Impact of propofol general anesthesia on cardiovascular and cerebrovascular closed loop variability interactions

Link Between Cardiovascular and Cerebrovascular Controls in Patients Undergoing Surgical Aortic Valve Replacement

Although the autonomic influence on cardiovascular (CV) and cerebrovascular (CBV) regulations has been widely recognized, their relationship is poorly explored especially in pathological situations. This study investigates the correlation between CV and CBV markers in 73 patients (48 males, age 63.6±13.1 yrs) undergoing surgical aortic valve replacement (SAVR) evaluated before the intervention (PRE), within 1-week post-surgery (POST) and after a 3-month follow-up (POST3). Patients were acquired before and after an orthostatic challenge. Frequency domain analysis assessing transfer function gain (TFG), phase (Ph), and squared coherence (K2) between heart period and systolic arterial pressure was exploited to evaluate CV control. The same frequency domain functions were derived to assess CBV regulation from mean cerebral blood velocity and mean arterial pressure. A correlation analysis between indexes of CV and CBV controls was carried out. Results showed that CV control was impaired in PRE, worsened in POST, and recovered in POST3, while CBV markers were almost unchanged. A significant positive relationship between CV and CBV markers was observed, especially in POST and POST3, thus suggesting that the compensation of a baroreflex impairment with a more efficient CBV control and vice versa. The maintenance of this relationship between CV and CBV controls in patients undergoing SAVR could be fundamental to prevent risky situations.Clinical Relevance- After surgical aortic valve replacement an impaired baroreflex control could be compensated by a more efficient cerebral autoregulation.

On the Different Abilities of Cross-Sample Entropy and K-Nearest-Neighbor Cross-Unpredictability in Assessing Dynamic Cardiorespiratory and Cerebrovascular Interactions

Nonlinear markers of coupling strength are often utilized to typify cardiorespiratory and cerebrovascular regulations. The computation of these indices requires techniques describing nonlinear interactions between respiration (R) and heart period (HP) and between mean arterial pressure (MAP) and mean cerebral blood velocity (MCBv). We compared two model-free methods for the assessment of dynamic HP-R and MCBv-MAP interactions, namely the cross-sample entropy (CSampEn) and k-nearest-neighbor cross-unpredictability (KNNCUP). Comparison was carried out first over simulations generated by linear and nonlinear unidirectional causal, bidirectional linear causal, and lag-zero linear noncausal models, and then over experimental data acquired from 19 subjects at supine rest during spontaneous breathing and controlled respiration at 10, 15, and 20 breaths·minute-1 as well as from 13 subjects at supine rest and during 60° head-up tilt. Linear markers were computed for comparison. We found that: (i) over simulations, CSampEn and KNNCUP exhibit different abilities in evaluating coupling strength; (ii) KNNCUP is more reliable than CSampEn when interactions occur according to a causal structure, while performances are similar in noncausal models; (iii) in healthy subjects, KNNCUP is more powerful in characterizing cardiorespiratory and cerebrovascular variability interactions than CSampEn and linear markers. We recommend KNNCUP for quantifying cardiorespiratory and cerebrovascular coupling.

Spectral decomposition of cerebrovascular and cardiovascular interactions in patients prone to postural syncope and healthy controls

We present a framework for the linear parametric analysis of pairwise interactions in bivariate time series in the time and frequency domains, which allows the evaluation of total, causal and instantaneous interactions and connects time- and frequency-domain measures. The framework is applied to physiological time series to investigate the cerebrovascular regulation from the variability of mean cerebral blood flow velocity (CBFV) and mean arterial pressure (MAP), and the cardiovascular regulation from the variability of heart period (HP) and systolic arterial pressure (SAP). We analyze time series acquired at rest and during the early and late phase of head-up tilt in subjects developing orthostatic syncope in response to prolonged postural stress, and in healthy controls. The spectral measures of total, causal and instantaneous coupling between HP and SAP, and between MAP and CBFV, are averaged in the low-frequency band of the spectrum to focus on specific rhythms, and over all frequencies to get time-domain measures. The analysis of cardiovascular interactions indicates that postural stress induces baroreflex involvement, and its prolongation induces baroreflex dysregulation in syncope subjects. The analysis of cerebrovascular interactions indicates that the postural stress enhances the total coupling between MAP and CBFV, and challenges cerebral autoregulation in syncope subjects, while the strong sympathetic activation elicited by prolonged postural stress in healthy controls may determine an increased coupling from CBFV to MAP during late tilt. These results document that the combination of time-domain and spectral measures allows us to obtain an integrated view of cardiovascular and cerebrovascular regulation in healthy and diseased subjects.

