Objectives : In this study, we aimed to identify demographic and clinical parameters in donors that
could predict the ischaemic time tolerated by the heart transplant in order to increase the number of
heart transplantation performed in our centre by expanding the area of graft recruitment.
Methods : A single site retrospective study was conducted on 116 heart transplantations performed
in our centre between 2013 and 2021. We first assessed independent correlations between donors
variables and the use of ECMO in the recipients. Multivariate logistic regression analyses were used to
identify interactions between the different donor parameters. A predictive score of primary graft
dysfunction (PGD) in the recipient was created based on the coefficients values of each parameter
included in the logistic regression model. The model was developed on a subset of 70% of our sample
randomly generated and tested on the remaining patients.
Results : Donor parameters independently correlated with PGD included sex, weight, height, total graft
ischemic time (p < 0.1). We assessed multivariate interaction by introducing these parameters in a
multivariate logistic regression model. The predictions of the model only slightly match the actual use
of ECMO with an accuracy of 0.641. In our second model, we included age, sex, total graft ischemic
time, height, weight, BMI, EF, hypertension, malignancy, diabetes, troponins, creatinin and CK. With
an accuracy of 0.719, this model seems to perform better in the prediction of ECMO use in the
recipient. Based on the coefficients values of each donor parameter inclueded in the model, a PGD
predictive score was derieved.
Conclusions : We created a predictive of PGD score based on identified donors parameters that may
help identify grafts with a higher tolerance to ischemia thus facilitating the decision-making process of
clinicians regarding the acceptance of a heart transplant with prolonged ischemic time.