Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

Screening of toxic-metabolic encephalopathy with and without epileptic seizure with density spectral array

OBJECTIVE

Although toxic metabolic encephalopathy (TME) is clinically common, distinguishing between TME with/without epileptic findings remains challenging. We examined the efficacy of density spectral array (DSA), a form of power-spectrum electroencephalography (EEG) screening tool, for clinicians not specializing in EEG interpretation to make this distinction.

METHODS

Among 346 patients with suspected TME who underwent EEG for acute impaired consciousness (2012-2023), 149 were ultimately diagnosed with TME (mean age 68.9 ± 13.3 years) were enrolled. Using EEG data, we operationally classified DSAs based on frequency changes, as follows: 1) flame or cyclic (presence of temporal frequency change), 2) band (presence of continuous alpha-range activity), 3) gradation (alpha to delta-range activity), and 4) other patterns. The inter-rater agreement rate for DSA pattern assignment was evaluated in a double-blind manner to confirm the reasonableness of the classification. Additionally, we evaluated the sensitivity and specificity of each DSA pattern at determining the ultimate diagnostic outcomes (TME alone or TME with epileptic findings).

RESULTS

TME alone and TME with epileptic findings were 136 and 13, respectively. The inter-rater agreement for DSA classification was high among clinicians (κ = 0.72-0.92). The flame or cyclic pattern exhibited high specificity (97.1 %), but low sensitivity (23.1 %) for the diagnosis of TME with epileptic findings. Conversely, the band and gradation patterns showed a high specificity (76.9-84.6 %) for the diagnosis of TME alone.

CONCLUSIONS

Overall, our DSA classification demonstrated a high inter-rater agreement rate, indicating utility as a simple yet specific tool for distinguishing TME with and without epileptic findings.

Prognostic Factors of Hospital Mortality After Near Hanging: A Retrospective two-Center French Study

Introductionsuicide is a global public health issue, with over 800 000 people taking their own lives every year. However, most suicide attempts do not result in death. Hanging is the most common method used in France, often leading to post-hanging coma (PHC). The prognosis for patients admitted in intensive care unit (ICU) following PHC is poor, yet predictive criteria of mortality have been poorly evaluated.Methodswe retrospectively collected prehospital and in-hospital data from 65 patients hospitalized in 2 French ICU for PHC, between first March 2010 and first August 2023, and compared characteristics between patients alive and dead.Resultshospital mortality was 52%. Among baseline characteristics, SAPSII and pre-hospital cardiac arrest were associated with mortality, respectively 47 versus 62 (P = .005) and 32% versus 85% (P = .001). Concerning neuroprognostication, abnormal pupillary light reflex (PLR) was more frequent in patients who died (14% vs 56%, P = .002), as abnormal EEG (0% vs 32%, P = .002) and abnormal transcranial doppler (10% vs 35%, P = .031).Conclusionwe identified several poor prognostic factors associated with hospital mortality after PHC. Further larger-scale studies are needed to supplement these findings.

Developing an Electroencephalogram-based Model to Predict Awakening after Cardiac Arrest Using Partial Processing with the BIS Engine

BACKGROUND

Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. The authors sought to determine whether internal electroencephalogram (EEG) subparameters extracted by the BIS monitor (Medtronic, USA), a device commonly used to estimate depth of anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest.

METHODS

In this retrospective cohort study, a three-layer neural network was trained to predict recovery of consciousness to the point of command following versus not based on 48 h of continuous EEG recordings in 315 comatose patients admitted to a single U.S. academic medical center after cardiac arrest (derivation cohort, n = 181; validation cohort, n = 134). Continuous EEGs were partially processed into subparameters using virtualized emulation of the BIS Engine ( i.e. , the internal software of the BIS monitor) applied to signals from the frontotemporal leads of the standard 10-20 EEG montage. The model was trained on hourly averaged measurements of these internal subparameters. This model's performance was compared to the modified Westhall qualitative EEG scoring framework.

RESULTS

Maximum prognostic accuracy in the derivation cohort was achieved using a network trained on only four BIS subparameters (inverse burst suppression ratio, mean spectral power density, gamma power, and theta/delta power). In a held-out sample of 134 patients, the model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristics curve, 0.86; accuracy, 0.87; sensitivity, 0.83; specificity, 0.88; positive predictive value, 0.71; negative predictive value, 0.94). Gamma band power has not been previously reported as a correlate of recovery potential after cardiac arrest.

