Characterization of Brain-Heart Interactions in a Rodent Model of Sepsis.

Loss of heart rate variability (HRV) and autonomic dysfunction are associated with poor outcomes in critically ill patients. Neuronal networks comprising brainstem and hypothalamus are involved in the "flight-or-fight" response via control over the autonomic nervous system and circulation. We hypothesized that sepsis-induced inflammation in brain regions responsible for autonomic control is associated with sympathovagal imbalance and depressed contractility. Sepsis was induced by fecal slurry injection in fluid-resuscitated rats. Sham-operated animals served as controls. Echocardiography-derived peak velocity (PV) was used to separate septic animals into good (PV ≥0.93 m/s, low 72-h mortality) and bad (PV <0.93, high 72-h mortality) prognosis. Cytokine protein levels were assessed by ELISA. All experiments were performed at 24 h post-insult. Increased levels of inflammation and oxidative injury were observed in the hypothalamus (TNF-α, IL-10, nitrite and nitrate and carbonyl groups) and brainstem (IL-1, IL-6, IL-10, nitrite and nitrate and carbonyl groups) of the septic animals (p < 0.05 vs. sham), but not in the pre-frontal cortex, an area not directly implicated in control of the autonomic nervous system. Good prognosis septic animals had increased sympathetic output and increased left ventricular contractility (p < 0.05 vs. sham). There was a significant inverse correlation between high frequency power (a marker of parasympathetic outflow) and contractility (r = -0.73, p < 0.05). We found no correlation between the degree of inflammation or injury to autonomic centers and cardiovascular function. In conclusion, control of autonomic centers and cardiac function in our long-term rodent model of sepsis was related to clinical severity but not directly to the degree of inflammation.

Heart period and blood pressure characteristics in splanchnic arterial occlusion shock-induced collapse.

The nature of hemodynamic instability typical of circulatory shock is not well understood, but an improved interpretation of its dynamic features could help in the management of critically ill patients. The objective of this work was to introduce new metrics for the analysis of arterial blood pressure (ABP) in order to characterize the risk of catastrophic outcome in splanchnic arterial occlusion (SAO) shock. Continuous ABP (fs = 1 kHz) was measured in rats during experimental SAO shock, which induced a fatal pressure drop (FPD) in ABP. The FPD could either be slow (SFPD) or fast (FFPD), with the latter causing cardiovascular collapse. Time series of mean arterial pressure, systolic blood pressure and heart period were derived from ABP. The sample asymmetry-based algorithm Heart Rate Characteristics was adapted to compute the Heart Period Characteristics (HPC) and the Blood Pressure Characteristics (BPC). Baroreflex sensitivity (BRS) was assessed by means of a bivariate model. The approach to FPD of the animals who collapsed (FFPD) was characterized by higher BRS in the low frequency band versus SFPD animals (0.36 ± 0.15 vs. 0.19 ± 0.12 ms/mmHg, p value = 0.0196), bradycardia as indicated by the HPC (0.76 ± 0.57 vs. 1.94 ± 1.27, p value = 0.0179) and higher but unstable blood pressure as indicated by BPC (3.02 ± 2.87 vs. 1.47 ± 1.29, p value = 0.0773). The HPC and BPC indices demonstrated promise as potential clinical markers of hemodynamic instability and impending cardiovascular collapse, and this animal study suggests their test in data from intensive care patients.

A review of methods for the signal quality assessment to improve reliability of heart rate and blood pressures derived parameters.

The assessment of signal quality has been a research topic since the late 1970s, as it is mainly related to the problem of false alarms in bedside monitors in the intensive care unit (ICU), the incidence of which can be as high as 90 %, leading to alarm fatigue and a drop in the overall level of nurses and clinicians attention. The development of efficient algorithms for the quality control of long diagnostic electrocardiographic (ECG) recordings, both single- and multi-lead, and of the arterial blood pressure (ABP) signal is therefore essential for the enhancement of care quality. The ECG signal is often corrupted by noise, which can be within the frequency band of interest and can manifest similar morphologies as the ECG itself. Similarly to ECG, also the ABP signal is often corrupted by non-Gaussian, nonlinear and non-stationary noise and artifacts, especially in ICU recordings. Moreover, the reliability of several important parameters derived from ABP such as systolic blood pressure or pulse pressure is strongly affected by the quality of the ABP waveform. In this work, several up-to-date algorithms for the quality scoring of a single- or multi-lead ECG recording, based on time-domain approaches, frequency-domain approaches or a combination of the two will be reviewed, as well as methods for the quality assessment of ABP. Additionally, algorithms exploiting the relationship between ECG and pulsatile signals, such as ABP and photoplethysmographic recordings, for the reduction in the false alarm rate will be presented. Finally, some considerations will be drawn taking into account the large heterogeneity of clinical settings, applications and goals that the reviewed algorithms have to deal with.