Objective: For heart transplantation, donor heart status needs to be evaluated during normothermic ex situ perfusion (ESHP). Left ventricular end-systolic elastance (E

$_\mathrm{es}$

) measures the left ventricular contractile function, but its estimation requires the occlusion of the left atrium line in the ESHP, which may cause unnecessary damage to the donor heart. We present a novel method to quantify E

$_\mathrm{es}$

based on hemodynamic parameters obtained from only one steady-state PV loop in ESHP. Methods: E

$_\mathrm{es}$

was obtained by the end-systolic point (P

$_\mathrm{es}$

, V

$_\mathrm{es}$

) and the volume axis intercept point of E

$_\mathrm{es}$

$_\mathrm{0}$

). V

$_\mathrm{0}$

was estimated through the support vector machine regression (SVR) method using parameters derived from the measured steady-state PV loop. To achieve high V

$_0$

estimation accuracy, a filter-based support vector machine recursive feature elimination method (SVM-RFE) algorithm selected the parameters for V

$_\mathrm{0}$

estimation. Hemodynamic parameter samples ( n = 101) obtained from ESHP experiments with pig's hearts were used to train the E

$_\mathrm{es}$

calculation model. Early post-transplantation outcomes in six heart transplantation experiments were then estimated from the trained E

$_\mathrm{es}$

calculation model. Results: E

$_\mathrm{es}$

calculated by the proposed method agreed well with conventional multi-beat estimates obtained by the occlusion process ( r = 0.88, p < 0.001, n = 101) and was capable of predicting the early post-transplant cardiac index ( r = 0.84, p < 0.05, n = 6). Conclusion: This method effectively assesses left ventricular contractility during ESHP and predicts early post-transplant outcomes in the porcine model. Significance: Our approach is the first to quantify E

$_\mathrm{es}$

by estimating V

$_\mathrm{0}$

from steady-state beats in ESHP for accurately predicting early post-transplantation outcomes.