CONCLUSIONS

In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative accepted standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.

Histopathological patterns of hypoxic-ischemic encephalopathy after cardiac arrest: A retrospective brain autopsy study of 319 patients

PURPOSE

Understanding the pathophysiology of hypoxic-ischemic encephalopathy (HIE) provides important knowledge for the interpretation of neuroprognostic investigations after cardiac arrest (CA). One important aspect are the patterns of regional severity of selective neuronal death within the brain.

METHODS

In a monocentric, retrospective study, we included CA patients with initially successful resuscitation, who had received brain autopsies after death. We quantified selective eosinophilic neuronal death (SEND) in cerebral neocortex, hippocampus, basal ganglia, cerebellum, and brainstem. Using a previously established classification, we dichotomized HIE severity in SEND 0-1 (<30%, reflecting no or mild HIE) versus SEND 2-4 (≥30%, reflecting moderate to severe HIE). We analyzed histopathological HIE patterns and analyzed inter-regional and inter-neocortical correlation of SEND.

RESULTS

Of 319 patients, the mean SEND was 1.8 in hippocampus, 1.4 in neocortex, and 0.9 in brainstem. Typical histopathological HIE patterns were: (I) No or mild SEND in all brain regions, (II) predominant SEND in hippocampus with no or mild SEND in other brain regions, (III) severe SEND in neocortex, but not in brainstem, and (IV) severe SEND in the brainstem with neocortical HIE. In 7(9.7%) of 72 patients with histopathology from two different neocortical regions, the SEND differed by more than 30%. Among 154 patients with a SEND greater than 30% in at least one brain region, 14(9.1%) had predominant SEND in cerebellum, and 4(2.6%) predominant SEND in basal ganglia.

CONCLUSIONS

CA causes typical histopathological HIE patterns, with the hippocampus being more susceptible to SEND, than the cerebral neocortex, and the brainstem being the most resistant brain region. The neocortical distribution of SEND is mostly homogeneous; however, a relevant subgroup shows substantial neocortical HIE heterogeneity. Further studies are required to provide a more granular histopathological analysis of infrequent HIE patterns and their implications for neuroprognostication.

Multimodal assessment of favorable neurological outcome using NSE levels and kinetics, EEG and SSEP in comatose patients after cardiac arrest

BACKGROUND

Prognostic markers of good neurological outcome after cardiac arrest (CA) remain limited. We aimed to evaluate the prognostic value of neuron-specific enolase (NSE), electroencephalogram (EEG) and somatosensory evoked potentials (SSEP) in predicting good outcome, assessed separately and in combination.

METHODS

A retrospective study was conducted in a tertiary CA center, using a prospective registry. We included all patients comatose after discontinuation of sedation and with one EEG and NSE blood measurement at 24, 48 or/and 72 h after CA. The primary outcome was favorable neurological outcome at three months, a Cerebral Performance Categories (CPC) scale 1-2 defining a good outcome.

RESULTS

Between January 2017 and April 2024, 215 patients were included. Participants were 63 years old (IQR [52-73]), and 73% were male. At 3 months, 54 patients (25.1%) had a good outcome. Compared to the poor outcome group, NSE blood levels were significantly lower in the good outcome group at 24 h (39 IQR[27-45] vs 54 IQR[37-82]µg/L, p < 0.001), 48 h (26 [18-43] vs 107 [54-227]µg/L, p < 0.001) and 72 h (20 µg/L IQR [15-30] vs 184 µg/l IQR [60-300], p < 0,001). Normal NSE (i.e., < 17 µg/L) at 24 h was highly predictive of good outcome, with a predictive positive value (PPV) of 71% despite a sensitivity (Se) of 9%. The best cut-off values for NSE at 24, 48 and 72 h were below 45.5, 51.5 and 41.5 µg/L, yielding PPV of 64%, 80% and 83% and sensitivities of 74%, 93% and 90%, respectively. A decreasing trend in NSE levels between 24 and 72 h was also highly predictive of good outcome (PPV 82%, Se 81%). A benign EEG pattern was more frequently observed in the good outcome group (87.1 vs 14.9%, p < 0.001) and predicted a good outcome with a PPV of 72% and a Se of 94%. Regarding SSEPs, a bilateral N20-baseline amplitude > 0.85 µV was predictive of good outcome (PPV 75%, Se 100%). The combination of NSE < 51.5 µg/l at 48 h, a decreasing NSE trend between 24 and 72 h and a benign EEG showed the best predictive value (PPV 96%, Se 76%).

