The authors contributed equally to this report.
A Risk-Prediction Model for In-Hospital Mortality After Heart Transplantation in US Children
Article first published online: 2 FEB 2012
© Copyright 2012 The American Society of Transplantation and the American Society of Transplant Surgeons
American Journal of Transplantation
Volume 12, Issue 5, pages 1240–1248, May 2012
How to Cite
Almond, C. S., Gauvreau, K., Canter, C. E., Rajagopal, S. K., Piercey, G. E. and Singh, T. P. (2012), A Risk-Prediction Model for In-Hospital Mortality After Heart Transplantation in US Children. American Journal of Transplantation, 12: 1240–1248. doi: 10.1111/j.1600-6143.2011.03932.x
- Issue published online: 26 APR 2012
- Article first published online: 2 FEB 2012
- Received 01 June 2011, revised 21 November 2011 and accepted for publication 25 November 2011
- heart failure;
- heart transplantation;
- risk factors;
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.