Exploring metrics for the characterization of the cerebral autoregulation during head-up tilt and propofol general anesthesia

Techniques grounded on the simultaneous utilization of Tiecks' second order differential equations and spontaneous variability of mean arterial pressure (MAP) and mean cerebral blood flow velocity (MCBFV), recorded from middle cerebral arteries through a transcranial Doppler device, provide a characterization of cerebral autoregulation (CA) via the autoregulation index (ARI). These methods exploit two metrics for comparing the measured MCBFV series with the version predicted by Tiecks' model: normalized mean square prediction error (NMSPE) and normalized correlation ρ. The aim of this study is to assess the two metrics for ARI computation in 13 healthy subjects (age: 27 ± 8 yrs., 5 males) at rest in supine position (REST) and during 60° head-up tilt (HUT) and in 19 patients (age: 64 ± 8 yrs., all males), scheduled for coronary artery bypass grafting, before (PRE) and after (POST) propofol general anesthesia induction. Analyses were carried out over the original MAP and MCBFV pairs and surrogate unmatched couples built individually via time-shifting procedure. We found that: i) NMSPE and ρ metrics exhibited similar performances in passing individual surrogate test; ii) the two metrics could lead to different ARI estimates; iii) CA was not different during HUT or POST compared to baseline and this conclusion held regardless of the technique and metric for ARI estimation. Results suggest a limited impact of the sympathetic control on CA.

Monitoring the Evolution of Asynchrony between Mean Arterial Pressure and Mean Cerebral Blood Flow via Cross-Entropy Methods

Cerebrovascular control is carried out by multiple nonlinear mechanisms imposing a certain degree of coupling between mean arterial pressure (MAP) and mean cerebral blood flow (MCBF). We explored the ability of two nonlinear tools in the information domain, namely cross-approximate entropy (CApEn) and cross-sample entropy (CSampEn), to assess the degree of asynchrony between the spontaneous fluctuations of MAP and MCBF. CApEn and CSampEn were computed as a function of the translation time. The analysis was carried out in 23 subjects undergoing recordings at rest in supine position (REST) and during active standing (STAND), before and after surgical aortic valve replacement (SAVR). We found that at REST the degree of asynchrony raised, and the rate of increase in asynchrony with the translation time decreased after SAVR. These results are likely the consequence of the limited variability of MAP observed after surgery at REST, more than the consequence of a modified cerebrovascular control, given that the observed differences disappeared during STAND. CApEn and CSampEn can be utilized fruitfully in the context of the evaluation of cerebrovascular control via the noninvasive acquisition of the spontaneous MAP and MCBF variability.

Categorizing the Role of Respiration in Cardiovascular and Cerebrovascular Variability Interactions

OBJECTIVE

Respiration disturbs cardiovascular and cerebrovascular controls but its role is not fully elucidated.

METHODS

Respiration can be classified as a confounder if its observation reduces the strength of the causal relationship from source to target. Respiration is a suppressor if the opposite situation holds. We prove that a confounding/suppression (C/S) test can be accomplished by evaluating the sign of net redundancy/synergy balance in the predictability framework based on multivariate autoregressive modelling. In addition, we suggest that, under the hypothesis of Gaussian processes, the C/S test can be given in the transfer entropy decomposition framework as well. Experimental protocols: We applied the C/S test to variability series of respiratory movements, heart period, systolic arterial pressure, mean arterial pressure, and mean cerebral blood flow recorded in 17 pathological individuals (age: 648 yrs; 17 males) before and after induction of propofol-based general anesthesia prior to coronary artery bypass grafting, and in 13 healthy subjects (age: 278 yrs; 5 males) at rest in supine position and during head-up tilt with a table inclination of 60.

RESULTS

Respiration behaved systematically as a confounder for cardiovascular and cerebrovascular controls. In addition, its role was affected by propofol-based general anesthesia but not by a postural stimulus of limited intensity.

CONCLUSION

The C/S test can be fruitfully exploited to categorize the role of respiration over causal variability interactions.

SIGNIFICANCE

The application of the C/S test could favor the comprehension of the role of respiration in cardiovascular and cerebrovascular regulations.

Respiration is a Confounder of the Closed Loop Relationship Between Mean Arterial Pressure and Mean Cerebral Blood Flow

This study tested the hypothesis that respiration (RESP) is a confounder or suppressor of the closed loop relationship responsible for the cerebrovascular dynamical interactions as assessed from spontaneous variability of mean arterial pressure (MAP) and mean cerebral blood flow (MCBF). The evaluation was carried out in the information domain via transfer entropy (TE) estimated through a linear model-based approach comparing TE markers computed solely over MAP and MCBF series with TE indexes accounting for the eventual action of RESP over MAP and MCBF. We considered 11 patients (age: 76±5 yrs, 7 males) undergoing surgical aortic valve replacement (SAVR) at supine resting (REST) and during active standing (STAND) before and after SAVR surgery. The decrease of the predictive ability of MCBF to MAP when accounting for RESP compared to the one assessed when disregarding RESP suggested that RESP is a confounder of the link from MCBF to MAP along the Cushing reflex instead of being a suppressor. This result was more evident in POST when autonomic control was dramatically depressed and in an unchallenged condition such as REST. RESP did not affect significantly the link from MAP to MCBF along the pressure-to-flow relationship. Clarification of the type of RESP influence on the MAP-MCBF closed loop relationship could favor a deeper characterization of cerebrovascular interactions and the comprehension of cerebral autoregulation mechanisms.Clinical Relevance- This study suggests that respiration is a confounder of the closed loop relationship between MAP and MCBF, especially of the flow-to-pressure causal link. This result might open new possibilities in elucidating the mechanisms of cerebral autoregulation in healthy and pathological populations.