Benign EEG for prognostication of favorable outcome after cardiac arrest: A reappraisal

Role of EEG spindle-like activity in post cardiac arrest prognostication

AIM

EEG is considered in guidelines for poor outcome prognostication in comatose patients after cardiac arrest (CA), but elements related to favorable prognosis have also been increasingly described. While spindle EEG activity is known to herald good outcome in critically ill patients, its occurrence in CA has received limited attention, essentially in pediatric cohorts. We postulated that this feature is related to favorable outcome in adults.

METHODS

Retrospective assessment of comatose adults following CA in a prospective institutional registry (09.2021-09.2023). Spindle-like activity, noted prospectively on early (12-36 h) and late (36-72 h) routine EEGs, was tested using 2x2 tables and comparisons of proportions for the likelihood of favorable outcome (CPC 1-2 at 3 months), including combinations with existing benign EEG descriptions (Westhall: no malignant or highly malignant features; modified: also allowing background discontinuity, low voltage, inverse development). Spindles were correlated with peak serum neuron-specific enolase (NSE) at 24-48 h as a marker of neuronal damage.

RESULTS

Among 276 patients, spindle-like activity was observed in 66 (23.9 %) of them, more often in early EEGs. While, in isolation, this feature detected within 72 h showed high specificity for CPC 1-2 (82.2 %) and low sensitivity (36.8 %), its addition significantly enhanced sensitivity of modified benign EEG (from 90.5 % to 95.8 %; p < 0.001; specificity at 54.4 %). Patients with spindle-like activity had significantly lower NSE (median 25.7µg/l, interquartile range 16.1-24.4, vs. 39.4 µg/l, interquartile range 21.1-95.1; p < 0.001).

CONCLUSION

Spindle-like EEG activity may orient on prognostication of favorable outcome in adult post CA patients, and correlates with lower neuronal damage.

Myoclonus After Cardiac Arrest did not Correlate with Cortical Response on Somatosensory Evoked Potentials

PURPOSE

Myoclonus after anoxic brain injury is a marker of significant cerebral injury. Absent cortical signal (N20) on somatosensory evoked potentials (SSEPs) after cardiac arrest is a reliable predictor of poor neurological recovery when combined with an overall clinical picture consistent with severe widespread neurological injury. We evaluated a clinical question of if SSEP result could be predicted from other clinical and neurodiagnostic testing results in patients with post-anoxic myoclonus.

METHODS

Retrospective chart review of all adult patients with post-cardiac arrest myoclonus who underwent both electroencephalographic (EEG) monitoring and SSEPs for neuroprognostication. Myoclonus was categorized as "non-myoclonic movements," "myoclonus not captured on EEG," "myoclonus without EEG correlate," "myoclonus with EEG correlate," and "status myoclonus." SSEP results were categorized as all absent, all present, N18 and N20 absent bilaterally, and N20 only absent bilaterally. Cox proportional hazards with censoring was used to evaluate the association of myoclonus category, SSEP results, and confounding factors with survival.

RESULTS

In 56 patients, median time from arrest to either confirmed death or last follow up was 9 days. The category of myoclonus was not associated with SSEP result or length of survival. Absence of N20 s or N18 s was associated with shorter survival (N20 hazard ratio [HR] 4.4, p = 0.0014; N18 HR 5.5, p < 0.00001).

CONCLUSIONS

Category of myoclonus did not reliably predict SSEP result. SSEP result was correlated with outcome consistently, but goals of care transitioned to comfort measures only in all patients with present peripheral potentials and either absent N20 s only or absence of N18 s and N20 s. Our results suggest that SSEPs may retain prognostic value in patients with post-anoxic myoclonus.

EEG for good outcome prediction after cardiac arrest: A multicentre cohort study

AIM

Assess the prognostic ability of a non-highly malignant and reactive EEG to predict good outcome after cardiac arrest (CA).