CONCLUSION

In comatose patients after CA, a low NSE levels at 24, 48 h or 72 h, a decreasing trend in NSE over time, a benign EEG and a high N20 amplitude are robust markers of favorable outcome, reducing prognosis uncertainty.

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.

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

PurposeMyoclonus 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.MethodsRetrospective 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.ResultsIn 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).ConclusionsCategory 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.

Temporal dynamics of neural synchrony and complexity of auditory EEG responses in post-hypoxic ischemic coma

The capacity to integrate information across brain regions and sufficient diversity of neural activity is necessary for consciousness. In patients in a post-hypoxic ischemic coma, the integrity of the auditory processing network is indicative of chances of regaining consciousness. However, our understanding of how measures of integration and differentiation of auditory responses manifest across time of coma is limited. We investigated the temporal evolution of neural synchrony of auditory-evoked electroencephalographic (EEG) responses, measured via their phase-locking value (PLV), and of their neural complexity in unconscious post-hypoxic ischemic comatose patients. Our results show that the PLV was predictive of chances to regain consciousness within the first 40 h post-cardiac arrest, while its predictive value diminished over subsequent time after coma onset. This was due to changing trajectories of PLV over time of coma for non-survivors, while survivors had stable PLV. The complexity of EEG responses was not different between patients who regained consciousness and those who did not, but it significantly diminished over time of coma, irrespective of the patient's outcome. Our findings provide novel insights on the optimal temporal window for assessing auditory functions in post-hypoxic ischemic coma. They are of particular importance for guiding the implementation of quantitative techniques for prognostication and contribute to an evolving understanding of neural functions within the acute comatose state.

Alpha-band activity in density spectral array predictive for neurological outcome in patients with hypoxic-ischemic encephalopathy

BACKGROUND

In patients with hypoxic-ischemic encephalopathy (HIE), EEG is used to predict outcomes. However, a clear threshold for EEG findings associated with favorable outcomes remains unestablished. This study evaluates the predictive value of density spectral array (DSA)-based background activity in HIE patients.

METHODS

Forty-four consecutive HIE patients with disturbance of consciousness (2010-2023) were retrospectively assessed and categorized into highly malignant, malignant, or benign EEG patterns according to the conventional EEG classification. The presence of alpha-band activity, defined as an increase in alpha (or theta) frequency band power visible in the DSA, was also assessed. The relationship among conventional EEG classification, alpha-band activity, and neurological outcomes was evaluated.

RESULTS

All patients with highly malignant EEG lacked alpha-band activity and experienced poor outcomes, whereas those with less severe patterns occasionally exhibited alpha-band activity (14 % in the malignant vs. 60 % in the benign, p = 0.021), and demonstrated various outcomes. Recovery of consciousness until discharge was more prominent in patients with alpha-band activity compared to those without (100 % vs. 39 %, p < 0.001).

CONCLUSIONS

DSA-based evaluations provide a simple and valuable tool for predicting favorable neurological outcomes.

System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method

BACKGROUND

A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests using machine learning methods.

METHODS

The neurological outcomes after CA (NOCA) method for predicting poor outcomes were developed using data from 390 patients with CA between May 2018 and June 2023. The outcome was poor neurological outcome, defined as a Cerebral Performance Category score of 3-5 at discharge. We analyzed 31 variables describing the circumstances at CA, demographics, comorbidities, and prognostic studies. The prognostic method was developed based on an extreme gradient-boosting algorithm with threefold cross-validation and hyperparameter optimization. The performance of the predictive model was evaluated using the receiver operating characteristic curve analysis and calculating the area under the curve (AUC).

RESULTS

Of the 390 total patients (mean age 64.2 years; 71.3% male), 235 (60.3%) experienced poor outcomes at discharge. We selected variables to predict poor neurological outcomes using least absolute shrinkage and selection operator regression. The Glasgow Coma Scale-M (best motor response), electroencephalographic features, the neurological pupil index, time from CA to return of spontaneous circulation, and brain imaging were found to be important key parameters in the NOCA score. The AUC of the NOCA method was 0.965 (95% confidence interval 0.941-0.976).

