Abstract The need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular obstructions that block posterior capillary blood flow. In this paper, we analyzed the relationship between the number of microbubbles generated and four circulation factors, i.e., intraoperative suction flow rate, venous reservoir level, continuous blood viscosity and perfusion flow rate in cardiopulmonary bypass, and proposed a neural-networked model to estimate the number of microbubbles with the factors. Model parameters were determined in a machine-learning manner using experimental data with bovine blood as the perfusate. The estimation accuracy of the model, assessed by tenfold cross-validation, demonstrated that the number of MBs can be estimated with a determinant coefficient R 2 = 0.9328 ( p < 0.001). A significant increase in the residual error was found when each of four factors was excluded from the contributory variables. The study demonstrated the importance of four circulation factors in the prediction of the number of MBs and its capacity to eliminate potential postsurgical complication risks.
This paper proposes a mathematical model for online prediction of the hematocrit- and temperature-based normal blood viscosity during cardiopulmonary bypass (CPB). Clinical trials were performed using a previously developed continuous blood viscosity monitoring system, and continuous pressure- and flow-based instantaneous viscosity (η e ) data were collected from 40 patients subjected to mild to moderate hypothermic CPB. The hematocrit and blood temperature data corresponding to η e were also acquired. It was found that the blood viscosity-temperature curves for the different hematocrit levels can be well fitted using linear models, with the parameters of the linear model (slopes and intersects) also exhibiting linear relationships with the hematocrit. Based on these relationships, we were able to predict the hematocrit- and temperature-based normal viscosity (η 0 ). To test the prediction accuracy, η 0 was compared with η e using the leave-one-out cross-validation procedure. Furthermore, η 0 and the offline-measured viscosity (η), determined using a conventional viscometer, were compared for 20 patients subjected to mildly hypothermic CPB. η 0 and η e -two online blood viscosity monitoring methods based on different principles-showed good agreement (R 2 = 0.74 and p <; 0.0001). Moreover, η 0 and η also showed good agreement (R 2 = 0.69 and p <; 0.0001). The proposed model is suitable for online prediction of the hematocrit- and temperature-based normal blood viscosity during CPB. The proposed model can function as the core of a future application for investigating the effects of blood viscosity during clinical perfusion management and facilitate detailed online blood viscosity studies.
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 (R 2 >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 (R 2 =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.
Body temperature maintained during open distal anastomosis in patients who undergo aortic surgery has been showing an upward trend; however, a higher temperature may increase visceral organ and spinal cord injury. Distal perfusion may reduce abdominal organ injury, especially acute kidney injury (AKI).From 2009 to 2016, 56 patients who underwent ascending aortic and/or aortic arch surgery were enrolled. Open distal anastomosis was performed using one of three protection strategies: 1) systemic temperature of 25°C followed by selective cerebral perfusion (SCP) with lower body circulatory arrest (Group CA25, n=27); 2) systemic temperature of 28°C followed by SCP with lower body circulatory arrest (Group CA28, n=4); and 3) systemic temperature of 28°C followed by SCP with distal aortic perfusion (Group DP, n=25).During the postoperative course, levels of blood urea nitrogen, creatinine, liver enzymes, lactate dehydrogenase and lactate in Groups CA28 and CA25 were significantly higher than those in Group DP. AKI defined by the AKI Network occurred in 28 cases (50%) and 3 cases required permanent hemodialysis. AKI was significantly higher in Groups CA25 and CA28 than in Group DP (p=0.026). Mid-term follow-up showed that patients who developed postoperative AKI were more likely to suffer from cardiovascular events.Distal perfusion during open distal anastomosis reduced kidney and liver injury after thoracic aortic surgery despite an increased body temperature of up to 28°C. This strategy may be useful to prevent AKI, liver dysfunction, the need for hemodialysis and multiple organ failure and could improve mid-term results.
We experienced 2 cases in which oversensing of a particular noise after the implantation of an implantable cardiac device was observed in the acute phase. These were unusual cases in which the noise exhibited a low frequency pattern and appeared several hours after the implantation, but disappeared within 1 week. Here we present these cases and the details of an experiment investigating the origin of the noise and the methods for its prevention. The noise in these cases led to pacing inhibition and could have induced an inappropriate shock due to oversensing, but its morphology and electromagnetic interference were atypical for a lead failure or myopotentials. The noise spontaneously disappeared from the analysis of the data stored in the device. In an experiment based on the Irnich model, in which it was assumed that blood invaded a damaged grommet, low frequency noise occurred which was similar to the noise in the two cases. We concluded that care must be exercised when handling grommets.
In this paper, we developed a model that uses pressure-flow monitoring information in the oxygenator to estimate viscosity of human blood. The comparison between estimated viscosity (ηe) and measured viscosity (η) was assessed in 16 patients who underwent cardiac surgery using mild hypothermia cardiopulmonary bypass (CPB). After initiation of CPB, ηe was recorded at three periods: post-establishment of total CPB, post-aortic cross-clamp, and post-declamp. During the same period, blood samples were collected from the circuit and η was measured with a torsional oscillation viscometer. The ηe was plotted as a function of η and the systematic errors and compatibility between two methods were assessed using Bland-Altman analysis. The parameters ηe and η were very strongly correlated at all points (R(2)=0.9616, p<;0.001). The Bland-Altman analysis revealed a mean bias of -0.001 mPas, a standard deviation of 0.03 mPas, limits of agreement of -0.06 mPas to 0.06 mPas, and a percent error of 3.3%. There was no fixed bias or proportion bias for the viscosity. As this method estimates blood viscosity with good precision during CPB continuously, it may be helpful for clinical perfusion management.
