Current state of high-fidelity multimodal monitoring in traumatic brain injury

Introduction Multimodality monitoring of patients with severe traumatic brain injury (TBI) is primarily performed in neurocritical care units to prevent secondary harmful brain insults and facilitate patient recovery. Several metrics are commonly monitored using both invasive and non-invasive techniques. The latest Brain Trauma Foundation guidelines from 2016 provide recommendations and thresholds for some of these. Still, high-level evidence for several metrics and thresholds is lacking. Methods Regarding invasive brain monitoring, intracranial pressure (ICP) forms the cornerstone, and pressures above 22 mmHg should be avoided. From ICP, cerebral perfusion pressure (CPP) (mean arterial pressure (MAP)-ICP) and pressure reactivity index (PRx) (a correlation between slow waves MAP and ICP as a surrogate for cerebrovascular reactivity) may be derived. In terms of regional monitoring, partial brain tissue oxygen pressure (PbtO(2)) is commonly used, and phase 3 studies are currently ongoing to determine its added effect to outcome together with ICP monitoring. Cerebral microdialysis (CMD) is another regional invasive modality to measure substances in the brain extracellular fluid. International consortiums have suggested thresholds and management strategies, in spite of lacking high-level evidence. Although invasive monitoring is generally safe, iatrogenic hemorrhages are reported in about 10% of cases, but these probably do not significantly affect long-term outcome. Non-invasive monitoring is relatively recent in the field of TBI care, and research is usually from single-center retrospective experiences. Near-infrared spectrometry (NIRS) measuring regional tissue saturation has been shown to be associated with outcome. Transcranial doppler (TCD) has several tentative utilities in TBI like measuring ICP and detecting vasospasm. Furthermore, serial sampling of biomarkers of brain injury in the blood can be used to detect secondary brain injury development. Conclusions In multimodal monitoring, the most important aspect is data interpretation, which requires knowledge of each metric's strengths and limitations. Combinations of several modalities might make it possible to discern specific pathologic states suitable for treatment. However, the cost-benefit should be considered as the incremental benefit of adding several metrics has a low level of evidence, thus warranting additional research.Peer reviewe

Fecal Coliform Release Studies and Development of a Preliminary Nonpoint Source Transport Model for Indicator Bacteria

The effect of grazing on water quality has been documented by bacteriological studies of streams adjacent to grazed areas. Bacterial release from fecal deposits is a parameter of the pollution transport mechanism that is poorly understood. The objective of this study was to determine a fecal coliform release function for cattle fecal deposits. Standard cowpies were rained on with a rainfall simulator, and the fecal coliform counts were determined using the most probably number (MPN) method of enumeration. The fecal deposits were rained on at ages 2 through 100 days. The effects of rainfall intensity and recurrent rainfall were tested. Naturally occurring fecal deposits were also tested to compare their results with the results from the standard cowpies. A log-log regression was found to describe the decline in peak fecal coliform release with fecal deposit age. The 100-day-old fecal deposits produced peak counts of 4,200 fecal coliforms per 100 milliliters of water. This quantity of release is insignificant compared to the release from fresher fecal material. Rainfall intensity had little effect on peak fecal coliform release from fecal deposits that ere 2 or 10 days old. At age 20 days the effect of rainfall intensity was significant; the highest intensity gave the lowest peak counts, and the lowest intensity gave the highest peak counts. The effect of rainfall intensity appears to be related to the dryness of the fecal deposits. Peak fecal coliform counts were significantly lowered by raining on the fecal deposits more than once. This decline was thought to be produced by the loss of bacteria from the fecal deposits during the previous wettings. Standard cowpies produced a peak release regression that was not significantly different from the regression for the natural fecal deposits. Apparently, grossly manipulating the fecal deposits did not significantly change the release patterns

Major Herbicides in Ground Water: Results from the National Water-Quality Assessment

