Sleep Features on Continuous Electroencephalography Predict Rehabilitation Outcomes After Severe Traumatic Brain Injury

Sandsmark, Danielle K.; Kumar, Monisha A.; Woodward, Catherine S.; Schmitt, Sarah; Park, Soo‐Jin; Lim, Miranda M.

doi:10.1097/htr.0000000000000217

Cited by 41 publications

(40 citation statements)

References 32 publications

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“…Previous studies revealed that post-TBI seizures in humans are associated with elevated intracranial pressure, longer-lasting cerebral metabolic distress related to an increase in the lactate/ pyruvate ratio, hippocampal atrophy, increased mortality, and the development of post-traumatic epilepsy. 2,3,5,37 These studies concluded that post-TBI seizures, although largely non-convulsive, are therapeutic targets for improving post-TBI outcome. In rats, a 2.8-atm fluid-percussion impact at the end of isoflurane anesthesia into a 5-mm-diameter craniotomy with the center coordinate 4 mm posterior to bregma and 2.5 mm lateral to midline results in an approximately 20-mm 2 cortical lesion with an epicenter in the left auditory cortex.…”

Section: Status Epilepticus After Tbi 1903mentioning

confidence: 99%

“…S tudies in humans monitored by electroencephalography in the intensive care unit show that approximately 20-30% of patients with severe traumatic brain injury (TBI) have nonconvulsive seizures or nonconvulsive status epilepticus (NCSE), [1][2][3][4][5] and 33% present in the first 3 days after injury. 6 Seizure occurrence exhibits a bimodal distribution, peaking at 29 h and 140 h postinjury.…”

Section: Introductionmentioning

confidence: 99%

“…3 Seizures are linked to increased intracranial pressure, cerebral metabolic distress, hippocampal atrophy, and increased mortality, implicating a therapeutic need to terminate the NCSE. [2][3][4][5] To date, an animal model of post-TBI NCSE has not been reported, although an animal model could provide a valuable in vivo platform for investigating the mechanisms of NCSE and testing the efficacy of mechanism-based treatments to stop NCSE in this clinically challenging group of patients.…”

Section: Introductionmentioning

confidence: 99%

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Acute Non-Convulsive Status Epilepticus after Experimental Traumatic Brain Injury in Rats

Andrade

Bañuelos-Cabrera

Lapinlampi

et al. 2019

Journal of Neurotrauma

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Severe traumatic brain injury (TBI) induces seizures or status epilepticus (SE) in 20-30% of patients during the acute phase. We hypothesized that severe TBI induced with lateral fluid-percussion injury (FPI) triggers post-impact SE. Adult Sprague-Dawley male rats were anesthetized with isoflurane and randomized into the sham-operated experimental control or lateral FPI-induced severe TBI groups. Electrodes were implanted right after impact or sham-operation, then videoelectroencephalogram (EEG) monitoring was started. In addition, video-EEG was recorded from naïve rats. During the first 72 h post-TBI, injured rats had seizures that were intermingled with other epileptiform EEG patterns typical to nonconvulsive SE, including occipital intermittent rhythmic delta activity, lateralized or generalized periodic discharges, spike-and-wave complexes, poly-spikes, poly-spike-and-wave complexes, generalized continuous spiking, burst suppression, or suppression. Almost all (98%) of the electrographic seizures were recorded during 0-72 h post-TBI (23.2-17.4 seizures/rat). Mean latency from the impact to the first electrographic seizure was 18.4-15.1 h. Mean seizure duration was 86-57 sec. Analysis of high-resolution videos indicated that only 41% of electrographic seizures associated with behavioral abnormalities, which were typically subtle (Racine scale 1-2). Fifty-nine percent of electrographic seizures did not show any behavioral manifestations. In most of the rats, epileptiform EEG patterns began to decay spontaneously on Days 5-6 after TBI. Interestingly, also a few sham-operated and naïve rats had post-operation seizures, which were not associated with EEG background patterns typical to non-convulsive SE seen in TBI rats. To summarize, our data show that lateral FPI-induced TBI results in non-convulsive SE with subtle behavioral manifestations; this explains why it has remained undiagnosed until now. The lateral FPI model provides a novel platform for assessing the mechanisms of acute symptomatic non-convulsive SE and for testing treatments to prevent post-injury SE in a clinically relevant context.

