Extended Analysis of Axonal Injuries Detected Using Magnetic Resonance Imaging in Critically Ill Traumatic Brain Injury Patients

Publisher Copyright: © Jonathan Tjerkaski et al., 2022; Published by Mary Ann Liebert, Inc. 2022.Studies show conflicting results regarding the prognostic significance of traumatic axonal injuries (TAI) in patients with traumatic brain injury (TBI). Therefore, we documented the presence of TAI in several brain regions, using different magnetic resonance imaging (MRI) sequences, and assessed their association to patient outcomes using machine learning. Further, we created a novel MRI-based TAI grading system with the goal of improving outcome prediction in TBI. We subsequently evaluated the performance of several TAI grading systems. We used a genetic algorithm to identify TAI that distinguish favorable from unfavorable outcomes. We assessed the discriminatory performance (area under the curve [AUC]) and goodness-of-fit (Nagelkerke pseudo-R2) of the novel Stockholm MRI grading system and the TAI grading systems of Adams and associates, Firsching and coworkers. and Abu Hamdeh and colleagues, using both univariate and multi-variate logistic regression. The dichotomized Glasgow Outcome Scale was considered the primary outcome. We examined the MRI scans of 351 critically ill patients with TBI. The TAI in several brain regions, such as the midbrain tegmentum, were strongly associated with unfavorable outcomes. The Stockholm MRI grading system exhibited the highest AUC (0.72 vs. 0.68-0.69) and Nagelkerke pseudo-R2 (0.21 vs. 0.14-0.15) values of all TAI grading systems. These differences in model performance, however, were not statistically significant (DeLong test, p > 0.05). Further, all included TAI grading systems improved outcome prediction relative to established outcome predictors of TBI, such as the Glasgow Coma Scale (likelihood-ratio test, p < 0.001). Our findings suggest that the detection of TAI using MRI is a valuable addition to prognostication in TBI.Peer reviewe

Analyses of cerebral microdialysis in patients with traumatic brain injury: relations to intracranial pressure, cerebral perfusion pressure and catheter placement

<p>Abstract</p> <p>Background</p> <p>Cerebral microdialysis (MD) is used to monitor local brain chemistry of patients with traumatic brain injury (TBI). Despite an extensive literature on cerebral MD in the clinical setting, it remains unclear how individual levels of real-time MD data are to be interpreted. Intracranial pressure (ICP) and cerebral perfusion pressure (CPP) are important continuous brain monitors in neurointensive care. They are used as surrogate monitors of cerebral blood flow and have an established relation to outcome. The purpose of this study was to investigate the relations between MD parameters and ICP and/or CPP in patients with TBI.</p> <p>Methods</p> <p>Cerebral MD, ICP and CPP were monitored in 90 patients with TBI. Data were extensively analyzed, using over 7,350 samples of complete (hourly) MD data sets (glucose, lactate, pyruvate and glycerol) to seek representations of ICP, CPP and MD that were best correlated. MD catheter positions were located on computed tomography scans as pericontusional or nonpericontusional. MD markers were analyzed for correlations to ICP and CPP using time series regression analysis, mixed effects models and nonlinear (artificial neural networks) computer-based pattern recognition methods.</p> <p>Results</p> <p>Despite much data indicating highly perturbed metabolism, MD shows weak correlations to ICP and CPP. In contrast, the autocorrelation of MD is high for all markers, even at up to 30 future hours. Consequently, subject identity alone explains 52% to 75% of MD marker variance. This indicates that the dominant metabolic processes monitored with MD are long-term, spanning days or longer. In comparison, short-term (differenced or Δ) changes of MD vs. CPP are significantly correlated in pericontusional locations, but with less than 1% explained variance. Moreover, CPP and ICP were significantly related to outcome based on Glasgow Outcome Scale scores, while no significant relations were found between outcome and MD.</p> <p>Conclusions</p> <p>The multitude of highly perturbed local chemistry seen with MD in patients with TBI predominately represents long-term metabolic patterns and is weakly correlated to ICP and CPP. This suggests that disturbances other than pressure and/or flow have a dominant influence on MD levels in patients with TBI.</p

Missing data.

<p>The amount of missing data was low and was imputed using multiple imputations.</p><p>Missing data.</p

The within patient changes of serum (A) and CSF (B)-NF-L illustrated using histograms of logged data.

<p>The differences over time for each patient were low, with a majority of patients not diverging more than 3.2 ng/L in serum (Fig 5A) and more than 10 ng/L in CSF (Fig 5B).</p

Patient demographics.

<p>Illustrating patient demographics including CT, MRI, biomarker and outcome data. Reference concentrations from healthy controls (Ctrl) are presented for each biomarker in serum and CSF [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132177#pone.0132177.ref044" target="_blank">44</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132177#pone.0132177.ref047" target="_blank">47</a>].</p><p>* = median, IQR</p><p>‡ = mean, SD</p><p>Patient demographics.</p

Both serum-S100B and –NF-L correlate to TBI outcome.

<p>Serum levels of S100B (A) and NF-L (B) (x-axis, respectively) vs Glasgow Outcome Score (GOS) (y-axis, left) shown using conditional density plots. The red line represents the data distribution. Outcome proportions are illustrated, summing to one (y-axis right).</p

Univariate proportional odds analysis of parameters versus Glasgow Outcome Score.

<p>Individual parameters and correlation (pseudo-R²) toward long term functional outcome (GOS).</p><p>Univariate proportional odds analysis of parameters versus Glasgow Outcome Score.</p

Characteristics of serum NF-L samples.

<p>Histograms illustrating the number of s-NF-L samples per patient (A) and the distribution over time after trauma (B). C illustrates the s-NF-L levels over time (one dot per sample), with a red line representing the locally weighted scatterplot smoothing (LOWESS), a nonlinear regression of data points. D illustrates the s-NF-L levels over time after trauma using boxplots.</p

Correlations between CSF and serum samples of NF-L are illustrated.

<p>One color represents one patient (A). The mean CSF and serum samples were calculated and correlated (B).</p