Objectives: In severe traumatic brain injury, cerebral perfusion pressure management based on cerebrovascular pressure reactivity index has the potential to provide a personalized treatment target to improve patient outcomes. So far, the methods have focused on identifying “one” autoregulation-guided cerebral perfusion pressure target—called “cerebral perfusion pressure optimal”. We investigated whether a cerebral perfusion pressure autoregulation range—which uses a continuous estimation of the “lower” and “upper” cerebral perfusion pressure limits of cerebrovascular pressure autoregulation (assessed with pressure reactivity index)—has prognostic value. Design: Single-center retrospective analysis of prospectively collected data. Setting: The neurocritical care unit at a tertiary academic medical center. Patients: Data from 729 severe traumatic brain injury patients admitted between 1996 and 2016 were used. Treatment was guided by controlling intracranial pressure and cerebral perfusion pressure according to a local protocol. Interventions: None. Methods and Main Results: Cerebral perfusion pressure-pressure reactivity index curves were fitted automatically using a previously published curve-fitting heuristic from the relationship between pressure reactivity index and cerebral perfusion pressure. The cerebral perfusion pressure values at which this “U-shaped curve” crossed the fixed threshold from intact to impaired pressure reactivity (pressure reactivity index = 0.3) were denoted automatically the “lower” and “upper” cerebral perfusion pressure limits of reactivity, respectively. The percentage of time with cerebral perfusion pressure below (%cerebral perfusion pressure < lower limit of reactivity), above (%cerebral perfusion pressure > upper limit of reactivity), or within these reactivity limits (%cerebral perfusion pressure within limits of reactivity) was calculated for each patient and compared across dichotomized Glasgow Outcome Scores. After adjusting for age, initial Glasgow Coma Scale, and mean intracranial pressure, percentage of time with cerebral perfusion pressure less than lower limit of reactivity was associated with unfavorable outcome (odds ratio %cerebral perfusion pressure < lower limit of reactivity, 1.04; 95% CI, 1.02–1.06; p < 0.001) and mortality (odds ratio, 1.06; 95% CI, 1.04–1.08; p < 0.001). Conclusions: Individualized autoregulation-guided cerebral perfusion pressure management may be a plausible alternative to fixed cerebral perfusion pressure threshold management in severe traumatic brain injury patients. Prospective randomized research will help define which autoregulation-guided method is beneficial, safe, and most practical.

Cerebral autoregulation (CA) is a protective mechanism that maintains cerebral blood flow at a relatively constant level despite fluctuations of cerebral perfusion pressure or arterial blood pressure. It is a universal physiological mechanism that may involve myogenic, neural control as well as metabolic regulations of cerebral vasculature in response to changes in pressure or cerebral blood flow. Traditionally, CA has been represented by a sigmoid curve with a wide plateau between about 50 mm Hg and 170 mm Hg of steady-state changes in mean arterial pressure, defined as static CA. With the advent of transcranial Doppler, measurement of cerebral blood flow in response to transient changes in arterial pressure has been used to assess dynamic CA. However, a gold standard for measuring CA is not currently available. Stroke has been the leading cause of long-term adult disability throughout the world. A better understanding of CA and its response to pathological derangements can help assess the severity of stroke, guide management decisions, assess response to interventions and provide prognostic information. The objective of this review is to provide a comprehensive insight about physiology of autoregulation, measurement methodologies and clinical applications in stroke to help build a consensus for what should be included in an internationally agreed protocol for CA testing and monitoring, and to promote its translation into clinical bedside practice for stroke management.

The laparoscopic approach is becoming increasingly frequent for many different surgical procedures. However, the combination of pneumoperitoneum and Trendelenburg positioning associated with this approach may increase the patient's risk for elevated intracranial pressure (ICP). Given that the gold standard for the measurement of ICP is invasive, little is known about the effect of these common procedures on ICP. We prospectively studied 40 patients without any history of cerebral disease who were undergoing laparoscopic procedures. Three different methods were used for non-invasive estimation of ICP: ultrasonography of the optic nerve sheath diameter (ONSD); transcranial Doppler-based (TCD) pulsatility index (ICPPI); and a method based on the diastolic component of the TCD cerebral blood flow velocity (ICPFVd). The ONSD and TCD were measured immediately after induction of general anaesthesia, after pneumoperitoneum insufflation, after Trendelenburg positioning, and again at the end of the procedure. The ONSD, ICPFVd, and ICPPI increased significantly after the combination of pneumoperitoneum insufflation and Trendelenburg positioning. The ICPFVd showed an area under the curve of 0.80 [95% confidence interval (CI) 0.70-0.90] to distinguish the stage associated with the application of pneumoperitoneum and Trendelenburg position; ONSD and ICPPI showed an area under the curve of 0.75 (95% CI 0.65-0.86) and 0.70 (95% CI 0.58-0.81), respectively. The concomitance of pneumoperitoneum and the Trendelenburg position can increase ICP as estimated with non-invasive methods. In high-risk patients undergoing laparoscopic procedures, non-invasive ICP monitoring through a combination of ONSD ultrasonography and TCD-derived ICPFVd could be a valid option to assess the risk of increased ICP.

