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Sympathetic nerve discharges during artificial heart circulation

Peripheral vascular resistance were reported to be mediated by the autonomic nervous system. Thus, in the next step, we paid attention to the direct recording of an autonomic nerve discharges during artificial heart circulation. By these reasons, to investigate the origin furthermore, sympathetic nerve activity was measured in the animal experiments with the artificial heart circulation by the use of the renal sympathetic nerve.

Material and method

The left pleural cavity was opened through the fifth intercostal space in the four adult mongrel dogs weighing 15-35 kg after intravenous administration of thiopental sodium (2.5 mg/kg) and ketamine sodium (5.0 mg/kg). Electrodes for electrocardiograph (ECG) were implanted, and aortic pressure (AoP) and left atrial pressure (LAP) were monitored continuously with catheters inserted into the aorta and left atrium through the left femoral artery and the left appendage, respectively.

For left artificial heart (LAH) implantation, a polyvinyl chloride (PVC) out-flow cannula with a T-shaped pipe was inserted into the descending aorta and secured by ligature. A PVC inflow cannula was inserted into the left atrium through the left atrial appendage. Both cannulae were connected to our TH-7B pneumatically driven sac-type blood pump via the built-in valve connectors. A PVC outflow cannula was inserted into the pulmonary artery, and a PVC inflow cannula was inserted into the right atrium through the right atrial appendage. Both cannulae were then connected to the right pump. The TH-7B pneumatically driven sac-type blood pump was used in these experiments to constitute a BVB-type complete artificial circulation model. Driving pressure and systolic duration of both blood pumps were selected to maintain hemodynamic derivatives within the normal range, and pump output was controlled to maintain the cardiac output before the electrical ventricular fibrillation. After BVB pump implantation, the left flank was opened, and the left renal artery was then exposed [7,8]. After the left renal sympathetic nerves were separated, the nerve sheath was removed, and the nerve was placed on bipolar stainless-steel electrodes for recording of renal sympathetic nerve activity (RSNA) [7,8]. The renal sympathetic nerve discharges were amplified by means of a preamplifier and main amplifier (DPA-21 , DPA-100E; DIA Medical System Co.) with a bandwidth of 30 Hz-3 k HZ and were displayed on an oscilloscope. Time series data of hemodynamic parameters and sympathetic nerve activity were recorded with an ink-jet recorder and in magnetic tape after the stabilization of all hemodynamic derivatives (almost 20-30 min after the preparation). After the control data recording, ventricular fibrillation was induced electrically. Confirming the stabilization of the hemodynamics with stable BVAD drive condition, time series data were recorded.

Experimental results and consideration of the sympathetic nerve recordings

During artificial circulation with biventricular assist type TAH model under electrically induced ventricular fibrillation, hemodynamic parameters were relatively easily maintained within normal range by the manually operated pneumatic drive console. Fig.6 showed an example of time series data of hemodynamic parameters and sympathetic nerve recording.

From upper tracing, electrocardiogram, arterial blood pressure, integrated nerve activity, and renal sympathetic nerve discharges were shown. During artificial heart circulation, sympathetic nerve discharges were synchronized with an artificial heart driving rhythm, though the sympathetic nerve activity were synchronized with heart rate and respiration during natural heart circulation. Driving rate of an artificial heart were fixed during the measurement, thus, burst of the sympathetic discharges had kept the fixed rhythm. These phenomenon had allowed us the reconstruction of the attractor of the sympathetic nerve activity in phase space.

Fig.7 showed an example of the reconstructed attractor of the integrated sympathetic nerve discharges. Time constant of the integration was 0.1 s. It was thought that it may be the strange attractor, which is the characteristics of deterministic chaos, because it had characteristics of fractal in these Orbit formation. Lyapunov numerical analysis suggest its sensitive dependence upon initial conditions. These results suggest that sympathetic nerve time series signals showed the characteristics of the deterministic chaos.

If a result is thought about frankly, central nervous system is though to play an important role to constitute the chaotic dynamics in circulation. Chaotic dynamics in the sympathetic nerve activity is thought to mediate the peripheral vesselユs properties, and make chaos in the circulation.

To analyze this information relationship, Mutual information analysis were performed in this study [9].

