Angela Velazquez

Metabolic crisis occurs with seizures and periodic discharges after brain trauma

Traumatic brain injury (TBI) results in persistent disruption of brain metabolism that has yet to be mechanistically defined. Early post‐traumatic seizures are one potential mechanism for metabolic crisis and hence could be a therapeutic target. We hypothesized that seizures and pseudoperiodic discharges (PDs) may be mechanistically linked to metabolic crisis as measured by cerebral microdialysis.

Intraventricular hemorrhage expansion in patients with spontaneous intracerebral hemorrhage

Objective: To evaluate whether delayed appearance of intraventricular hemorrhage (dIVH) represents an independent entity from intraventricular hemorrhage (IVH) present on admission CT or is primarily related to the time interval between symptom onset and admission CT. Methods: A total of 282 spontaneous intracerebral hemorrhage (ICH) patients, admitted February 2009–March 2014 to the neurological intensive care unit of a tertiary care university hospital, were prospectively enrolled in the ICH Outcomes Project. Multivariate logistic regression was used to determine associations with acute mortality and functional long-term outcome (modified Rankin Scale). Results: A cohort of 282 ICH patients was retrospectively studied: 151 (53.5%) had intraventricular hemorrhage on initial CT scan (iIVH). Of the remaining 131 patients, 19 (14.5%) developed IVH after the initial CT scan (dIVH). The median times from symptom onset to admission CT were 1.1, 6.0, and 7.4 hours for the dIVH, iIVH, and no IVH groups (Mann-Whitney U test, dIVH vs iIVH, p < 0.001) and median time from onset to dIVH detection was 7.2 hours. The increase in ICH volume following hospital admission was larger in dIVH than in iIVH and no IVH patients (mean 17.6, 0.2, and 0.4 mL). After controlling for components of the ICH score and hematoma expansion, presence of IVH on initial CT was associated with discharge mortality and poor outcome at 3, 6, and 12 months, but dIVH was not associated with any of the outcome measures. Conclusions: In ICH patients, associated IVH on admission imaging is commonly encountered and is associated with poor long-term outcome. In contrast, dIVH on subsequent scans is far less common and does not appear to portend worse outcome.

Prognostication of long-term outcomes after subarachnoid hemorrhage: The FRESH score.

To create a multidimensional tool to prognosticate long‐term functional, cognitive, and quality of life outcomes after spontaneous subarachnoid hemorrhage (SAH) using data up to 48 hours after admission.

Electroencephalographic Periodic Discharges and Frequency-Dependent Brain Tissue Hypoxia in Acute Brain Injury.

Importance Periodic discharges (PDs) that do not meet seizure criteria, also termed the ictal interictal continuum, are pervasive on electroencephalographic (EEG) recordings after acute brain injury. However, their association with brain homeostasis and the need for clinical intervention remain unknown. Objective To determine whether distinct PD patterns can be identified that, similar to electrographic seizures, cause brain tissue hypoxia, a measure of ongoing brain injury. Design, Setting, and Participants This prospective cohort study included 90 comatose patients with high-grade spontaneous subarachnoid hemorrhage who underwent continuous surface (scalp) EEG (sEEG) recording and multimodality monitoring, including invasive measurements of intracortical (depth) EEG (dEEG), partial pressure of oxygen in interstitial brain tissue (PbtO2), and regional cerebral blood flow (CBF). Patient data were collected from June 1, 2006, to September 1, 2014, at a single tertiary care center. The retrospective analysis was performed from September 1, 2014, to May 1, 2016, with a hypothesis that the effect on brain tissue oxygenation was primarily dependent on the discharge frequency. Main Outcomes and Measures Electroencephalographic recordings were visually classified based on PD frequency and spatial distribution of discharges. Correlations between mean multimodality monitoring data and change-point analyses were performed to characterize electrophysiological changes by applying bootstrapping. Results Of the 90 patients included in the study (26 men and 64 women; mean [SD] age, 55 [15] years), 32 (36%) had PDs on sEEG and dEEG recordings and 21 (23%) on dEEG recordings only. Frequencies of PDs ranged from 0.5 to 2.5 Hz. Median PbtO2 was 23 mm Hg without PDs compared with 16 mm Hg at 2.0 Hz and 14 mm Hg at 2.5 Hz (differences were significant for 0 vs 2.5 Hz based on bootstrapping). Change-point analysis confirmed a temporal association of high-frequency PD onset (≥2.0 Hz) and PbtO2 reduction (median normalized PbtO2 decreased by 25% 5-10 minutes after onset). Increased regional CBF of 21.0 mL/100 g/min for 0 Hz, 25.9 mL/100 g/min for 1.0 Hz, 27.5 mL/100 g/min for 1.5 Hz, and 34.7 mL/100 g/min for 2.0 Hz and increased global cerebral perfusion pressure of 91 mm Hg for 0 Hz, 100.5 mm Hg for 0.5 Hz, 95.5 mm Hg for 1.0 Hz, 97.0 mm Hg for 2.0 Hz, 98.0 mm Hg for 2.5 Hz, 95.0 mm Hg for 2.5 Hz, and 67.8 mm Hg for 3.0 Hz were seen for higher PD frequencies. Conclusions and Relevance These data give some support to consider redefining the continuum between seizures and PDs, suggesting that additional damage after acute brain injury may be reflected by frequency changes in electrocerebral recordings. Similar to seizures, cerebral blood flow increases in patients with PDs to compensate for the increased metabolic demand but higher-frequency PDs (>2 per second) may be inadequately compensated without an additional rise in CBF and associated with brain tissue hypoxia, or higher-frequency PDs may reflect inadequacies in brain compensatory mechanisms.