To improve understanding of the factors affecting pesticide occurrence in ground water, patterns of detection were examined for selected herbicides, based primarily on results from the National Water-Quality Assessment (NAWQA) program. The NAWQA data were derived from 2227 sites (wells and springs) sampled in 20 major hydro- logic basins across the USA from 1993 to 1995. Results are presented for six high-use herbicides—atrazine (2-chloro-4-ethylamino-6-iso-propylamino- s-triazine), cyanazine (2-[4-chloro-6-ethylamino-1,3,5-triazin-2-yl]amino]-2-methylpropionitrile), simazine (2-chloro-4,6-bis- [ethylamino]-s-triazine), alachlor (2-chloro-N-[2,6-diethylphenyl]-N- [methoxymethyl]acetamide), acetochlor (2-chloro-N-[ethoxymethyl]- N-[ 2-ethyl-6-methylphenyl]acetamide), and metolachlor (2-chloro-N- [2-ethyl-6-methylphenyl]-N-[2-methoxy-1-methylethyl]acetamide)— as well as for prometon (2,4-bis[isopropylamino]-6-methoxy-s-triazine), a nonagricultural herbicide detected frequently during the study. Concentrations were \u3c1 μg L-1 at 98% of the sites with detections, but exceeded drinking-water criteria (for atrazine) at two sites. In urban areas, frequencies of detection (at or above 0.01 μg L-1 ) of atrazine, cyanazine, simazine, alachlor, and metolachlor in shallow ground water were positively correlated with their nonagricultural use nationwide (P \u3c 0.05). Among different agricultural areas, frequencies of detection were positively correlated with nearby agricultural use for atrazine, cyanazine, alachlor, and metolachlor, but not simazine. Multivariate analysis demonstrated that for these five herbicides, frequencies of detection beneath agricultural areas were positively correlated with their agricultural use and persistence in aerobic soil. Acetochlor, an agricultural herbicide first registered in 1994 for use in the USA, was detected in shallow ground water by 1995, consistent with previous field-scale studies indicating that some pesticides may be detected in ground water within 1 yr following application. The NAWQA results agreed closely with those from other multistate studies with similar designs

Monthlong Intubated Patient with Life-Threatening COVID-19 and Cerebral Microbleeds Suffers Only Mild Cognitive Sequelae at 8-Month Follow-up: A Case Report.

OBJECTIVE: To elaborate on possible cognitive sequelae related to COVID-19, associated cerebrovascular injuries as well as the general consequences from intensive care. COVID-19 is known to have several, serious CNS-related consequences, but neuropsychological studies of severe COVID-19 are still rare. METHODS: M., a 45-year-old man, who survived a severe COVID-19 disease course including Acute Respiratory Distress Syndrome (ARDS), cerebral microbleeds, and 35 days of mechanical ventilation, is described. We elaborate on M's recovery and rehabilitation process from onset to the 8-month follow-up. The cognitive functions were evaluated with a comprehensive screening battery at 4 weeks after extubation and at the 8-month follow-up. RESULTS: Following extubation, M. was delirious, reported visual hallucinations, and had severe sleeping difficulties. At about 3 months after COVID-19 onset, M. showed mild to moderate deficits on tests measuring processing speed, working memory, and attention. At assessments at 8 months, M. performed better, with results above average on tests measuring learning, memory, word fluency, and visuospatial functions. Minor deficits were still found regarding logical reasoning, attention, executive functioning, and processing speed. There were no lingering psychiatric symptoms. While M. had returned to a part-time job, he was not able to resume previous work-tasks. CONCLUSION: This case-study demonstrates possible cognitive deficits after severe COVID-19 and emphasizes the need of a neuropsychological follow-up, with tests sensitive to minor deficits. The main findings of this report provide some support that the long-term prognosis for cognition in severe COVID-19 may be hopeful

Cellular infiltration in traumatic brain injury

Abstract: Traumatic brain injury leads to cellular damage which in turn results in the rapid release of damage-associated molecular patterns (DAMPs) that prompt resident cells to release cytokines and chemokines. These in turn rapidly recruit neutrophils, which assist in limiting the spread of injury and removing cellular debris. Microglia continuously survey the CNS (central nervous system) compartment and identify structural abnormalities in neurons contributing to the response. After some days, when neutrophil numbers start to decline, activated microglia and astrocytes assemble at the injury site—segregating injured tissue from healthy tissue and facilitating restorative processes. Monocytes infiltrate the injury site to produce chemokines that recruit astrocytes which successively extend their processes towards monocytes during the recovery phase. In this fashion, monocytes infiltration serves to help repair the injured brain. Neurons and astrocytes also moderate brain inflammation via downregulation of cytotoxic inflammation. Depending on the severity of the brain injury, T and B cells can also be recruited to the brain pathology sites at later time points