According to an algorithm proposed by Fraser and Swinney [9], we had measured how dependent the values of y (t+T) are on the velues of x(t). Then T is a time delay. Assignment [s,q] = [x(t), y(t+T)] was made to consider the a general coupled system (S,Q). The mutual information was made to answer the question, メ Given a measurement of s, how many bits on the average can be predicted about q ? メ

I(S,Q) = コ Psq (s,q) log [Psq (s,q) / (Ps(s) Pq (q))] dsdq (1)

Where i) S and Q denote systems, ii) Ps(s) and Pq (q) are the probability densities at s and q, respectively, iii) Psq (s,q) is the joint probability density at s and q. If the value of mutual information for (S,Q) is larger, it means that mutual dependence between S and Q is stronger.

The results suggest that in some driving conditions, coupling of sympathetic nerve and peripheral vesselユs properties had become strong compared with that during natural heart beat. Thus, sympathetic nerve were suggested to contribute the chaotic dynamic in circulation sufficiently.

If chaotic dynamics were generated in the peripheral vesselユs properties, it must influence the arterial baroreflex system on the arterial wall. This information was communicated to the brain, and response to this information is sent to the sinus nodes. And finally, it make chaos in heart rate variability.

These results suggest the importance of the brain in generating chaos in circulation. In the next step, we want to consider the reasonable control of an artificial heart by the use of these information from brain by the view point of nonlinear dynamics.

3. Predictive control of an artificial heart system

Tohoku University has been involved continuously in AH research for over 20 years, and has developed an automatic AH control system using various types of microsensors. However, the control algorithm of the AH using hemodynamic parameters and hormonal factors could not avoid the possibility of a time delay compared with the natural heart.

 

Prediction with an information of sympathetic nerve discharges

We therefore proceeded to evaluate the autonomic nervous system using the sympathetic neurogram during AH pumping, and developed a new control algorithm of the ventricular assist device(VAD)using the sympathetic tone. The present study was designed to establish a real time control system for the total artificial heart(TAH),which works at a speed like the natural heart without any time delay,using sympathetic tone and hemodynamic parameters.Not all cardiac nerves could be used for measuring the sympathetic neurogram,because TAH implantation removed the cardiac nervous system and retrograde generation extinguishes them [11]. So we used the renal sympathetic nerve,already employed for autonomic nervous system evaluation,as the parameter of sympathetic tone. The renal sympathetic nerve is almost completely composed of efferent sympathetic nerves [12]. This may be useful for assessment of efferent sympathetic tone in long-term animal experiments. Moving averages of left atrial pressure (LAP) and aortic pressure (AoP) were calculated and taken as the parameters of the preload and afterload of the natural heart. Multiple linear regression analysis was used to set up the equations which indicate following cardiac output.

In this study, the functional formula for an accurate estimate of the following cardiac output was designed, and the possibility of a real-time automatic TAH control system was analyzed [13]. Seven adult mongrel dogs of both sexes weighing 15-35 kg were anesthetized with thiopental sodium (2.5 mg/kg) and intravenous ketamine sodium (5.0 mg/kg). After tracheal tube intubation, they were placed on a volume-limited respirator (Acoma ARF-850). The anesthesia was maintained with nitrous oxide inhalation under mechanical ventilation. Electrocardiogram (ECG) electrodes were implanted in the left foreleg and both hind legs. AoP and LAP were monitored continuously from a catheter inserted into the aorta and left atrium through the left femoral artery and the left appendage, respectively.

Ultrasonic flowmeter was used in this experiment to avoid electromagnetic noise contamination on nerve waveform. An ultrasonic flow meter (Advance: Transonic T101) was placed on the pulmonary artery to measure cardiac output, because right flow rate must be controlled in the animal with TAH to keep up lung oxygenation. Left flank was opened between the iliac crest and the costovertebral angle, and the left renal artery was then, exposed. The left renal sympathetic nerve was dissected free from the left renal artery and surrounding connective tissues. After removal of the nerve sheath, the nerve was attached to a bipolar stainless steel electrode for recording renal sympathetic nerve activity (RSNA). Discharges were recorded after amplifying the original signal with a differential preamplifier and the main amplifier (DIA Medical System Co.: DPA-21, DPA-100E) of band width 30Hz-3kHZ, and displayed on an oscilloscope. The output from the amplifier was passed through a gate circuit for removing baseline noise, and rectified by an absolute value circuit. Then the rectified output was integrated by an R-C integrator circuit (time constant, 0.1 s ). The output of the integrated nerve discharges were calibrated in l/V and their areas were measured for a given period, and expressed as RSNA per time unit.