CONCLUSIONS

The NOCA score represents a simple method for predicting neurological outcomes, with good performance in patients with CA, using a machine learning analysis that incorporates widely available variables.

Myoclonus After Cardiac Arrest: Need for Standardization-A Systematic Review and Research Proposal on Terminology

OBJECTIVES

Although myoclonus less than or equal to 72 hours after cardiac arrest (CA) is often viewed as a single entity, there is considerable heterogeneity in its clinical and electrophysiology characteristics, and its strength of association with outcome. We reviewed definitions, electroencephalogram, and outcome of myoclonus post-CA to assess the need for consensus and the potential role of electroencephalogram for further research.

DATA SOURCES

PubMed, Embase, and Cochrane databases.

STUDY SELECTION

English-language adult (≥ 18 yr) studies from 1966 to May 31, 2024, reporting myoclonus, myoclonic status/status myoclonus (MyS/SM), myoclonic status epilepticus (MSE), and/or early Lance-Adams Syndrome (eLAS) less than or equal to 72 hours post-CA. All study designs were independently screened by two authors.

DATA EXTRACTION

Data on patients presenting myoclonus, MyS/SM, MSE, and eLAS less than or equal to 72 hours post-CA, along with their definitions, electroencephalogram, and outcomes were extracted. The Newcastle-Ottawa Scale and Cochrane-Risk-of-Bias Assessment tool were used to evaluate study quality (PROSPERO n.CRD42023438107).

DATA SYNTHESIS

Of 585 identified articles, 119 met the inclusion criteria, revealing substantial heterogeneity in definitions, electroencephalogram, and outcomes. Among 3881 patients, myoclonus was reported in 2659, MyS/SM in 883, MSE in 569, and eLAS in 40. Among patients with a defined outcome, a Cerebral Performance Category (CPC) scale of 1-2 was reported in 9.8% of patients with myoclonus, 5.8% with MyS/SM, 5.7% with MSE, and 82.0% with eLAS. Electroencephalogram was recorded in 2714 patients (69.9%). CPC of 1-2 was observed in 1.6% of patients with suppression/suppression burst (SB)/unreactive (U) electroencephalogram, 11.3% with non-SB/U electroencephalogram and status epilepticus (SE), and 22.3% with non-SB/U electroencephalogram without SE.

CONCLUSIONS

Heterogeneity in definitions resulted in weak associations with outcomes. We propose to investigate myoclonus by including related electroencephalogram patterns: myoclonus associated with suppression/SB background electroencephalogram, myoclonus with nonsuppression/SB background but SE-electroencephalogram, and myoclonus with nonsuppression/SB background without SE-electroencephalogram. This pragmatic research approach should be validated in future studies.

Personalized neuron-specific enolase level based on EEG pattern for prediction of poor outcome after cardiac arrest

BACKGROUND

After cardiac arrest (CA), the European recommendations suggest to use a neuron-specific enolase (NSE) level > 60 µg/L at 48-72 h to predict poor outcome. However, the prognostic performance of NSE can vary depending on electroencephalogram (EEG). The objective was to determine whether the NSE threshold which predicts poor outcome varies according to EEG patterns and the effect of electrographic seizures on NSE level.

METHODS

A retrospective study was conducted in a tertiary CA center, using a prospective registry of 155 adult patients comatose 72 h after CA. EEG patterns were classified according to the Westhall classification (benign, malignant or highly malignant). Neurological outcome was evaluated using the CPC scale at 3 months (CPC 3-5 defining a poor outcome).

RESULTS

Participants were 64 years old (IQR [53; 72,5]), and 74% were male. 83% were out-of-hospital CA and 48% were initial shockable rhythm. Electrographic seizures were observed in 5% and 8% of good and poor outcome patients, respectively (p = 0.50). NSE blood levels were significantly lower in the good outcome (median 20 µg/L IQR [15; 30]) compared to poor outcome group (median 110 µg/l IQR [49;308], p < 0,001). Benign EEG was associated with lower level of NSE compared to malignant and highly malignant patterns (p < 0.001). The NSE level was not significantly increased in patients with seizures as compared with malignant patterns (p = 0.15). In patients with a malignant EEG, a NSE > 45.2 µg/L was predictive of unfavorable outcome with 100% specificity and a higher sensitivity (70.8%) compared to the recommended NSE cut-off of 60 µg/l (Se = 66%). Combined to electrographic seizures, a NSE > 53.5 µg/L predicts poor outcome with 100% specificity and a higher sensitivity (77.7%) compared to the recommended cut-off (Se = 66.6%). Combined to a benign EEG, a NSE level > 78.2 µg/L was highly predictive of a poor outcome with a higher specificity (Sp = 100%) compared to the recommended cut-off (Sp = 94%).

