Cardioventilatory Coupling in Critically Ill Patients

Overview

This study measures the cardioventilatory coupling in critically ill patients during mechanical ventilation in controlled mode (pressure controlled) and in patient-driven mode (pressure support and neurally adjusted ventilatory assist).

Intrathoracic pressure oscillations due to positive pressure ventilation induce cyclic modifications of activity of the pulmonary vascular receptors and cardiac mechanoceptors. These effects changes the autonomic nervous system modulation with modification of both sympathetic and vagal activity. Cardio-ventilatory interaction recently has been studied through the heart rate variability in critically ill mechanical ventilated patients. The analysis of the power spectrum density provides information about the power (the variance of beat-to-beat interval) is distributed as frequencies' function. Healthy spontaneously breathing subjects show a cyclic inspiratory increase and expiratory decrease of heart rate, and 'phase coupling' between heart beats and respiration (causing heart beats to occur at constant phases of the respiratory cycle), commonly known as cardioventilatory coupling. These phenomena originate from a complex interplay of several mechanisms including central drive, feedback from arterial baroreceptors, feedback from thoracic and lung stretch receptors, and non-neural mechanisms intrinsic to the heart, which are not fully understood. Although the physiological importance of respiratory sinus arrhythmia and cardioventilatory coupling have not been elucidated, several authors suggested that they might improve ventilation/perfusion matching through a redistribution of heart beats (and consequently of perfusion) within the respiratory cycle, with beneficial effects on gas exchange. Furthermore, decreased respiratory sinus arrhythmia amplitude has been used as an indicator of impaired autonomic control and of poor clinical outcome, also during mechanical ventilation.It has been shown that during controlled mechanical ventilation the respiratory sinus arrhythmia amplitude is considerably reduced and the cardioventilatory coupling generally abolished. Theoretically, mechanical ventilation modes that assist the respiratory pump upon triggering by the patient, such as pressure support ventilation (PSV) might maintain higher respiratory sinus arrhythmia levels through centrally-originated phasic vagal modulation compared to controlled mechanical ventilation. In addition, mechanical ventilation with breath-by-breath variable tidal volumes, so-called 'variable ventilation', could better preserve respiratory sinus arrhythmia and cardioventilatory coupling, and this might play a role in the improved arterial oxygenation found in different models of acute lung injury, although with variable results, when comparing variable and conventional mechanical ventilation. In anesthetized experimental animals the cardioventilatory coupling was more preserved during pressure assisted ventilation than pressure controlled ventilation. If these findings could be present in the humans is yet unknown. Furthermore, it has been demonstrated that some new methodologies of assisted ventilation, such as the neurally adjusted ventilatory assist (NAVA), increase the breath-to-breath variability and tidal volume variability, but their effects on cardioventilatory coupling are not understood. The study aims to measure the heart rate variability, the respiratory rate variability and the cardioventilatory coupling in critically ill patients during mechanical ventilation both in controlled mode (pressure controlled) and in assisted mode (pressure support ventilation and NAVA). Methods enrolled patients are connected to a S/5 ICU monitor (GE, Helsinki, Finland) and are mechanically ventilated with a Servo-I ventilator (Maquet, Germany) provided with diaphragmatic electrical activity module (EAdi, Maquet, Germany). A nasogastric 16-Fr EAdi catheter is positioned in all patients. A sequence of three consecutive study phases of different mechanical ventilation modes (PCV, PSV, and NAVA) is started. The sequence of ventilatory modes is randomized for every patient. After a 10 min acclimation period for each study phase, electrocardiographic, arterial pressure and ventilatory waves are collected for consecutive 30 min to a laptop pc via S/5 Collect (GE, Helsinki, Finland) and NAVA Tracker (Maquet, Germany) software for Windows. Heart rate variability analysis Linear analysis Sequences of 300 consecutives heart beats are selected inside each experimental phase. The mean and the variance of heart period are expressed in msec and msec\^2 respectively. Autoregressive spectral density is factorized into components each of them characterized by a central frequency. A spectral component is labeled as LF if its central frequency is between 0.04 and 0.15 Hz, while it is classified as HF if its central frequency is between 0.15 and 0.5 Hz. The HF power is considered to represent respiration-driven vagal modulation of heart rate. To rule out the effect of changes of total power spectrum densities on LF and HF components, spectral values are also expressed in normalized units (NU). Normalization consisted in dividing the power of a given spectral component by the total power minus the power below 0.04 Hz (Very Low Frequency \[VLF\] spectral component), and multiplying the ratio by 100. The ratio of the LF power to the HF (LF/HF) is considered an indicator of the balance between sympathetic and vagal modulation directed to the heart. Nonlinear analysis Non-linear analysis was conducted by means of symbolic analysis. The symbolic analysis is conducted on the same sequences of 300 consecutive heart beats that are used for the autoregressive analysis. The whole range of the R-to-R interval into each series is uniformly divided in 6 slices (symbols) and pattern of 3 consecutive heart beat intervals were considered. Thus each sequence of 300 heart beats had its own R-to-R range and 298 consecutive triplets of symbols. The Shannon entropy of the distribution of the patterns is calculated to provide a quantification of the complexity of the pattern distribution. All triplets of symbols are grouped into 3 possible patterns of variation: (i) no variation (0V, all 3 symbols were equal), (ii) 1 variation (1V, 2 consequent symbols were equal and the remaining symbol was different), (iii) patterns with 2 variations (2V, all symbols were different from the previous one). Previously, the percentage of 0V patterns was found to increase (and 2V decrease) in response to sympathetic stimuli, whereas 2V patterns increased (and 0V decreased) in response to vagal stimuli. The percentage of the patterns 0V and 2V was calculated, and the 0V/2V ratio was calculated to estimate the balance between sympathetic and vagal modulation. Cardioventilatory coupling analysis Phase synchronization method for quantification of coupling between weakly coupled self-sustained oscillators (here heart and ventilator) will be implemented. The idea behind is that a good ventilatory mode should produce cardio-respiratory coordination indistinguishable from that observed in healthy subject during spontaneous breathing at the same frequency and amplitude. Phase synchronization technique is complemented with joint symbolic analysis and cross-conditional entropy assessing of cardio-respiratory coupling in terms of degree of repeatability of coordination schemes between heart rate signal and ventilation. These approaches have the main advantages that they are less sensitive to non-stationarities and they are capable of capturing nonlinear couplings over shorter data sequences. Sample size given a heart rate variability total variance equal to 1000 msec\^2, to detect a difference of 300 msec\^2 (with a standard deviation of 200 msec\^2) between the study phases with an alpha error=0.05, power=80%, effect size=0.4, 22 patients will be recruited. The normal distribution is checked with Kolmogorov-Smirnov test. Two tails Student t test for dependent or independent sample as needed, or Wilcoxon- U test in case of not normal distribution are employed. Repeated measures are analyzed with one way analysis of variance (ANOVA) followed by post-hoc Bonferroni's test.

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Interventions

Eligibility

Locations (2)

Outcomes