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

• Published in: Journal of clinical monitoring and computing 2020-10, Vol.34 (5), p.971-994
• Main Authors: Thelin, Eric P., Raj, Rahul, Bellander, Bo-Michael, Nelson, David, Piippo-Karjalainen, Anna, Siironen, Jari, Tanskanen, Päivi, Hawryluk, Gregory, Hasen, Mohammed, Unger, Bertram, Zeiler, Frederick A.
• Format: Article
• Language: English
• Subjects: Anesthesiology; Autoregressive models; Brain Injuries, Traumatic; Cerebrovascular Circulation; Critical Care Medicine; Data capture; Data points; Economic models; Head injuries; Health Sciences; Humans; Impulse response; Intensive; Intracranial Pressure; Medicin och hälsovetenskap; Medicine; Medicine & Public Health; Original Research; Physiology; Pilot Projects; Prospective Studies; Reactivity; Response functions; Statistical analysis; Statistics for Life Sciences; Traumatic brain injury; Windows (intervals)
• ISSN: 1387-1307; 1573-2614; 1573-2614
• DOI: 10.1007/s10877-019-00392-y

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