A risk-prediction model for in-hospital mortality after heart transplantation in US children

We sought to develop and validate a quantitative risk-prediction model for predicting the risk of posttransplant in-hospital mortality in pediatric heart transplantation (HT). Children <18 years of age who underwent primary HT in the United States during 1999-2008 (n = 2707) were identified using Organ Procurement and Transplant Network data. A risk-prediction model was developed using two-thirds of the cohort (random sample), internally validated in the remaining one-third, and independently validated in a cohort of 338 children transplanted during 2009-2010. The best predictive model had four categorical variables: hemodynamic support (ECMO, ventilator support, VAD support vs. medical therapy), cardiac diagnosis (repaired congenital heart disease [CHD], unrepaired CHD vs. cardiomyopathy), renal dysfunction (severe, mild-moderate vs. normal) and total bilirubin (≥ 2.0, 0.6 to <2.0 vs. <0.6 mg/dL). The C-statistic (0.78) and the Hosmer-Lemeshow goodness-of-fit (p = 0.89) in the model-development cohort were replicated in the internal validation and independent validation cohorts (C-statistic 0.75, 0.81 and the Hosmer-Lemeshow goodness-of-fit p = 0.49, 0.53, respectively) suggesting acceptable prediction for posttransplant in-hospital mortality. We conclude that this risk-prediction model using four factors at the time of transplant has good prediction characteristics for posttransplant in-hospital mortality in children and may be useful to guide decision-making around patient listing for transplant and timing of mechanical support. This article describes a model to predict the risk of in-hospital mortality in children undergoing heart transplant in the US, which the authors validated both internally in their development cohort and externally in a prospective cohort.