ECG, systolic, diastolic, and mean AoP, mean LAP, mean pulmonary artery flow, and RSNA were measured. RSNA was quantified from the area of the integrated nerve discharge waveform per time unit. All data were analyzed in the computer system (NEC PC9801VM21). Moving averages of the mean LAP and AoP were calculated as the parameters of the preload and afterload of the natural heart, respectively. Multiple regression analysis was done using the preload, afterload, RSNA, and the following cardiac output. Differences were analyzed by the paired t-test and the F-test and were considered significant when p<0.05.

 

Experimental results and discussion

An example of the computer analyzed time series data obtained from an adult mongrel dog is shown in Fig.8. From the upper tracing, ECG, AoP, left ventricular pressure (LVP), integrated RSNA, marker, RSNA, pulmonary artery flow, (PAF), and LAP were quantified in the computer system. Grouped discharges, which appeared synchronously with heart beat and respiration, were observed in the spontaneous activity of the renal sympathetic nerve. Fig.9 illustrates the responses of the RSNA and heart rate to clamping the descending aorta. AoP dropped immediately after Ao clamping, and rose again soon after declamping. But the RSNA responses needed some time delay, which probably indicates the response time of the baroreflex. The heart rate response needed longer to respond to sympathetic tone, probably indicating the response time of the sinus node to the autonomic nervous system.

Assuming the moving averages of AoP, LAP, and integrated RSNA as the explanatory variables, and the following cardiac output as the criterion variable, multiple linear regression analysis was computed using the time series data of this experiment. An example of the multiple regression equation for the following cardiac output is shown below.

Y = al × mRSNA + a2 ×mLAP - a3 × m AoP + b (2)

Where Y is the following cardiac output (1/min), mRSNA the moving average of mean RSNA (μV sec), mLAP the moving average of mean LAP (mmHg), and mAOP the moving average of mean AoP (mmHg).

For example, from the time series data of these experiments, they were calculated as al=0.024, a2=0.413, a3=0.004, and b=0.967. The multiple correlation coefficient was R = 0.700 and this equation was significant (p<0.01); in the explanatory variables, mRSNA and mLAP were significant, but not mAoP. A multi-co-linearity problem between mRSNA and mAOP eliminates the mAOP among the explanatory variables by the backward elimination method.

Thus, an example of new multiple regression equation is given below.

Y = al ×mRSNA + a2 ×mLAP + b (3)

This was worked out as al=0.022, a2=0.430, and b=0.700. The multiple correlation coefficient was R = 0.682 and contribution rate was R = 0.465, so this equation was significant (p<0.01). Standardized partial regression coefficients in this dog were al=0.432 and a2=0.454, so the contributions of these parameters to this equation were not significantly different.

Fig.10 illustrates the correlation between estimated cardiac output calculated in this equation and the following cardiac output detected about 2.9 seconds (five beats) after the moving averages of the LAP and AoP. Average and standard deviation of the standardized partial regression coefficients in the seven dogs were calculated as al = 0.363 ±0.062, and a2 = 0.471± 0.067. Both parts of this equation were significant. Therefore mean LAP and RSNA, recorded five beats before, were useful to estimate the following cardiac output.

A variety of TAH control methods have been evaluated in various laboratories in the world [13,14]. The control algorithm based on Starling's law has been used by many investigators and others have proposed a control method in which AoP is maintained as a constant value [15]. However, these control strategies could not avoid the possibility of a time delay compared with the natural heart, because they were based on hemodynamic data, which are by definition the result of the behavior of the vascular system in the immediate past . In the natural heart. cardiac output can be changed by various mechanisms: sympathetic stimulation, with an increase in venous return and heart rate or stroke volume or both, and Starling's law.

The sympathetic nervous system detects a physiological stress and transmits this information to the peripheral organs, and adapts the circulation and metabolism to meet demand [11,12]. However, in most TAH systems, there is no feedback loop to transmit this information to the control system. We believe that it is necessary to detect the sympathetic tone for a real time TAH control system. In this study, we used the sympathetic tone and Starling's law as the information for establishing the target cardiac output for the TAH real time control system. Estimated cardiac output, calculated by multiple linear regression analysis of the RSNA and preload, was correlated significantly with the following cardiac output, actually measured later. This suggests the possibility of a real-time control method of the TAH system using sympathetic tone and hemodynamic parameters.

