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

Eric P. Thelin, Rahul Raj, Bo-Michael Bellander, David Nelson, Anna Piippo-Karjalainen, Jari Siironen, Päivi Tanskanen, Gregory Hawryluk, Mohammed Hasen, Bertram Unger, Frederick A. Zeiler

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

Sammanfattning

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 
Originalspråkengelska
TidskriftJournal of Clinical Monitoring and Computing
ISSN1573-2614
DOI
Status!!E-pub ahead of print - 1 okt 2019
MoE-publikationstypA1 Tidskriftsartikel-refererad

Vetenskapsgrenar

  • 3126 Kirurgi, anestesiologi, intensivvård, radiologi

Citera det här

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. / Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study. I: Journal of Clinical Monitoring and Computing. 2019.
@article{dcac2af7864d46219414db9040ee8406,
title = "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 ",
keywords = "3126 Surgery, anesthesiology, intensive care, radiology",
author = "Thelin, {Eric P.} and Rahul Raj and Bo-Michael Bellander and David Nelson and Anna Piippo-Karjalainen and Jari Siironen and P{\"a}ivi Tanskanen and Gregory Hawryluk and Mohammed Hasen and Bertram Unger and Zeiler, {Frederick A.}",
year = "2019",
month = "10",
day = "1",
doi = "10.1007/s10877-019-00392-y",
language = "English",
journal = "Journal of Clinical Monitoring and Computing",
issn = "1387-1307",
publisher = "Springer-Verlag",

}

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

I: Journal of Clinical Monitoring and Computing, 01.10.2019.

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

TY - JOUR

T1 - Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

AU - Thelin, Eric P.

AU - Raj, Rahul

AU - Bellander, Bo-Michael

AU - Nelson, David

AU - Piippo-Karjalainen, Anna

AU - Siironen, Jari

AU - Tanskanen, Päivi

AU - Hawryluk, Gregory

AU - Hasen, Mohammed

AU - Unger, Bertram

AU - Zeiler, Frederick A.

PY - 2019/10/1

Y1 - 2019/10/1

N2 - 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 

AB - 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 

KW - 3126 Surgery, anesthesiology, intensive care, radiology

U2 - 10.1007/s10877-019-00392-y

DO - 10.1007/s10877-019-00392-y

M3 - Article

JO - Journal of Clinical Monitoring and Computing

JF - Journal of Clinical Monitoring and Computing

SN - 1387-1307

ER -