CONCLUSION

In comatose patients after AC, a personalized approach of NSE according to EEG pattern could improve the specificity and sensitivity of this biomarker for poor outcome prediction. Compared to others malignant EEG, no significant difference of NSE level was observed in case of electrographic seizures.

Correlation Between Quantitative Background Suppression on EEG and Serum NSE in Patients With Hypoxic-ischemic Encephalopathy

PURPOSE

We evaluated the correlation between quantitative background activities on electroencephalography (EEG) and serum neuron specific enolase (NSE) in patients with hypoxic-ischemic encephalopathy as well as a diagnostic value of prognostication.

METHODS

This retrospective cohort study enrolled patients with return of spontaneous circulation after cardiac arrest from March 2010 to March 2020. The inclusion criteria were (1) older than the age of 16 years and (2) patients who had both EEG and NSE. The median time for EEG and NSE were 3 days (interquartile range 2-5 days) and 3 days (interquartile range 2-4 days), respectively. The quantification of background activity was conducted with the suppression ratio (SR). We used a machine learning (eXtreme Gradient Boosting algorithm) to evaluate whether the SR could improve the accuracy of prognostication.

RESULTS

We enrolled 151 patients. The receiver operating characteristic analysis revealed a cut-off value of serum NSE and the SR for poor outcome, serum NSE (>31.9 μg/L, area under curve [AUC] = 0.88), and the SR (>21.5%, AUC = 0.75 in the right hemisphere, >34.4%, AUC = 0.76 in the left hemisphere). There was a significant positive correlation between the severity of SR and the level of NSE ( ρ = 0.57, p < 0.0001 for the right hemisphere, ρ = 0.58, p < 0.0001 for the left hemisphere). The SR showed an excellent diagnostic value for predicting poor outcome (93% specificity, 60% sensitivity in the right hemisphere and 93% specificity, 58% sensitivity in the left hemisphere). With machine learning analysis, there was an increment in distinguishing the neurological outcome by adding SR on clinical factors.

CONCLUSIONS

The SR showed a positive correlation with the level of serum NSE. The diagnostic value of the SR for predicting poor outcome was excellent, suggesting that it can be a possible biomarker for neuroprognostication in patients with hypoxic-ischemic encephalopathy.

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury

Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially harmful activity even in patients without overt clinical signs or neurologic diagnoses. Manual annotation by expert neurophysiologists is a major resource limitation in investigating the prognostic and therapeutic implications of these EEG patterns and in expanding EEG use to a broader set of patients who are likely to benefit. Artificial intelligence (AI) has already demonstrated clinical success in guiding cEEG allocation for patients at risk for seizures, and its potential uses in neurocritical care are expanding alongside improvements in AI itself. We review both current clinical uses of AI for EEG-guided management as well as ongoing research directions in automated seizure and ischemia detection, neurologic prognostication, and guidance of medical and surgical treatment.

Prognostic Value of Signal Abnormalities on Brain MRI in Post-Anoxic Super-Refractory Status Epilepticus: A Single-Center Retrospective Study

BACKGROUND

Epileptiform activity, including status epilepticus (SE), occurs in up to one-third of comatose survivors of cardiac arrest and may predict poor outcome. The relationship between SE and hypoxic-ischemic brain injury (HIBI) is not established.

METHODS

This is a single-center retrospective study on consecutive patients with post-anoxic super-refractory SE. HIBI was graded as non-widespread (group 1) or widespread (group 2) by qualitative analysis of DWI/ADC and T2w-FLAIR. Between-group differences in the rate of poor neurological outcome at 6 months (primary outcome), SE resolution and consciousness recovery before discharge, and mortality at 6 months (secondary outcomes) were investigated.

