Background: The pediatric heart transplant community uses weight-based donor-to-recipient size matching almost exclusively, despite no evidence to validate weight as a reliable surrogate of cardiac size. Donor size mismatch is the second most common reason for the refusal of donor hearts in current practice (∼30% of all refusals). Whereas case-by-case segmentation of total cardiac volume (TCV) by computed tomography (CT) for direct virtual transplantation is an attractive option, it remains limited by the unavailability of donor chest CT. We sought to establish a predictive model for donor TCV on the basis of anthropomorphic and chest X-ray (CXR) cardiac measures.
Methods: Banked imaging studies from 141 subjects with normal CT chest angiograms were obtained and segmented using 3-dimensional modeling to derive TCV. CXR data were available for 62 of those subjects. A total of 3 predictive models of TCV were fit through multiple linear regression using the following variables: Model A (weight only); Model B (weight, height, sex, and age); Model C (weight, height, sex, age, and 1-view anteroposterior CXR maximal horizontal cardiac width).
Results: Model C provided the most accurate prediction of TCV (optimism corrected R2 = 0.99, testing set R2 = 0.98, mean absolute percentage error [MAPE] = 8.6%) and outperformed Model A (optimism corrected R2 = 0.94, testing set R2 = 0.94, MAPE = 16.1%) and Model B (optimism corrected R2 = 0.97, testing set R2 = 0.97, MAPE = 11.1%).
Conclusions: TCV can be predicted accurately using readily available anthropometrics and a 1-view CXR from donor candidates. This simple and scalable method of TCV estimation may provide a reliable and consistent method to improve donor size matching.
Keywords: 3D imaging; pediatric heart failure; pediatric heart transplantation; size matching; total cardiac volume.
Copyright © 2020 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.