METHODS

Prospective observational multicentre substudy of the "Targeted Hypothermia versus Targeted Normothermia after Out-of-hospital Cardiac Arrest Trial", also known as the TTM2-trial. Presence or absence of highly malignant EEG patterns and EEG reactivity to external stimuli were prospectively assessed and reported by the trial sites. Highly malignant patterns were defined as burst-suppression or suppression with or without superimposed periodic discharges. Multimodal prognostication was performed 96 h after CA. Good outcome at 6 months was defined as a modified Rankin Scale score of 0-3.

RESULTS

873 comatose patients at 59 sites had an EEG assessment during the hospital stay. Of these, 283 (32%) had good outcome. EEG was recorded at a median of 69 h (IQR 47-91) after CA. Absence of highly malignant EEG patterns was seen in 543 patients of whom 255 (29% of the cohort) had preserved EEG reactivity. A non-highly malignant and reactive EEG had 56% (CI 50-61) sensitivity and 83% (CI 80-86) specificity to predict good outcome. Presence of EEG reactivity contributed (p < 0.001) to the specificity of EEG to predict good outcome compared to only assessing background pattern without taking reactivity into account.

CONCLUSION

Nearly one-third of comatose patients resuscitated after CA had a non-highly malignant and reactive EEG that was associated with a good long-term outcome. Reactivity testing should be routinely performed since preserved EEG reactivity contributed to prognostic performance.

Quantitative and Radiological Assessment of Post-cardiac-Arrest Comatose Patients with Diffusion-Weighted Magnetic Resonance Imaging

BACKGROUND

Although magnetic resonance imaging, particularly diffusion-weighted imaging, has increasingly been used as part of a multimodal approach to prognostication in patients who are comatose after cardiac arrest, the performance of quantitative analysis of apparent diffusion coefficient (ADC) maps, as compared to standard radiologist impression, has not been well characterized. This retrospective study evaluated quantitative ADC analysis to the identification of anoxic brain injury by diffusion abnormalities on standard clinical magnetic resonance imaging reports.

METHODS

The cohort included 204 previously described comatose patients after cardiac arrest. Clinical outcome was assessed by (1) 3-6 month post-cardiac-arrest cerebral performance category and (2) coma recovery to following commands. Radiological evaluation was obtained from clinical reports and characterized as diffuse, cortex only, deep gray matter structures only, or no anoxic injury. Quantitative analyses of ADC maps were obtained in specific regions of interest (ROIs), whole cortex, and whole brain. A subgroup analysis of 172 was performed after eliminating images with artifacts and preexisting lesions.

RESULTS

Radiological assessment outperformed quantitative assessment over all evaluated regions (area under the curve [AUC] 0.80 for radiological interpretation and 0.70 for the occipital region, the best performing ROI, p = 0.011); agreement was substantial for all regions. Radiological assessment still outperformed quantitative analysis in the subgroup analysis, though by smaller margins and with substantial to near-perfect agreement. When assessing for coma recovery only, the difference was no longer significant (AUC 0.83 vs. 0.81 for the occipital region, p = 0.70).

CONCLUSIONS

Although quantitative analysis eliminates interrater differences in the interpretation of abnormal diffusion imaging and avoids bias from other prediction modalities, clinical radiologist interpretation has a higher predictive value for outcome. Agreement between radiological and quantitative analysis improved when using high-quality scans and when assessing for coma recovery using following commands. Quantitative assessment may thus be more subject to variability in both clinical management and scan quality than radiological assessment.

Utility and rationale for continuous EEG monitoring: a primer for the general intensivist