Objectives: Autonomic impairment after acute traumatic brain injury has been associated independently with both increased morbidity and mortality. Links between autonomic impairment and increased intracranial pressure or impaired cerebral autoregulation have been described as well. However, relationships between autonomic impairment, intracranial pressure, impaired cerebral autoregulation, and outcome remain poorly explored. Using continuous measurements of heart rate variability and baroreflex sensitivity we aimed to test whether autonomic markers are associated with functional outcome and mortality independently of intracranial variables. Further, we aimed to evaluate the relationships between autonomic functions, intracranial pressure, and cerebral autoregulation. Design: Retrospective analysis of a prospective database. Setting: Neurocritical care unit in a university hospital. Subjects: Sedated patients with severe traumatic brain injury. Measurements and Main Results: Waveforms of intracranial pressure and arterial blood pressure, baseline Glasgow Coma Scale and 6 months Glasgow Outcome Scale were recorded. Baroreflex sensitivity was assessed every 10 seconds using a modified cross-correlational method. Frequency domain analyses of heart rate variability were performed automatically every 10 seconds from a moving 300 seconds of the monitoring time window. Mean values of baroreflex sensitivity, heart rate variability, intracranial pressure, arterial blood pressure, cerebral perfusion pressure, and impaired cerebral autoregulation over the entire monitoring period were calculated for each patient. Two hundred and sixty-two patients with a median age of 36 years entered the analysis. The median admission Glasgow Coma Scale was 6, the median Glasgow Outcome Scale was 3, and the mortality at 6 months was 23%. Baroreflex sensitivity (adjusted odds ratio, 0.9; p = 0.02) and relative power of a high frequency band of heart rate variability (adjusted odds ratio, 1.05; p < 0.001) were individually associated with mortality, independently of age, admission Glasgow Coma Scale, intracranial pressure, pressure reactivity index, or cerebral perfusion pressure. Baroreflex sensitivity showed no correlation with intracranial pressure or cerebral perfusion pressure; the correlation with pressure reactivity index was strong in older patients (age, > 60 yr). The relative power of high frequency correlated significantly with intracranial pressure and cerebral perfusion pressure, but not with pressure reactivity index. The relative power of low frequency correlated significantly with pressure reactivity index. Conclusions: Autonomic impairment, as measured by heart rate variability and baroreflex sensitivity, is significantly associated with increased mortality after traumatic brain injury. These effects, though partially interlinked, seem to be independent of age, trauma severity, intracranial pressure, or autoregulatory status, and thus represent a discrete phenomenon in the pathophysiology of traumatic brain injury. Continuous measurements of heart rate variability and baroreflex sensitivity in the neuromonitoring setting of severe traumatic brain injury may carry novel pathophysiological and predictive information.

Elevation of intracranial pressure (ICP) may occur in many diseases, and therefore the ability to measure it noninvasively would be useful. Flow velocity signals from transcranial Doppler (TCD) have been used to estimate ICP; however, the relative accuracy of these methods is unclear. This study aimed to compare four previously described TCD-based methods with directly measured ICP in a prospective cohort of traumatic brain-injured patients. Noninvasive ICP (nICP) was obtained using the following methods: 1) a mathematical “black-box” model based on interaction between TCD and arterial blood pressure (nICP_BB); 2) based on diastolic flow velocity (nICP_FVd); 3) based on critical closing pressure (nICP_CrCP); and 4) based on TCD-derived pulsatility index (nICP_PI). In time domain, for recordings including spontaneous changes in ICP greater than 7 mm Hg, nICP_PI showed the best correlation with measured ICP (R = 0.61). Considering every TCD recording as an independent event, nICP_BB generally showed to be the best estimator of measured ICP (R = 0.39; p < 0.05; 95% confidence interval [CI] = 9.94 mm Hg; area under the curve [AUC] = 0.66; p < 0.05). For nICP_FVd, although it presented similar correlation coefficient to nICP_BB and marginally better AUC (0.70; p < 0.05), it demonstrated a greater 95% CI for prediction of ICP (14.62 mm Hg). nICP_CrCP presented a moderate correlation coefficient (R = 0.35; p < 0.05) and similar 95% CI to nICP_BB (9.19 mm Hg), but failed to distinguish between normal and raised ICP (AUC = 0.64; p > 0.05). nICP_PI was not related to measured ICP using any of the above statistical indicators. We also introduced a new estimator (nICP_Av) based on the average of three methods (nICP_BB, nICP_FVd, and nICP_CrCP), which overall presented improved statistical indicators (R = 0.47; p < 0.05; 95% CI = 9.17 mm Hg; AUC = 0.73; p < 0.05). nICP_PI appeared to reflect changes in ICP in time most accurately. nICP_BB was the best estimator for ICP “as a number.” nICP_Av demonstrated to improve the accuracy of measured ICP estimation.

The impulse response (IR)-based autoregulation index (ARI) allows for continuous monitoring of cerebral autoregulation using spontaneous fluctuations of arterial blood pressure (ABP) and cerebral flow velocity (FV). We compared three methods of autoregulation assessment in 288 traumatic brain injury (TBI) patients managed in the Neurocritical Care Unit: (1) IR-based ARI; (2) transfer function (TF) phase, gain, and coherence; and (3) mean flow index (Mx). Autoregulation index was calculated using the TF estimation (Welch method) and classified according to the original Tiecks’ model. Mx was calculated as a correlation coefficient between 10-second averages of ABP and FV using a moving 300-second data window. Transfer function phase, gain, and coherence were extracted in the very low frequency (VLF, 0 to 0.05 Hz) and low frequency (LF, 0.05 to 0.15 Hz) bandwidths. We studied the relationship between these parameters and also compared them with patients’ Glasgow outcome score. The calculations were performed using both cerebral perfusion pressure (CPP; suffix ‘c’) as input and ABP (suffix ‘a’). The result showed a significant relationship between ARI and Mx when using either ABP ( r=−0.38, P<0.001) or CPP ( r=−0.404, P<0.001) as input. Transfer function phase and coherence_a were significantly correlated with ARI_a and ARI_c ( P<0.05). Only ARI_a, ARI_c, Mx_a, Mx_c, and phase_c were significantly correlated with patients’ outcome, with Mx_c showing the strongest association.