RESULTS

From January 2011 to February 2023, 40 patients were included. HIBI was widespread in 45% of patients and non-widespread in 55%. The rate of poor neurological outcome at 6 months was 27% in group 1 and 83% in group 2 (OR 12.8, CI 95% [2.5-64.3], p = 0.002). The rate of consciousness recovery before discharge was 73% in group 1 versus 22% in group 2 (OR 8.8, CI 95% [1.9-40.3], p = 0.005). SE resolved in 95% of patients in group 1 versus 67% in group 2 (OR 10.5, CI 95% [1.1-97.9], p = 0.039). Mortality rate at 6 months was 27% in group 1 versus 50% in group 2 (OR 0.4, CI 95% [0.1-1.9], p = 0.303).

CONCLUSION

Patients with widespread HIBI had higher odds of poor outcome at 6 months, lower probability of SE resolution and of consciousness recovery before discharge compared to those with non-widespread HIBI. Mortality at 6 months did not differ significantly between the two groups.

Evoked potentials in patients with disorders of consciousness

Acute coma in the intensive care unit and persistent disorders of consciousness (DoC) in neuro-rehabilitation are frequent in patients with hypoxic-ischemic encephalopathy after cardiac arrest (CA), traumatic brain injury, intracranial hemorrhage, or ischemic stroke. Reliable prognostication of long-term neurologic outcomes cannot be made by clinical examination alone in the early phase for many patients, and thus, additional investigations are necessary. Evoked potentials provide inexpensive, real-time, high temporal resolution, bedside, quantifiable information on different sensory pathways into the brain including local and global cortical processing. Short-latency somatosensory evoked potentials can reliably predict poor neurologic long-term outcome in the early phase after CA and are recommended by guidelines as one investigation within an early multimodal assessment. Middle-latency and event-related or cognitive evoked potentials provide information on the integrity of more advanced cortical processing, some closely related to consciousness. This information can help to identify those comatose patients with a good prognosis in the acute phase and help to better understand their precise clinical state and the chances of further recovery in patients with persistent DoC in neuro-rehabilitation. Further studies are necessary to improve the applicability of research findings in the clinical sphere.

Neurologic prognostication in coma and disorders of consciousness

Coma and disorders of consciousness (DoC) are clinical syndromes primarily resulting from severe acute brain injury, with uncertain recovery trajectories that often necessitate prolonged supportive care. This imposes significant socioeconomic burdens on patients, caregivers, and society. Predicting recovery in comatose patients is a critical aspect of neurocritical care, and while current prognostication heavily relies on clinical assessments, such as pupillary responses and motor movements, which are far from precise, contemporary prognostication has integrated more advanced technologies like neuroimaging and electroencephalogram (EEG). Nonetheless, neurologic prognostication remains fraught with uncertainty and significant inaccuracies and is impacted by several forms of prognostication biases, including self-fulfilling prophecy bias, affective forecasting, and clinician treatment biases, among others. However, neurologic prognostication in patients with disorders of consciousness impacts life-altering decisions including continuation of treatment interventions vs withdrawal of life-sustaining therapies (WLST), which have a direct influence on survival and recovery after severe acute brain injury. In recent years, advancements in neuro-monitoring technologies, artificial intelligence (AI), and machine learning (ML) have transformed the field of prognostication. These technologies have the potential to process vast amounts of clinical data and identify reliable prognostic markers, enhancing prediction accuracy in conditions such as cardiac arrest, intracerebral hemorrhage, and traumatic brain injury (TBI). For example, AI/ML modeling has led to the identification of new states of consciousness such as covert consciousness and cognitive motor dissociation, which may have important prognostic significance after severe brain injury. This chapter reviews the evolving landscape of neurologic prognostication in coma and DoC, highlights current pitfalls and biases, and summarizes the integration of clinical examination, neuroimaging, biomarkers, and neurophysiologic tools for prognostication in specific disease states. We will further discuss the future of neurologic prognostication, focusing on the integration of AI and ML techniques to deliver more individualized and accurate prognostication, ultimately improving patient outcomes and decision-making process in neurocritical care.

