Video kinematics of an unloaded, ex-vivo beating heart can predict future loaded heart function.

Abstract

There is not a reliable biomarker for identifying which hearts are at highest risk for primary graft dysfunction. We hypothesize that ex-vivo video kinematics (EVVK) of unloaded beating hearts can help predict post-implant heart performance. Porcine hearts procured following a DCD protocol were re-animated in unloaded ex situ heart perfusion (ESHP) before transitioning to a loaded configuration. Five second videos were recorded using a consumer cell phone. Machine learning EVVK software was developed to autonomously quantify heart function. Of 22 porcine hearts, 6 (27%) did not tolerate loading. EVVK demonstrated prognostic capability to differentiate the future loaded performance in terms of both compliance (p=0.004) and contractility (p=0.003). We also demonstrate proof-of-concept in using EVVK on human hearts (n=4) to provide a quantitative functional assessment of the allograft during transportation from the donor to recipient center. Overall, non-invasive EVVK may be able to aid clinicians in quantitative heart evaluation.