IMPORTANCE Advances in treatment of traumatic brain injury are hindered by the inability to monitor pathological mechanisms in individual patients for targeted neuroprotective treatment. Spreading depolarizations, a mechanism of lesion development in animal models, are a novel candidate for clinical monitoring in patients with brain trauma who need surgery.OBJECTIVE To test the null hypothesis that spreading depolarizations are not associated with worse neurologic outcomes. DESIGN, SETTING, AND PARTICIPANTSThis prospective, observational, multicenter cohort study was conducted from February 2009 to August 2013 in 5 level 1 trauma centers. Consecutive patients who required neurological surgery for treatment of acute brain trauma and for whom research consent could be obtained were enrolled; participants were excluded because of technical problems in data quality, patient withdrawal, or loss to follow-up. Primary statistical analysis took place from April to December 2018. Evaluators of outcome assessments were blinded to other measures.INTERVENTIONS A 6-contact electrode strip was placed on the brain surface during surgery for continuous electrocorticography during intensive care. MAIN OUTCOMES AND MEASURESElectrocorticography was scored for depolarizations, following international consensus procedures. Six-month outcomes were assessed by the Glasgow Outcome Scale-Extended score. RESULTS A total of 157 patients were initially enrolled; 19 were subsequently excluded. The 138 remaining patients (104 men [75%]; median [interquartile range] age, 45 [29-64] years) underwent a median (interquartile range) of 75.5 (42.2-117.1) hours of electrocorticography. A total of 2837 spreading depolarizations occurred in 83 of 138 patients (60.1% incidence) who, compared with patients who did not have spreading depolarizations, had lower prehospital systolic blood pressure levels (mean [SD], 133 [31] mm Hg vs 146 [33] mm Hg; P = .03), more traumatic subarachnoid hemorrhage (depolarization incidences of 17 of 37 [46%], 18 of 32 [56%], 22 of 33 [67%], and 23 of 30 patients [ 77%] for Morris-Marshall Grades 0, 1, 2, and 3/4, respectively; P = .047), and worse radiographic pathology (in 38 of 73 patients [52%] and 42 of 60 patients [70%] for Rotterdam Scores 2-4 vs 5-6, respectively; P = .04). Of patients with depolarizations, 32 of 83 (39%) had only sporadic events that induced cortical spreading depression of spontaneous electrical activity, whereas 51 of 83 patients (61%) exhibited temporal clusters of depolarizations (Ն3 in a 2-hour span). Nearly half of those with clusters (23 of 51 [45%]) also had depolarizations in an electrically silent area of the cortex (isoelectric spreading depolarization). Patients with clusters did not improve in motor neurologic examinations from presurgery to postelectrocorticography, while other patients did improve. In multivariate ordinal regression adjusting for baseline prognostic variables, the occurrence of depolarization clusters had an odds ratio of 2.29 (95% CI, 1.13-4.65; P = .02) for worse ...

Objective: After traumatic brain injury (TBI), continuous electroencephalography (cEEG) is widely used to detect electrographic seizures (ESz). With the development of standardized cEEG terminology, we aimed to describe the prevalence and burden of ictal-interictal patterns and ESz after moderate-to-severe TBI and to correlate cEEG features with functional outcome. Design: Post-hoc analysis of the prospective, randomized controlled phase 2 multicenter INTREPID2566 study (ClinicalTrials.gov: ). cEEG was initiated upon admission to the ICU. The primary outcome was the 3-month Glasgow Outcome Scale-Extended (GOSE). Consensus EEG reviews were performed by raters certified in standardized cEEG terminology blinded to clinical data. Rhythmic, periodic, or ictal patterns were referred to as ictal-interictal continuum (IIC); severe IIC was defined as ≥1.5 Hz lateralized rhythmic delta activity or generalized periodic discharges, and any lateralized periodic discharges or ESz. Setting: 20 US Level I trauma centers Patients: Patients with non-penetrating TBI and post-resuscitation GCS 4–12 were included. Interventions: None. Measurements and Main Results: Among 152 patients with cEEG (age 34 ± 14 years; 88% male), 22 (14%) had severe IIC including ESz in 4 (2.6%). Severe IIC correlated with initial prognostic score (International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT)) (r=0.51, p=0.01) and Injury Severity Score (ISS) (r=0.49, p=0.01), but not with functional outcome. After controlling clinical covariates, unfavorable outcome was independently associated with: absence of posterior dominant rhythm (common odds ratio 3.38; 95% CI 1.30–9.09), absence of N2 sleep transients (3.69; 1.69–8.20), predominant delta activity (2.82; 1.32–6.10) and discontinuous background (5.33; 2.28–12.96). Conclusions: Severe IIC patterns, including ESz, were associated with clinical markers of injury severity but not functional outcome in this prospective cohort of patients with moderate-to-severe TBI. Importantly, cEEG background features were independently associated with functional outcome and improved the area-under-the-curve of existing, validated predictive models.

Continuous intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care after severe brain injuries such as traumatic brain injury and acts as a biomarker of secondary brain injury. With the rapid development of artificial intelligent (AI) approaches to data analysis, the acquisition, storage, real-time analysis, and interpretation of physiological signal data can bring insights to the field of neurocritical care bioinformatics. We review the existing literature on the quantification and analysis of the ICP waveform and present an integrated framework to incorporate signal processing tools, advanced statistical methods, and machine learning techniques in order to comprehensively understand the ICP signal and its clinical importance. Our goals were to identify the strengths and pitfalls of existing methods for data cleaning, information extraction, and application. In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome. We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.

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