Bedside quantitative electroencephalography improves assessment of consciousness in comatose subarachnoid hemorrhage patients.

Accurate behavioral assessments of consciousness carry tremendous significance in guiding management, but are extremely challenging in acutely brain‐injured patients. We evaluated whether electroencephalography (EEG) and multimodality monitoring parameters may facilitate assessment of consciousness in patients with subarachnoid hemorrhage.

Dynamic regimes of neocortical activity linked to corticothalamic integrity correlate with outcomes in acute anoxic brain injury after cardiac arrest

Recognition of potential for neurological recovery in patients who remain comatose after cardiac arrest is challenging and strains clinical decision making. Here, we utilize an approach that is based on physiological principles underlying recovery of consciousness and show correlation with clinical recovery after acute anoxic brain injury.

Deriving the PRx and CPPopt from 0.2-Hz Data: Establishing Generalizability to Bedmaster Users

OBJECTIVE The objective was to explore the validity of industry-parameterized vital signs in the generation of pressure reactivity index (PRx) and optimal cerebral perfusion pressure (CPPopt) values. MATERIALS AND METHODS Ten patients with intracranial pressure (ICP) monitors from 2008 to 2013 in a tertiary care hospital were included. Arterial blood pressure (ABP) and ICP were sampled at 240 Hz (of waveform data) and 0.2 Hz (of parameterized data produced by heuristic industry proprietary algorithms). 240-Hz ABP were filtered for pulse pressure and diastolic ABP within the limits of 20-150 mmHg. The PRx was calculated as Pearsons correlation coefficient using 10-s averages of ICP and ABP over a 5-min moving window with 80% overlap. For ease of comparison, we used the naming convention of BMx for PRx values derived from 0.2-Hz data. A 5-min median cerebral perfusion pressure (CPP) trend was calculated, PRx or BMx values divided and averaged into CPP bins spanning 5 mmHg. The minimum Y value (PRx or BMx) of the parabolic function fit to the resulting XY plot of 4 h of data was obtained, and updated every 1 min. Pearsons R correlations were calculated for each patient. Linear mixed-effects models were used with a random intercept to assess the overall correlation between the PRx (outcome) and the BMx (fixed effect) or the CPPopt-PRx (outcome) and the CPPopt-BMx (fixed effect). RESULTS The overall correlation between the PRx and BMx was 0.78 based on the linear mixed effects models (p < 0.0001), and the overall correlation for the CPPopt-PRx and CPPopt-BMx based on the linear mixed effects models was 0.76 (p < 0.0001). One patient had low correlation of CPPopts derived from the PRx vs the BMx; this patient had the least number of hours of CPPopt data to compare. CONCLUSIONS The BMx shows promise in CPPopt derivation against the validated PRx measure. If further developed, it could expand the capability of centers to derive CPPopt goals for use in clinical trials.

Early myoclonus following anoxic brain injury

Background It is unknown whether postanoxic cortical and subcortical myoclonus are distinct entities with different prognoses. Methods In this retrospective cohort study of 604 adult survivors of cardiac arrest over 8.5 years, we identified 111 (18%) patients with myoclonus. Basic demographics and clinical characteristics of myoclonus were collected. EEG reports, and, when available, raw video EEG, were reviewed, and all findings adjudicated by 3 authors blinded to outcomes. Myoclonus was classified as cortical if there was a preceding, time-locked electrographic correlate and otherwise as subcortical. Outcome at discharge was determined using Cerebral Performance Category. Results Patients with myoclonus had longer arrests with less favorable characteristics compared to patients without myoclonus. Cortical myoclonus occurred twice as often as subcortical myoclonus (59% vs 23%, respectively). Clinical characteristics during hospitalization did not distinguish the two. Rates of electrographic seizures were higher in patients with cortical myoclonus (43%, vs 8% with subcortical). Survival to discharge was worse for patients with myoclonus compared to those without (26% vs 39%, respectively), but did not differ between subcortical and cortical myoclonus (24% and 26%, respectively). Patients with cortical myoclonus were more likely to be discharged in a comatose state than those with subcortical myoclonus (82% vs 33%, respectively). Among survivors, good functional outcome at discharge was equally possible between those with cortical and subcortical myoclonus (12% and 16%, respectively). Conclusions Cortical and subcortical myoclonus are seen in every sixth patient with cardiac arrest and cannot be distinguished using clinical criteria. Either condition may have good functional outcomes.

Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods

Purpose Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone. Methods 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt–Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and linear and kernel support vector machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. Results The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset. Conclusion Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy.