• Frederick A Zeiler, Peter Smielewski, Joseph Donnelly, Marek Czosnyka, David K Menon, and Ari Ercole.
• 1 Division of Anaesthesia, Addenbrooke's Hospital, University of Cambridge , Cambridge, United Kingdom .
• J. Neurotrauma. 2018 Jul 15; 35 (14): 1559-1568.

AbstractThe study objective was to derive models that estimate the pressure reactivity index (PRx) using the noninvasive transcranial Doppler (TCD) based systolic flow index (Sx_a) and mean flow index (Mx_a), both based on mean arterial pressure, in traumatic brain injury (TBI). Using a retrospective database of 347 patients with TBI with intracranial pressure and TCD time series recordings, we derived PRx, Sx_a, and Mx_a. We first derived the autocorrelative structure of PRx based on: (A) autoregressive integrative moving average (ARIMA) modeling in representative patients, and (B) within sequential linear mixed effects (LME) models with various embedded ARIMA error structures for PRx for the entire population. Finally, we performed sequential LME models with embedded PRx ARIMA modeling to find the best model for estimating PRx using Sx_a and Mx_a. Model adequacy was assessed via normally distributed residual density. Model superiority was assessed via Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log likelihood (LL), and analysis of variance testing between models. The most appropriate ARIMA structure for PRx in this population was (2,0,2). This was applied in sequential LME modeling. Two models were superior (employing random effects in the independent variables and intercept): (A) PRx ∼ Sx_a, and (B) PRx ∼ Sx_a + Mx_a. Correlation between observed and estimated PRx with these two models was: (A) 0.794 (p < 0.0001, 95% confidence interval (CI) = 0.788-0.799), and (B) 0.814 (p < 0.0001, 95% CI = 0.809-0.819), with acceptable agreement on Bland-Altman analysis. Through using linear mixed effects modeling and accounting for the ARIMA structure of PRx, one can estimate PRx using noninvasive TCD-based indices. We have described our first attempts at such modeling and PRx estimation, establishing the strong link between two aspects of cerebral autoregulation: measures of cerebral blood flow and those of pulsatile cerebral blood volume. Further work is required to validate.

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