Publications

A Novel Method of Donor‒Recipient Size Matching in Pediatric Heart Transplantation

Did you know the second most common reason for refusal of donor hearts in pediatric heart transplantation is donor size mismatch? Donor-to-recipient size matching is almost exclusively weight-based, but there is little to no evidence that validates weight as a reliable surrogate of cardiac size. Another size matching option is a case-by-case segmentation of total cardiac volume (TCV) by computed tomography (CT) for direct virtual transplantation; however, it remains limited by the unavailability of donor chest CT.

A team at Cincinnati Children’s Hospital Medical Center’s Heart Institute developed a predictive model for donor TCV on the basis of anthropomorphic and chest X-ray (CXR) cardiac measures in this article “A novel method of donor‒recipient size matching in pediatric heart transplantation: A total cardiac volume‒predictive model.” It was published December 4, 2020 in The Journal of Heart and Lung Transplantation, and the team included ACTION leaders Farhan Zafar, Chet Villa, Angela Lorts, David Morales, and Ryan Moore. 

Three predictive models of TCV were fit through multiple linear regression with differing variables; Model A as weight only, Model B was weight, height, sex, and age, and Model C was weight, height, sex, age, and 1-view anteroposterior CXR maximal horizontal cardiac width. The results? The most accurate prediction of TCV was provided by Model C. Researchers conclude that TCV can be predicted accurately using readily available anthropometrics and a 1-view CXR from donor candidates. This is a simple, scalable, and reliable method of TCV estimation to improve donor size matching.

CITATION

Szugye NA, Zafar F, Ollberding NJ, Villa C, Lorts A, Taylor MD, Morales DLS, Moore RA. A novel method of donor‒recipient size matching in pediatric heart transplantation: A total cardiac volume‒predictive model. J Heart Lung Transplant. 2021 Feb;40(2):158-165. doi: 10.1016/j.healun.2020.11.002. Epub 2020 Dec 4. PMID: 33317957; PMCID: PMC7855742.