Predicting graft loss by 1 year in pediatric heart transplantation candidates: an analysis of the Pediatric Heart Transplant Study database.

BACKGROUND Pediatric data on the impact of pre-heart transplantation (HTx) risk factors on early post-HTx outcomes remain inconclusive. Thus, among patients with previous congenital heart disease or cardiomyopathy, disease-specific risk models for graft loss were developed with the use pre-HTx recipient and donor characteristics. METHODS AND RESULTS Patients enrolled in the Pediatric Heart Transplant Study (PHTS) from 1996 to 2006 were stratified by pre-HTx diagnosis into cardiomyopathy and congenital heart disease cohorts. Logistic regression identified independent, pre-HTx risk factors. Risk models were constructed for 1-year post-HTx graft loss. Donor factors were added for model refinement. The models were validated with the use of patients transplanted from 2007 to 2009. Risk factors for graft loss were identified in patients with cardiomyopathy (n=896) and congenital heart disease (n=965). For cardiomyopathy, independent risk factors were earlier year of transplantation, nonwhite race, female sex, diagnosis other than dilated cardiomyopathy, higher blood urea nitrogen, and panel reactive antibody >10%. The recipient characteristic risk model had good accuracy in the validation cohort, with predicted versus actual survival of 97.5% versus 95.3% (C statistic, 0.73). For patients with congenital heart disease, independent risk factors were nonwhite race, history of Fontan, ventilator dependence, higher blood urea nitrogen, panel reactive antibody >10%, and lower body surface area. The risk model was less accurate, with 86.6% predicted versus 92.4% actual survival, in the validation cohort (C statistic, 0.63). Donor characteristics did not enhance model precision. CONCLUSIONS Risk factors for 1-year post-HTx graft loss differ on the basis of pre-HTx cardiac diagnosis. Modeling effectively stratifies the risk of graft loss in patients with cardiomyopathy and may be an adjunctive tool in allocation policies and center performance metrics.