Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

Abstract

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p < 0.0001 for each patient). Thus, these particular L-PRx variants appear closest in nature to standard PRx. ICP and MAP derived via 10-s or minute based averaging display similar statistical time-series structure and co-variance patterns. PRx and L-PRx based on shorter windows also behave similarly over time. These results imply certain L-PRx variants may carry similar information to PRx in TBI.

Evaluation of the relationship between slow-waves of intracranial pressure, mean arterial pressure and brain tissue oxygen in TBI: a CENTER-TBI exploratory analysis

“…Second, Granger causality analysis confirmed the strong directional relationship of MAP on ICP, as seen in previous work on the topic [29,30,34]. A similar directional relationship was seen on Granger testing for MAP on PbtO 2 , though the magnitude of the F-statistic was much smaller than that see for the MAP on ICP relationship.…”

Effects of Norepinephrine, Propofol, and Hemoglobin Concentration on Dynamic Measurements of Cerebrovascular Reactivity in Acute Brain Injury

“…Time series modeling and analysis have been proven to be very useful for continuous datasets assessing cerebrovascular reactivity. [20][21][22][23] The dependence of (patho-) physiological variables to each other is not always instantaneous, and often the response occurs after a certain lapse of time (lag). A time-series lagged correlation analysis is therefore well suited to study large datasets from patients in the intensive care unit and identify delayed correlations between two physiological variables.…”

“…26,27 LAx is based on longer time windows than PRx and theoretically is outside of the vasogenic frequency range associated with CA, but has been proven to provide useful information on cerebrovascular reactivity and patient outcome. 15,22 Third, due to the retrospective nature of the data we cannot independently analyze the effect of one variable on cerebrovascular reactivity. Therefore, it cannot be completely ruled out that our results are biased due to concurrent changes of other variables (e.g., concurrent changes in propofol and norepinephrine infusion rates).…”

The Impact of Vasopressor and Sedative Agents on Cerebrovascular Reactivity and Compensatory Reserve in Traumatic Brain Injury: An Exploratory Analysis

“…These methods are similar to those of prior works in the field. 10,14,28 Signal processing The following signal processing occurred using similar methodology, covered in other publications by our group and the senior author. 10,14,28 CPP was calculated as mean arterial pressure (MAP)-ICP.…”

“…10,14,28 Signal processing The following signal processing occurred using similar methodology, covered in other publications by our group and the senior author. 10,14,28 CPP was calculated as mean arterial pressure (MAP)-ICP. Pulse amplitude of ICP (AMP) was derived from the fundamental amplitude of the ICP waveform in the frequency domain, using Fourier analysis over a non-overlapping 10-sec moving window.…”