Clinical neurophysiology for neurological prognostication of comatose patients after cardiac arrest

Rossetti, Andrea O.

doi:10.1016/j.cnp.2017.03.001

Cited by 16 publications

(19 citation statements)

References 58 publications

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“…BSP can be seen within 48 hours in up to 50% of patients after cardiac arrest undergoing therapeutic hypothermia (TH) 12 . Current literature typically describes this pattern as "highly malignant" and it is often reported to have 0% false positive rate (FPR) for prediction of poor outcome in these patients 11,[13][14][15] . A recent paper 16 used a model based on quantitative measures of EEG background discontinuity and achieved 100% specificity (sensitivity of 50%) for poor outcome as early as 24 hours after cardiac arrest and 90% specificity (sensitivity of 57%) for a good outcome at 12 hours.…”

Section: Introductionmentioning

confidence: 99%

Spectral Content of Electroencephalographic Burst-Suppression Patterns May Reflect Neuronal Recovery in Comatose Post-Cardiac Arrest Patients

Sekar

Schiff

Labar

2019

Journal of Clinical Neurophysiology

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Purpose: To assess the potential biological significance of variations in burst-suppression patterns (BSP) after cardiac arrest in relation to recovery of consciousness. In the context of recent theoretical models of BSP, bursting frequency may be representative of underlying network dynamics; discontinuous activation of membrane potential during impaired cellular energetics may promote neuronal rescue. Methods: We reviewed a database of 73 comatose post-cardiac arrest patients who underwent therapeutic hypothermia (TH) to assess for the presence of BSP and clinical outcomes. In a subsample of patients with BSP (n=14) spectral content of burst and suppression periods were quantified using multi-taper method. Results: BSP was seen in 45/73 (61%) patients. Comparable numbers of patients with (31.1%) and without (35.7%) BSP regained consciousness by the time of hospital discharge. In addition, in two unique cases, BSP initially resolved then spontaneously re-emerged after completion of TH and cessation of sedative medications. Both patients recovered consciousness. Spectral analysis of bursts in all patients regaining consciousness (n=6) showed a prominent theta frequency (5-7Hz) feature, but not in age-matched patients with induced BSP who did not recover consciousness (n=8). Conclusions: The prognostic implications of BSP after hypoxic brain injury may vary based on the intrinsic properties of the underlying brain state itself. The presence of theta activity within bursts may index potential viability of neuronal networks underlying recovery of consciousness;

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Section: Introductionmentioning

confidence: 99%

Spectral Content of Electroencephalographic Burst-Suppression Patterns May Reflect Neuronal Recovery in Comatose Post-Cardiac Arrest Patients

Sekar

Schiff

Labar

2019

Journal of Clinical Neurophysiology

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“…Early, reliable identification of patients without potential to recover from coma, or with the risk to develop severe neurological disabilities, is of paramount importance in order to inform relatives and to avoid inappropriate continuation of life‐supporting treatments (Sandroni et al, ). Currently, prognostication is based on multimodal approaches combining clinical and paraclinical examinations (Rossetti, ; Sandroni, D'Arrigo, & Nolan, ). One of the most important modality is the electroencephalogram (EEG; Backman et al, ; Hofmeijer et al, ; Rossetti, ; Rossetti et al, ; Sandroni et al, ; Thenayan, Savard, Sharpe, Norton, & Young, ; Westhall et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, prognostication is based on multimodal approaches combining clinical and paraclinical examinations (Rossetti, ; Sandroni, D'Arrigo, & Nolan, ). One of the most important modality is the electroencephalogram (EEG; Backman et al, ; Hofmeijer et al, ; Rossetti, ; Rossetti et al, ; Sandroni et al, ; Thenayan, Savard, Sharpe, Norton, & Young, ; Westhall et al, ). In clinical practice, EEG analysis is performed visually by a trained electroencephalographer based on relatively few criteria (Hirsch et al, ).…”

Section: Introductionmentioning

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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“…This point was explicitly addressed in a recent consensus survey, in which 88% of the 24 international EEG experts who participated declared that EMG-R should not qualify as EEG reactivity [3]. Currently, EMG activity in comatose patients after CA is rather considered as an artifact, that if too abundant may be suppressed pharmacologically [13] or with computational methods [14] to ensure better interpretation of the EEG.…”

Section: Introductionmentioning

confidence: 99%

Electromyographic reactivity measured with scalp-EEG contributes to prognostication after cardiac arrest

et al. 2019

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Aim: To assess whether stimulus-induced modifications of electromyographic activity observed on scalp EEG have a prognostic value in comatose patients after cardiac arrest. Methods: 184 adult patients from a multi-centric prospective register who underwent an early EEG after cardiac arrest were included. Auditory and somatosensory stimulation was performed during EEG-recording. EEG reactivity (EEG-R) and EMG reactivity (EMG-R) were retrospectively assessed visually by board-certified electroencephalographers, and compared with clinical outcome (cerebral performance category, CPC) at three months. A favorable functional outcome was defined as CPC 1-2, an unfavorable outcome as CPC 3-5.

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Clinical neurophysiology for neurological prognostication of comatose patients after cardiac arrest

Abstract: HighlightsA multimodal prognostic approach is recommended after cardiac arrest.EEG (background and, reactivity, repetitive epileptiform features) and SSEP are core assessments.Some outlook into long-latency evoked potentials is offered.

Cited by 16 publications

References 58 publications

Spectral Content of Electroencephalographic Burst-Suppression Patterns May Reflect Neuronal Recovery in Comatose Post-Cardiac Arrest Patients

Spectral Content of Electroencephalographic Burst-Suppression Patterns May Reflect Neuronal Recovery in Comatose Post-Cardiac Arrest Patients

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

Electromyographic reactivity measured with scalp-EEG contributes to prognostication after cardiac arrest

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