Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature

GOAL

Microbubbles (MBs) are known to occur within the circuits of cardiopulmonary bypass (CPB) systems, and higher-order dysfunction after cardiac surgery may be caused by MBs as well as atheroma dispersal associated with cannula insertion. As complete MB elimination is not possible, monitoring MB count rates is critical. We propose an online detection system with a neural network-based model to estimate MB count rate using five parameters: suction flow rate, venous reservoir level, perfusion flow rate, hematocrit level, and blood temperature.

METHODS

Perfusion experiments were performed using an actual CPB circuit, and MB count rates were measured using the five varying parameters.

RESULTS

Bland-Altman analysis indicated a high estimation accuracy (R2 > 0.95, p < 0.001) with no significant systematic error. In clinical practice, although the inclusion of clinical procedures slightly decreased the estimation accuracy, a high coefficient of determination for 30 clinical cases (R2 = 0.8576) was achieved between measured and estimated MB count rates.

CONCLUSIONS

Our results highlight the potential of this system to improve patient outcomes and reduce MB-associated complication risk.

Hematic Antegrade Repriming Reduces Emboli on Cardiopulmonary Bypass: A Randomized Controlled Trial

Particulate and gaseous microemboli (GME) are side effects of cardiac surgery that interfere with postoperative recovery by causing endothelial dysfunction and vascular blockages. GME sources during surgery are multiple, and cardiopulmonary bypass (CPB) is contributory to this embolic load. Hematic antegrade repriming (HAR) is a novel procedure that combines the benefits of repriming techniques with additional measures, by following a standardized procedure to provide a reproducible hemodilution of 300 ml. To clarify the safety of HAR in terms of embolic load delivery, a prospective and controlled study was conducted, by applying Doppler probes to the extracorporeal circuit, to determine the number and volume of GME released during CPB. A sample of 115 patients (n = 115) was considered for assessment. Both groups were managed under strict normothermia, and similar clinical conditions and protocols, receiving the same open and minimized circuit. Significant differences in GME volume delivery (control group [CG] = 0.28 ml vs. HAR = 0.08 ml; p = 0.004) and high embolic volume exposure (>1 ml) were found between the groups (CG = 30.36% vs. HAR = 4.26%; p = 0.001). The application of HAR did not represent an additional embolic risk and provided a four-fold reduction in the embolic volume delivered to the patient (coefficient, 0.24; 95% CI, 0.08-0.72; p = 0.01), which appears to enhance GME clearance of the oxygenator before CPB initiation.

Neural Network-based Estimation of Microbubbles Generated in Cardiopulmonary Bypass Circuit: A Clinical Application Study

The cardiopulmonary bypass system used in cardiac surgery can generate microbubbles (MBs) that may cause complications, such as neurocognitive dysfunction, when delivered into the blood vessel. Estimating the number of MBs generated, thus, is necessary to enable the surgeons to deal with it. To this end, we previously proposed a neural network-based model for estimating the number of MBs from four factors measurable from the cardiopulmonary bypass system: suction flow rate, venous reservoir level, blood viscosity, and perfusion flow rate. However, the model has not been adapted to the data collected from actual surgery cases. In this study, the accuracy of MBs estimated by the proposed model was examined in four clinical cases. The results showed that the coefficient of determination between estimated MBs and the measured MBs throughout the surgeries was R2=0.558 (p<0.001). We found that the surgical treatments, such as administration of drugs, fluids and blood transfusions, increased the number of measured MBs. The coefficient of determination increased to R²= 0.8762 (p<0.001) by excluding the duration of these treatments. This result indicates that the model can estimate the number of MBs with high accuracy under the clinical environment.