Cardiac arrest and disorders of consciousness

As the second most common cause of coma and disorders of consciousness, cardiac arrest is defined as a cessation of cardiac mechanical activity and absence of circulation. Cardiac arrest can happen due to an intrinsic cardiac condition or secondary to noncardiac causes such as respiratory, neurologic, metabolic causes or external causes such as toxic ingestion, asphyxia, drowning, trauma, and other environmental exposures. While cardiac arrest resuscitation research and practice has evolved over decades, the overall survival to hospital discharge remains low across different types of cardiac arrest (about 9%-29%). This chapter focuses on disorders of consciousness after cardiac arrest and how it is different from other etiologies. It also discusses advances and controversies in diagnosis, management, prognostication and research.

Electroencephalography (EEG) for Neurological Prognostication in Post-Anoxic Coma Following Cardiac Arrest and Its Relationship to Outcome

BACKGROUND/OBJECTIVES

Cardiac arrest may cause significant hypoxic-ischemic injury leading to coma, seizures, myoclonic jerks, or status epilepticus. Mortality is high, but accurate prognostication is challenging. A multimodal approach is employed, in which electroencephalography (EEG) forms a key part with several recognised patterns of prognostic significance.

METHODS

In this retrospective study, clinical and qualitative features of the EEG of patients admitted to the Intensive Care Unit (ICU) at Kingston General Hospital following cardiac arrest from 2017 to 2020 were reviewed. The study included 81 adult patients (≥18 years). Outcome was assessed using the Cerebral Performance Category (CPC) as 1-2 (favourable) or 3-5 (unfavourable). EEG patterns were divided into groups within the highly malignant, malignant and benign patterns described in the literature.

RESULTS

There were a wide range of causes and 22% had a favourable outcome. Highly malignant, malignant and benign patterns were associated with survival in 0%, 70% and 100%, respectively, and favourable outcomes in 0%, 48% and 100%. All patients with seizures died, and 94% with myoclonus had unfavourable outcomes. In contrast, EEG reactivity and improvement on follow-up EEG were associated with a favourable outcome.

CONCLUSIONS

Highly malignant EEG, seizures and myoclonus were associated with unfavourable outcomes, while patients with malignant EEG had better outcomes.

Application of multi-feature-based machine learning models to predict neurological outcomes of cardiac arrest

Cardiac arrest (CA) is a major disease burden worldwide and has a poor prognosis. Early prediction of CA outcomes helps optimize the therapeutic regimen and improve patients' neurological function. As the current guidelines recommend, many factors can be used to evaluate the neurological outcomes of CA patients. Machine learning (ML) has strong analytical abilities and fast computing speed; thus, it plays an irreplaceable role in prediction model development. An increasing number of researchers are using ML algorithms to incorporate demographics, arrest characteristics, clinical variables, biomarkers, physical examination findings, electroencephalograms, imaging, and other factors with predictive value to construct multi-feature prediction models for neurological outcomes of CA survivors. In this review, we explore the current application of ML models using multiple features to predict the neurological outcomes of CA patients. Although the outcome prediction model is still in development, it has strong potential to become a powerful tool in clinical practice.

Association of hyperosmolar therapy with cerebral oxygen extraction after cardiac arrest

BACKGROUND

Elevated jugular bulb venous oxygen saturation (SjvO2) after cardiac arrest may be due to diffusion-limited oxygen extraction secondary to perivascular edema. Treatment with hyperosmolar solution (HTS) may decrease this edema and thus the barrier to oxygen diffusion. Alternatively, SjvO2 may rise when cerebral metabolic rate declines due to irreversible cellular injury, which would not be affected by HTS. Electroencephalography (EEG) may differentiate between these etiologies of elevated SjvO2. We hypothesized SjvO2 would be lower after treatment with HTS and EEG could identify treatment responders.

METHODS

We conducted a retrospective observational cohort study including comatose survivors of cardiac arrest who had at least one elevated SjvO2 (>75%) and were EEG-monitored. We quantified the change in consecutive SjvO2 values within a sample pair using a multivariable mixed-effects regression, treating HTS as a fixed effect, adjusting for mean arterial pressure, partial pressure of arterial oxygen, and partial pressure of carbon dioxide. We classified pretreatment EEG patterns as benign or indicative of potential metabolic failure and tested for an interaction of EEG pattern with HTS.

RESULTS

Our primary adjusted analysis showed an independent association of HTS treatment with change in SjvO2 (β -2.2; 95% confidence interval [CI], -4.0 to -0.3%). In our interaction model, the effect of treatment differed by EEG pattern (β for interaction term -10.9%, 95% CI -17.9 to -3.9%). HTS was associated with a significant change in SjvO2 among those with benign pre-treatment EEG patterns (-12.4%, 95% CI -18.4 to -6.4%) but was not associated with a change in SjvO2 in those with ominous pre-treatment EEG patterns (-1.6%, 95% CI -3.6 to 0.4%).