This review offers a comprehensive guide for general intensivists on the utility of continuous EEG (cEEG) monitoring for critically ill patients. Beyond the primary role of EEG in detecting seizures, this review explores its utility in neuroprognostication, monitoring neurological deterioration, assessing treatment responses, and aiding rehabilitation in patients with encephalopathy, coma, or other consciousness disorders. Most seizures and status epilepticus (SE) events in the intensive care unit (ICU) setting are nonconvulsive or subtle, making cEEG essential for identifying these otherwise silent events. Imaging and invasive approaches can add to the diagnosis of seizures for specific populations, given that scalp electrodes may fail to identify seizures that may be detected by depth electrodes or electroradiologic findings. When cEEG identifies SE, the risk of secondary neuronal injury related to the time-intensity "burden" often prompts treatment with anti-seizure medications. Similarly, treatment may be administered for seizure-spectrum activity, such as periodic discharges or lateralized rhythmic delta slowing on the ictal-interictal continuum (IIC), even when frank seizures are not evident on the scalp. In this setting, cEEG is utilized empirically to monitor treatment response. Separately, cEEG has other versatile uses for neurotelemetry, including identifying the level of sedation or consciousness. Specific conditions such as sepsis, traumatic brain injury, subarachnoid hemorrhage, and cardiac arrest may each be associated with a unique application of cEEG; for example, predicting impending events of delayed cerebral ischemia, a feared complication in the first two weeks after subarachnoid hemorrhage. After brief training, non-neurophysiologists can learn to interpret quantitative EEG trends that summarize elements of EEG activity, enhancing clinical responsiveness in collaboration with clinical neurophysiologists. Intensivists and other healthcare professionals also play crucial roles in facilitating timely cEEG setup, preventing electrode-related skin injuries, and maintaining patient mobility during monitoring.

Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications

Outcome prognostication in comatose patients after cardiac arrest (CA) remains to date a challenge. The major determinant of clinical outcome is the post-hypoxic/ischemic encephalopathy. Electroencephalography (EEG) is routinely used to assess neural functions in comatose patients. Currently, EEG-based outcome prognosis relies on visual evaluation by medical experts, which is time consuming, prone to subjectivity, and oblivious to complex patterns. The field of deep learning has given rise to powerful algorithms for detecting patterns in large amounts of data. Analyzing EEG signals of coma patients with deep neural networks with the goal of assisting in outcome prognosis is therefore a natural application of these algorithms. Here, we provide the first narrative literature review on the use of deep learning for prognostication after CA. Existing studies show overall high performance in predicting outcome, relying either on spontaneous or on auditory evoked EEG signals. Moreover, the literature is concerned with algorithmic interpretability, and has shown that largely, deep neural networks base their decisions on clinically or neurophysiologically meaningful features. We conclude this review by discussing considerations that the fields of artificial intelligence and neurology will need to jointly address in the future, in order for deep learning algorithms to break the publication barrier, and to be integrated in clinical practice.

The effect of sedation and time after cardiac arrest on coma outcome prognostication based on EEG power spectra

Early prognostication of long-term outcome of comatose patients after cardiac arrest remains challenging. Electroencephalography-based power spectra after cardiac arrest have been shown to help with the identification of patients with favourable outcome during the first day of coma. Here, we aim at comparing the power spectra prognostic value during the first and second day after coma onset following cardiac arrest and to investigate the impact of sedation on prognostication. In this cohort observational study, we included comatose patients (N = 91) after cardiac arrest for whom resting-state electroencephalography was collected on the first and second day after cardiac arrest in four Swiss hospitals. We evaluated whether the average power spectra values at 4.6-15.2 Hz were predictive of patients' outcome based on the best cerebral performance category score at 3 months, with scores ranging from 1 to 5 and dichotomized as favourable (1-2) and unfavourable (3-5). We assessed the effect of sedation and its interaction with the electroencephalography-based power spectra on patient outcome prediction through a generalized linear mixed model. Power spectra values provided 100% positive predictive value (95% confidence intervals: 0.81-1.00) on the first day of coma, with correctly predicted 18 out of 45 favourable outcome patients. On the second day, power spectra values were not predictive of patients' outcome (positive predictive value: 0.46, 95% confidence intervals: 0.19-0.75). On the first day, we did not find evidence of any significant contribution of sedative infusion rates to the patient outcome prediction (P > 0.05). Comatose patients' outcome prediction based on electroencephalographic power spectra is higher on the first compared with the second day after cardiac arrest. Sedation does not appear to impact patient outcome prediction.