Non-Invasive Pressure Reactivity Index Using Doppler Systolic Flow Parameters: A Pilot Analysis

Frederick A. Zeiler , Peter Smielewski , Andrew Stevens , Marek Czosnyka , D.K. Menon , Ari Ercole

Abstract

The goal was to predict pressure reactivity index (PRx) using non-invasive transcranial Doppler (TCD) based indices of cerebrovascular reactivity, systolic flow index (Sx_a), and mean flow index (Mx_a). Continuous extended duration time series recordings of middle cerebral artery cerebral blood flow velocity (CBFV) were obtained using robotic TCD in parallel with direct intracranial pressure (ICP). PRx, Sx_a, and Mx_a were derived from high frequency archived signals. Using time-series techniques, autoregressive integrative moving average (ARIMA) structure of PRx was determined and embedded in the following linear mixed effects (LME) models of PRx: PRx ∼ Sx_a and PRx ∼ Sx_a + Mx_a. Using 80% of the recorded patient data, the LME models were created and trained. Model superiority was assessed via Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood (LL). The superior two models were then used to predict PRx using the remaining 20% of the signal data. Predicted and observed PRx were compared via Pearson correlation, linear models, and Bland–Altman (BA) analysis. Ten patients had 3–4 h of continuous uninterrupted ICP and TCD data and were used for this pilot analysis. Optimal ARIMA structure for PRx was determined to be (2,0,2), and this was embedded in all LME models. The top two LME models of PRx were determined to be: PRx ∼ Sx_a and PRx ∼ Sx_a + Mx_a. Estimated and observed PRx values from both models were strongly correlated (r > 0.9; p < 0.0001 for both), with acceptable agreement on BA analysis. Predicted PRx using these two models was also moderately correlated with observed PRx, with acceptable agreement (r = 0.797, p = 0.006; r = 0.763, p = 0.011; respectively). With application of ARIMA and LME modeling, it is possible to predict PRx using non-invasive TCD measures. These are the first and as well as being preliminary attempts at doing so. Much further work is required.
Author Frederick A. Zeiler - [Addenbrooke's Hospital [University of Cambridge (CAM)]]
Frederick A. Zeiler,,
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- Addenbrooke's Hospital
, Peter Smielewski - [University of Cambridge]
Peter Smielewski,,
-
-
, Andrew Stevens - [Addenbrooke's Hospital [University of Cambridge (CAM)]]
Andrew Stevens,,
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- Addenbrooke's Hospital
, Marek Czosnyka (FEIT / PE)
Marek Czosnyka,,
- The Institute of Electronic Systems
, D.K. Menon - [Addenbrooke's Hospital [University of Cambridge (CAM)]]
D.K. Menon,,
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- Addenbrooke's Hospital
, Ari Ercole - [Addenbrooke's Hospital [University of Cambridge (CAM)]]
Ari Ercole,,
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- Addenbrooke's Hospital
Journal seriesJournal of Neurotrauma, ISSN 0897-7151, (A 35 pkt)
Issue year2019
Vol36
No5
Pages713-720
Publication size in sheets0.5
Keywords in Englishautoregulationbrain; injury; TBI; TCD; time series
ASJC Classification2728 Clinical Neurology
DOIDOI:10.1089/neu.2018.5987
URL https://www.liebertpub.com/doi/10.1089/neu.2018.5987
Languageen angielski
Score (nominal)35
ScoreMinisterial score = 35.0, 05-09-2019, ArticleFromJournal
Publication indicators Scopus Citations = 0; WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.534; WoS Impact Factor: 2017 = 5.002 (2) - 2017=5.063 (5)
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