplantations reported in the UNOS data set suggests that they have similar long-term outcomes with 1-year survival of 85% (unpublished data); this is supported by case reports providing anecdotal evidence of good long-term outcomes after simultaneous renal transplantation.^21,22 Further analysis of simultaneous transplants and other therapies for pediatric patients with pretransplant renal dysfunction is warranted.¹⁷

As noted earlier, we did not include CHD among our HRC because of the perceived limitations in the UNOS database with regard to congenital diagnoses. Despite these limitations, CHD did predict poorer outcomes in this population. Single-institution data suggests that complex CHD accounts for most of this additional risk.¹⁰ Further studies will be needed in data sets including stratification of congenital diagnoses to accurately elucidate the impact of CHD on posttransplant outcomes.

Impact of Multiple HRC
Although renal failure was the only one of the traditional HRC to be a significant predictor of early mortality, the accumulation of HRC in a single patient resulted in a significant decrement in early survival. This phenomenon has not previously been described and suggests that criteria for transplantation should include the number of HRC rather than simply the presence or absence of a single risk factor.

Clinical Status Before Transplant
In this analysis, as in previous reports, indicators of pretransplant clinical status remain the most significant predictors of poor outcomes. Consistent with previous reports,^10,23 the need for ECMO was a strong negative predictor of both 30-day and contingent 1-year mortality. The need for hospitalization, especially in the intensive care unit and with mechanical ventilation, was associated with poor outcomes. This (particularly given the negative effects of preoperative or postoperative renal failure) reinforces the need to attempt transplantation before patients becoming critically ill and—where not possible—to attempt aggressive resuscitation with mechanical ventricular support (rather than ECMO) to improve end-organ function, wean patients from the ventilator, and optimize their clinical condition.

Screening Score for HRC Patients
The development of a simplified screening score for pediatric patients meeting traditional HRC should provide important prognostic information to physicians, patients, and families as they contemplate transplantation. The scoring system presented here divides patients into three risk groups, with an approximate doubling of mortality between groups: those with scores of 0 to 4 are at low risk (1-year mortality 15%), those with scores from 5 to 9 are at moderate risk (1-year mortality 30%), and those with scores of 10 or more are at high risk (1-year mortality 62%). Although this score applies specifically to patients in the traditional HRC group, further evaluation may enable its applicability to be broadened to a larger cross-section of patients.

Validation of the score may enable its use in accurately stratifying patients into standard and alternate-list recipients. Finally, clinical therapies may be directed at correcting conditions known to predispose to poor outcomes (mechanical ventilation, renal failure, use of ECMO) before transplantation.

Limitations
These data have several limitations. First, there are problems inherent to the UNOS data set: incomplete data entry and variability between reporting centers as to which pretransplant hemodynamic and clinical variables are reported. Second, although the data analysis supports associations between variables and outcomes, causal relationships cannot be determined. Many of the risk factors may simply be markers for poor clinical status before transplantation rather than a direct causal factor in poor survival. Finally, the scoring system we describe here remains to be validated, and further study is required to confirm its accuracy in predicting early posttransplant outcomes. In addition, it applies specifically to patients within the HRC group and its applicability to standard risk recipients remains to be determined.

	Conclusions

Top Abstract Introduction Methods Results Discussion Conclusions Appendix E1 References

 
In summary, we have reviewed the UNOS thoracic organ transplant registry and examined the outcomes of patients historically considered at high risk for early transplant mortality. We have found that several of these HRC do not predict poor outcomes but that the cumulative effect of multiple criteria in a single patient results in poor survival. Finally, we have described a simplified screening score to identify the patients within the HRC population most and least likely to survive transplantation. This forms an initial attempt to accurate stratify patients before transplantation. Further efforts are required to optimally allocate the limited supply of donor organs.

	Appendix E1

Top Abstract Introduction Methods Results Discussion Conclusions Appendix E1 References

 
All significant multivariate predictors of 30-day mortality were included in the score. Significant univariate predictors were also included when certain criteria were met: (1) they were highly predictive of mortality but the variable was not coded in a sufficient number of patients to meet the threshold P value for entry into the multivariate model and (2) inclusion in the scoring scale increased the predictive value of the scale (measured by receiver operating curves, c-score, and ² tests. The score assigned to each risk factor (given in Table 4) was based on the OR for 30-day and 1-year mortality and was adjusted to maximize the predictive value of the scoring scale at both time periods.

	Acknowledgments

 
We thank UNOS for supplying these data and Katarina Anderson for her assistance with our analysis.

	Footnotes

 
This work was funded in part by the Health Resources and Services Administration contract 231-00-0115 and departmental funding sources.

The content of this article is the responsibility of the authors alone and does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government.

	References

Top Abstract Introduction Methods Results Discussion Conclusions Appendix E1 References

 

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Personal Folders Download to citation manager Author home page(s): Ryan R. Davies Mark J. Russo Timothy M. Martens Annetine C. Gelijns Alan J. Moskowitz Jan M. Quaegebeur Ralph S. Mosca Jonathan M. Chen Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Davies, R. R. Articles by Chen, J. M. Search for Related Content PubMed Articles by Davies, R. R. Articles by Chen, J. M. Related Collections Congenital - cyanotic Transplantation - heart Related Article