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Section: Status Epilepticus After Tbi 1903mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Acute Non-Convulsive Status Epilepticus after Experimental Traumatic Brain Injury in Rats

Andrade

Bañuelos-Cabrera

Lapinlampi

et al. 2019

Journal of Neurotrauma

View full text Add to dashboard Cite

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“…When interpreting the EEG of a comatose patient, clinicians do not only rely on previously seen coma‐EEG but also evoke their longtime acquired experience with EEG recordings in other conditions. In the context of prognostication after CA, clinicians might for instance recognize the presence of typical patterns of nonrapid eye movement (NREM) sleep, which can be suggestive of a favorable outcome for patients in the Intensive Care Unit (Murray, Boylan, Ryan, & Connolly, ; Sandsmark et al, ). We wanted to determine whether the performance of the network could be improved by teaching it to recognize elements of sleep stage NREM‐2 (sleep‐spindles or K‐complexes) or NREM 3 (rhythmic delta activity).…”

Section: Discussionmentioning

confidence: 99%

EEG‐based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features

Jonas

Rossetti

Oddo

et al. 2019

Human Brain Mapping

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Prognostication for comatose patients after cardiac arrest is a difficult but essential task. Currently, visual interpretation of electroencephalogram (EEG) is one of the main modality used in outcome prediction. There is a growing interest in computer‐assisted EEG interpretation, either to overcome the possible subjectivity of visual interpretation, or to identify complex features of the EEG signal. We used a one‐dimensional convolutional neural network (CNN) to predict functional outcome based on 19‐channel‐EEG recorded from 267 adult comatose patients during targeted temperature management after CA. The area under the receiver operating characteristic curve (AUC) on the test set was 0.885. Interestingly, model architecture and fine‐tuning only played a marginal role in classification performance. We then used gradient‐weighted class activation mapping (Grad‐CAM) as visualization technique to identify which EEG features were used by the network to classify an EEG epoch as favorable or unfavorable outcome, and also to understand failures of the network. Grad‐CAM showed that the network relied on similar features than classical visual analysis for predicting unfavorable outcome (suppressed background, epileptiform transients). This study confirms that CNNs are promising models for EEG‐based prognostication in comatose patients, and that Grad‐CAM can provide explanation for the models' decision‐making, which is of utmost importance for future use of deep learning models in a clinical setting.

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“…48 Some, if not all, of these processes may be related to the healing and recovery process after brain injury. 49 Studies in comatose patients with continuous EEG monitoring have demonstrated that the presence of sleep architecture is associated with a good outcome, 50 yet SWS quantification and association with recovery has not yet been performed in a human TBI series.…”

Section: Tbi Effects On Clinical Sleep Parametersmentioning

confidence: 99%

TBI and Sleep–Wake Disorders: Pathophysiology, Clinical Management, and Moving towards the Future

Werner

Baumann

2017

Semin Neurol

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After experiencing a traumatic brain injury (TBI), the majority of patients will develop sleep–wake disorders (SWD). These can include insomnia, pleiosomnia, excessive daytime sleepiness, obstructive and/or central sleep apnea, circadian sleep–wake disorders, and potentially a variety of parasomnias. Untreated SWD may impede the recovery process and can negatively impact attention, executive function, and working memory. Importantly, these patients tend to misperceive their posttraumatic sleep problems. Consequently, interviews performed in standard clinical practice will not sufficiently capture SWD patients, potentially compromising safety and productivity.In this review, the authors outline the state of current TBI-related SWD, highlighting proposed mechanisms, treatment modalities, and areas for further clinical investigation. They highlight data examining a role for slow wave sleep (SWS) in the enhancement of neural repair. They also examine the utility of enhanced cohort recruitment and SWD biomarker discovery via use of social media, smart-devices, and data-sharing networks.

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Sleep Features on Continuous Electroencephalography Predict Rehabilitation Outcomes After Severe Traumatic Brain Injury

Cited by 41 publications

References 32 publications

Acute Non-Convulsive Status Epilepticus after Experimental Traumatic Brain Injury in Rats

Acute Non-Convulsive Status Epilepticus after Experimental Traumatic Brain Injury in Rats

EEG‐based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features

TBI and Sleep–Wake Disorders: Pathophysiology, Clinical Management, and Moving towards the Future

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