Though RSNA is useful to detect the efferent sympathetic tone, it is not the cardiac nerve, so it is always open to discussion whether RSNA tone acts like that of the cardiac nerve. But as the cardiac nerves do not exist after TAH implantation, RSNA is useful to determine the sympathetic efferent tone, because it is almost entirely composed of efferent sympathetic nerve fibers [12].

Another important problem concerns hormonal factors like the catecholamines. If there is to be the useful sensor of hormonal factors, we must add the explanatory variables of the equation which estimate the following cardiac output. Important problems in the development of a permanent electrode for the neurogram still remain unsolved. By this reason, we proposed new TAH control methodology based upon the neural information and shown below.

Mayer wave control for an artificial heart

From the various view pints, control algorithm of the total artificial heart (TAH) has been discussed more and more till now [13-15]. Several investigators proposed the automatic TAH control algorithm to prevent thrombus formation and suggest the importance of full stroke driving [14,15]. Some researchers reported the importance of left and right heart balance, some investigators controlled their artificial heart according to the Starling's law [14]. And other researchers controlled their devices to maintain the arterial blood pressures [15]. Many developers of a TAH showed the importance of the optimal drive of their devices, however, few researchers pointed out the importance of an optimization from the physiological and pathophysiological viewpoints of the creatures [16,17].

According to the widespread clinical application of TAH like the Bridge use for transplantation, importance of Quality of life (QOL) for a patient with an artificial heart attracts attention as a next step of the development [13,15]. Various cardiac output is needed during various activity of the creatures [11]. If the cardiac output of the patients with artificial heart cannot follow the physical activity, congestive heart failure may be occur and patients cannot enjoy their QOL. By this reason, we must develop the new control algorithm, which can catch up the physical activities.

It was very important to consider the optimal drive of TAH, not only an optimal drive for mechanical devices, but also an optimal drive from the physiological view point. For that purpose, information of an autonomic nervous system may be needed, however, it is very difficult to monitor the autonomic nerve discharges continuously [7,8].

In this study, we propose the new basic concept for an automatic control algorithm of the total artificial heart (TAH) using fluctuations in the circulatory system [18,19]. It was reported that fluctuation of hemodynamics reflects ongoing information of the autonomic nervous system [18,19]. Several investigators suggested that Mayer wave around 0.1 Hz was reported to reflect the sympathetic nerve information, and respiratory wave was reported to be reflected the parasympathetic nerve information [18]. To detect the information of the autonomic nervous system, we paid attention to these components of rhythmical fluctuation in this study. However, of course, we could not measure the heart rate variabilities (HRV) in the creatures with an artificial heart, because there was no heart. Accordingly, an only information that could easily measure in the hemodynamics reflecting the central nervous system (CNS), might be the peripheral vesselユs properties. For such reason, we paid attention to the fluctuations of the vascular resistance, which could measure even during an artificial heart circulation.

By the use of the chronic animal experiments recording time series data of hemodynamic parameters using healthy adult goats in the awake conditions, fluctuations in the peripheral vascular resistance were calculated in this study. The Probability of an predictive automatic TAH control system using autonomic nerve information was considered.

The goats used weighed from 60 to 70 kg with a mean of 65 kg. These goats were kept fasting for 2 days before the experiments. Three goats were anesthetized by halothane inhalation. After tracheal tube intubation by tracheotomy, they were placed on a respirator. Electrodes for electrocardiography (ECG) were attached to the legs and later implanted in the pericardium. The left pleural cavity was opened by a left fourth rib resection. Arterial blood pressure was monitored continuously with catheters inserted into the aorta through the left internal thoracic artery. Central venous pressure (CVP) was measured by the fluid-filled catheter inserted through the internal thoracic vein. Cardiac out put was measured with an electromagnetic flow meter attached to the ascending Aorta. After closing the chest, the goats were placed in the cage, and extubated after waking. All hemodynamic time series data were monitored continuously during the experiments (Fukuda Denshi: MCS-5000) recorded in the awake condition.

All time series data were recorded in magnetic tape data recorder (TEAC: RD-130TE). Quantitative analysis, statistics processing and spectral analysis was carried out in the personal computer system (PC9801BA) through the AD converter. Mayer wave peak was clearly recognized in all goats in the spectrum of the vascular resistance. The band pass filter was used to convert this information for the automatic control. Frequency of the band-pass were 0.04 - 0.15 Hz, which was reported to be the frequency band of the Mayer wave.

