Prediction Model for Contractile Function of Circulatory Death Donor Hearts Based on Microvascular Flow Shifts During Ex Situ Hypothermic Cardioplegic Machine Perfusion

Abstract

Background Hearts procured from circulatory death donors (DCD) are predominantly maintained by machine perfusion (MP) with normothermic donor blood. Currently, DCD heart function is evaluated by lactate and visual inspection. We have shown that MP with the cardioplegic, crystalloid Custodiol-N solution is superior to blood perfusion to maintain porcine DCD hearts. However, no method has been developed yet to predict the contractility of DCD hearts after cardioplegic MP. We hypothesize that the shift of microvascular flow during continuous MP with a cardioplegic preservation solution predicts the contractility of DCD hearts. Methods and Results In a pig model, DCD hearts were harvested and maintained by MP with hypothermic, oxygenated Custodiol-N for 4 hours while myocardial microvascular flow was measured by Laser Doppler Flow (LDF) technology. Subsequently, hearts were perfused with blood for 2 hours, and left ventricular contractility was measured after 30 and 120 minutes. Various novel parameters which represent the LDF shift were computed. We used 2 combined LDF shift parameters to identify bivariate prediction models. Using the new prediction models based on LDF shifts, highest r² for end-systolic pressure was 0.77 (P=0.027), for maximal slope of pressure increment was 0.73 (P=0.037), and for maximal slope of pressure decrement was 0.75 (P=0.032) after 30 minutes of reperfusion. After 120 minutes of reperfusion, highest r² for end-systolic pressure was 0.81 (P=0.016), for maximal slope of pressure increment was 0.90 (P=0.004), and for maximal slope of pressure decrement was 0.58 (P=0.115). Identical prediction models were identified for maximal slope of pressure increment and for maximal slope of pressure decrement at both time points. Lactate remained constant and therefore was unsuitable for prediction. Conclusions Contractility of DCD hearts after continuous MP with a cardioplegic preservation solution can be predicted by the shift of LDF during MP.

Keywords: Custodiol‐N; coronary microvasculature; donation after circulatory death; heart transplantation; machine perfusion; myocardial microcirculation; prediction model.

Figure 1

Donation after circulatory death model and functional analysis. A, DCD induction by the termination of mechanical ventilation. B, Continuous measurement of microvascular flow by laser Doppler perfusion probe during 4 hours of hypothermic, oxygenated cardioplegic perfusion with Custodiol‐N. C, Contractile assessment. D, Development of prediction models. DCD indicates donation after circulatory death; and LDP, laser Doppler perfusion. The figure was created with BioRender.

Figure 2

Models. AUC indicates area under the curve; LDF, Laser Doppler Flow; LDP, laser Doppler perfusion; rAUC, relatve AUC; and rLDF, relative LDF and shows the mean of the LDF signal over the observed period.

Figure 3

Workflow. A, Model prediction and model identification. B, Venous lactate concentration. LVV indicates Left ventricular volume.

Figure 4

Heat map of prediction models with 1 parameter. The heat map depicts the color‐coded r ² (A) or slope (B) of each prediction model shown on the left for each contractile parameter shown below at 30 and 120 minutes of reperfusion and for pressure‐contractility‐matching. N=8. AUC indicates area under the curve; dp/dt_max, Maximal pressure increase; dp/dt_min, Maximal pressure decrease; ESP, end‐systolic pressure; LDP, laser Doppler perfusion; PCM, pressure‐contractility‐matching; PP, perfusion pressure; and SecLast, second‐last.

Figure 5

r² values for prediction models with 1 parameter. The heat map depicts the actual r ² values (A) or slope (B) of each prediction model shown on the left for each contractile parameter shown below at 30 and 120 minutes of reperfusion and for pressure‐contractility‐matching. Significant (P<0.05) r² are marked green. N=8. AUC indicates area under the curve. dp/dt_max, Maximal pressure increase, dp/dt_min, Maximal pressure decrease; ESP, end‐systolic pressure; LDP, laser Doppler perfusion; PCM, pressure‐contractility‐matching; PP, perfusion pressure; and SecLast, second‐last.

Figure 6

Heat map of prediction models based on 2 combined parameters. The heat map depicts the color‐coded r ² of each prediction model shown on the left for each contractile parameter shown below at 30 and 120 minutes of reperfusion and for pressure‐contractility‐matching. N=8. AUC indicates area under the curve; dp/dt_max, maximal pressure increase; dp/dt_min, maximal pressure decrease; ESP, end‐systolic pressure; LDP, laser Doppler perfusion, PCM, pressure‐contractility‐matching; PP, perfusion pressure; and SecLast, second‐last.

Figure 7

Best prediction models per parameter. Mean prediction over time was calculated, by calculating the mean of r ² at 30 and 120 minutes of reperfusion at 20 mL left ventricular volume. Thus, no error bars or significances could be calculated. N=8. AUC indicates area under the curve; dp/dt_max, maximal pressure increase; dp/dt_min, maximal pressure decrease; ESP, end‐systolic pressure; LDP, laser Doppler perfusion; LVV, left ventricular volume; PCM, pressure‐contractility‐matching; PP, perfusion pressure; and SecLast, second‐last. *P<0.05.