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

Prognostic performance of computerized tomography scoring systems in civilian penetrating traumatic brain injury : an observational study

Background The prognosis of penetrating traumatic brain injury (pTBI) is poor yet highly variable. Current computerized tomography (CT) severity scores are commonly not used for pTBI prognostication but may provide important clinical information in these cohorts. Methods All consecutive pTBI patients from two large neurotrauma databases (Helsinki 1999-2015, Stockholm 2005-2014) were included. Outcome measures were 6-month mortality and unfavorable outcome (Glasgow Outcome Scale 1-3). Admission head CT scans were assessed according to the following: Marshall CT classification, Rotterdam CT score, Stockholm CT score, and Helsinki CT score. The discrimination (area under the receiver operating curve, AUC) and explanatory variance (pseudo-R-2) of the CT scores were assessed individually and in addition to a base model including age, motor response, and pupil responsiveness. Results Altogether, 75 patients were included. Overall 6-month mortality and unfavorable outcome were 45% and 61% for all patients, and 31% and 51% for actively treated patients. The CT scores' AUCs and pseudo-R(2)s varied between 0.77-0.90 and 0.35-0.60 for mortality prediction and between 0.85-0.89 and 0.50-0.57 for unfavorable outcome prediction. The base model showed excellent performance for mortality (AUC 0.94, pseudo-R-2 0.71) and unfavorable outcome (AUC 0.89, pseudo-R-2 0.53) prediction. None of the CT scores increased the base model's AUC (p > 0.05) yet increased its pseudo-R-2 (0.09-0.15) for unfavorable outcome prediction. Conclusion Existing head CT scores demonstrate good-to-excellent performance in 6-month outcome prediction in pTBI patients. However, they do not add independent information to known outcome predictors, indicating that a unique score capturing the intracranial severity in pTBI may be warranted.Peer reviewe

TRAJECTORY CLUSTERING USING LATENT CLASS MODELS FOR UNSUPERVISED TBI BIOMARKER TEMPORAL PHENOTYPE DISCOVER

Background: TBI biomarkers display population-level time-varying kinetics [1] which may be a rich source of pathobiological information [2]. At an individual level, deviations from stereotypical trajectories may represent different pathological processes or secondary insults. A method for discovering such phenotypes may be useful in in- dividualising treatments in real-time. Methods: Serial blood (12hourly) and CSF (6hourly) samples were obtained from seventeen adult patients with severe TBI (Stockholm ethics committee approval #2009/1112-31). S100B and neuron-specific enolase (NSE) concentrations were measured along with blood:CSF albumin quotient Qa as a measure of blood-brain-barrier (BBB) integrity. S100B and NSE concentrations were log-transformed: Equivalent to the assumption of baseline exponential decay. We used trajectory modeling combining a quadratic mixed effects model with latent group analysis to search for characteristic trajectories in the measured parameter. Results: For serum S100B, we discovered two phenotypes with fast and slow kinetics. The fast group corresponded with patients with more severe extracranial injury. For serum NSE, again two phenotypes were discovered; a time-decaying group and another with a peak around day 4. CSF analysis yielded two latent groups for both S100B and NSE: a time-decaying group and another displaying prolonged elevation over several days. Qa data clustered into three groups: two with fast, slow decay and another with prolonged elevation. The group with prolonged BBB permeability had corresponding poorer outcomes. Conclusions: Small numbers prevent statistical comparison, but trajectory modeling identified a number of phenotypes with plausible pathobiological significance. In particular the technique revealed a group of patients with secondary serum NSE release and another with sustained BBB permeability. Such groups seem to relate to injury profile and outcome suggesting biological relevance. To our knowledge this is the first use of an unsupervised clustering technique in kinetic phenotype discovery. References: [1] Ercole A, Thelin EP, Holst A, Bellander BM, Nelson DW. Kinetic modelling of serum S100b after traumatic brain injury. BMC Neurol. 2016;16:93. [2] Thelin EP, Zeiler FA, Ercole A, Mondello S, Büki A, Bellander BM, Helmy A, Menon DK, Nelson DW. Serial Sampling of Serum Protein Biomarkers for Monitoring Human Traumatic Brain Injury Dynamics: A Systematic Review. Front Neurol. 2017;8:300