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Transplantation. 2019 Feb 19. doi: 10.1097/TP.0000000000002679. [Epub ahead of print]

A Recipient Risk Prediction Tool for Short Term Mortality after Pediatric Heart Transplantation.

Author information

1
Department of Pediatrics, Section of Pediatric Cardiology, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas.
2
Department of Pediatrics, Washington University School of Medicine, Saint Louis, MO.

Abstract

BACKGROUND:

The first year after heart transplantation (HT) has the highest risk of mortality. We aim to derive and validate a recipient risk-prediction tool for early mortality after pediatric HT.

METHODS:

The ISHLT registry was used to identify patients (≤18 years) who underwent primary-HT during 01/2000-12/2014. Independent predictors of 1-year mortality were identified based on recipient characteristics at HT. Risk scores were assigned based on the magnitude of relative odds of 1-year mortality. The predictive capability of the ISHLT registry derived recipient risk score was externally validated using the SRTR registry data from 2015 - 2017 to ensure a cohort of patients completely exclusive from the derivation cohort.

RESULTS:

A total of 5045 eligible patients were included in the analysis. The 20-point risk scoring system incorporated 8 recipient variables including age at HT, diagnosis, pre-HT ventilator use, ECMO, inhaled nitric oxide use, infection, estimated glomerular filtration rate, and serum bilirubin. Compared to low-risk score group, high-risk group had sevenfold increased risk of 1-year mortality (HR 7.4 [5.2, 9.1], P<0.001). The C-statistics (0.77) and the Hosmer-Lemeshow goodness-of-fit (0.9) for recipient risk score using derivation cohort from ISHLT registry performed well, and was similar to the internal and external validation cohort (C-statistics 0.75, 0.78 and Hosmer-Lemeshow goodness-of-fit p = 0.4, 0.3 respectively).

CONCLUSION:

This study derived and externally validated a simple risk predictive model based on recipient characteristics at HT that has good prediction characteristics for 1-year post HT mortality. This model may help clinicians identify candidates who are at a higher risk for post-HT mortality and may optimize organ-sharing.

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