Of course, an important problem of the predictive control for TAH using neural information was the Time lag of the prediction. In this study, we calculated the cross correlation function of the band pass value of the peripheral vascular resistance and cardiac output. And time delay were considered for an automatic TAH control system using information of the fluctuations in the peripheral vascular resistance. Finally, we proposed the predictive TAH control algorithm using autonomic nerve information.

Firstly, we must check the spectral peaks in the power spectrum of the peripheral vascular resistance, which could obtained even during artificial heart circulation. If we could not obtained spectral peaks by the use of the methodologies of an FFT, we could not use this information for an artificial heart control. In all experimental animals, Mayer wave peaks, reflecting autonomic nerve regulation, was observed, suggesting that we might be able to use this information. Fig.11 showed an example of the time series data of the hemodynamic parameters. From upper tracing, heart rate, arterial blood pressure, cardiac output, peripheral vascular resistances, and resistances after band pass processing. Band pass filter was established to 0.04 - 0.15 Hz, which was reported to be the Mayer wave fluctuations in hemodynamic time series data. Behavior of the time series data suggesting that band passed data gave us an information different from peripheral vascular resistances. After an intense changes of the filtered peripheral vascular resistances, cardiac output were shown as increasing.

Secondary, we must check the time lag between the changes of the Mayer wave and alteration of the cardiac output. Fig.12 showed an example of cross correlation of the band-passed peripheral vascular resistances and cardiac output. Clear peak was observed in the almost five seconds after, suggesting the possibility to predict the cardiac output from the information of the Mayer wave in vascular resistances.

Finally, we predicted the cardiac out put in future by the use of the Mayer wave in the peripheral vascular resistances, which could easily obtained from the creatures with an artificial heart. Fig.13 showed an example of the prediction after five seconds. X axis showed an band passed peripheral vascular resistances and Y axis showed measured cardiac out put recorded five seconds after. Significant correlation was observed in the figure, suggesting the probability of the realization of the predictive automatic control for an artificial heart system. In all four goats, significant correlation was observed between cardiac output after five seconds and filter treated peripheral vascular resistance, suggesting reproducibility of this prediction algorithm.

One of the major findings of this study is the probability of an actualization of the predictive control algorithm for an artificial heart by the use of an information from autonomic nervous system. For the QOL of the patient with an artificial heart, information from biological system may be important, because artificial heart must be respond to the physical activity. Pump out put of an artificial heart must increase, when runs. And output must decrease, when sleeps. To control an artificial heart, biological information may be necessary. However, it is very difficult to detect autonomic nerve activity directly in the chronic stage. In this study, stable measurement of an autonomic nerve information was realized by the use of the hemodynamic fluctuations. It may become insensitive compared with the direct measurement as we shown before, we selected the stability.

In this study, time series curve of the Mayer wave of vascular resistance was provided. This index was reported to be very useful for the parameter of the sympathetic nervous system. It was compared with time series curve of cardiac output. After a change of Mayer wave, increase in the cardiac output was observed after five seconds. This phenomenon may be interpreted that sympathetic nerve control the changes of the cardiac output. These results suggest that artificial heart may be controlled by the measurement of the Mayer wave of the vascular resistance.

Of course, development of another control algorithm for an artificial heart by the use of direct measurement has been still ongoing in Tohoku University. For example, we recorded the sympathetic nerve discharges in animals with artificial hearts for the first time in the world [7,8]. Some investigators carried out a supplementary examination and reported. Recently we measured the vagal nerve activity in the chronic animal experiments in the awake condition. It may give us an useful information to control an artificial heart in future. Trial for direct measurement of an autonomic nerve has been still ongoing, however, in this stage, indirect measurement may be desirable for an stability.

Mayer wave fluctuations in HRV was reported to be originated from the fluctuations in the arterial blood pressure [18]. If the blood pressure was changed, this information was detected in the baroreceptors in arterial wall and carotid sinus. This information was sent to the central nervous system (CNS). After that, orders were sent to the sinus nodes and peripheral vessels. If the sinus nodes and vascular resistances were altered, blood pressure were, of course, changed. And this information were sent to the CNS. Thus, it makes a negative feedback loop. In this study, we made a kind of feedback circuit by the use of the Mayer wave in resistances.

If we consider the nonlinear dynamics in circulation, feed back loop takes important role. Thus, we tried to make chaos in electrical simulation circuit with feedback loop and shown below.

Last modified:2006/04/10 22:43:46
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