CONCLUSIONS

HTS was independently associated with decreased SjvO2 in patients resuscitated from cardiac arrest, and this effect was limited to patients with benign pretreatment EEG patterns. Our results suggest diffusion-limited oxygen extraction secondary to modifiable perivascular edema as the etiology of elevated SjvO2, and EEG pattern may be useful to identify treatment responders.

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.

[Utility of EEG in neurological emergencies and critical care]

EEG is useful for evaluation of pathophysiology and prognostication of neurocritically ill patients, as it provides non-invasive, real-time monitoring of cerebral function. There have been recently a lot of advances in research on critical care EEG according to the American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology. Based on the latest knowledge, this review discusses clinical utilization of EEG in neurocritically ill patients, including critical care continuous EEG monitoring, and key points of interpretation of critical care EEG, classifying main purposes into three points: detection of electrographic and electroclinical seizures, consideration of special encephalopathies, and evaluation and prognostication of cerebral function. Neurologists should have fundamental ability to read and interpret critical care EEG and support treating physicians in terms of therapeutic strategy.

Neuroprognostication, withdrawal of care and long-term outcomes after cardiopulmonary resuscitation

PURPOSE OF REVIEW

Survivors of cardiac arrest often have increased long-term risks of mortality and disability that are primarily associated with hypoxic-ischemic brain injury (HIBI). This review aims to examine health-related long-term outcomes after cardiac arrest.

RECENT FINDINGS

A notable portion of cardiac arrest survivors face a decline in their quality of life, encountering persistent physical, cognitive, and mental health challenges emerging years after the initial event. Within the first-year postarrest, survivors are at elevated risk for stroke, epilepsy, and psychiatric conditions, along with a heightened susceptibility to developing dementia. Addressing these challenges necessitates establishing comprehensive, multidisciplinary care systems tailored to the needs of these individuals.

SUMMARY

HIBI remains the leading cause of disability among cardiac arrest survivors. No single strategy is likely to improve long term outcomes after cardiac arrest. A multimodal neuroprognostication approach (clinical examination, imaging, neurophysiology, and biomarkers) is recommended by guidelines, but fails to predict long-term outcomes. Cardiac arrest survivors often experience long-term disabilities that negatively impact their quality of life. The likelihood of such outcomes implements a multidisciplinary care an integral part of long-term recovery.

Multiomic biomarkers after cardiac arrest

Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after cardiac arrest. This review article delves into the current knowledge of cardiac arrest biomarkers across various omic fields and suggests directions for future research aiming to integrate multiple omics data layers to improve outcome prediction and cardiac arrest patient's care.

Relevance of Continuous EEG versus Routine EEG for Outcome Prediction after Traumatic Brain Injury

INTRODUCTION

In a cohort of adult patients with disturbance of consciousness after TBI, we aimed to explore the relationship of continuous video-EEG (cEEG) versus routine EEG (rEEG) with mortality and functional outcome.

METHODS

This is a post hoc analysis of a randomized controlled trial (CERTA), in which adults with disorder of consciousness and needing EEG (excluding those with proven seizures/SE just before) were randomized 1:1 to cEEG or two rEEG. In TBI patients, correlation between EEG duration, mortality, and modified Rankin score (mRs, good 0-2) at 6 months was assessed.

RESULTS

Among 364 patients, 44 presenting with consciousness impairment after TBI were included; 29 randomized to cEEG and 15 to rEEG. Mortality (p = 0.88) and functional outcome (p = 0.58) at 6 months were similar between groups. There was a nonsignificant tendency toward more seizure/status epilepticus detection with cEEG (p = 0.08). In multivariable regression, cEEG was not related to functional outcome (OR: 0.75 [0.13-4.24], p = 0.745) or mortality (OR: 7.11 [0.51-99.32], p = 0.145).

CONCLUSION

Despite allowing increased seizure detections in TBI patients, cEEG does not seem to be associated with better functional outcome or mortality over rEEG. Pending larger trials, repeated rEEG might be acceptable in post-TBI disorder of consciousness, especially in resource-limited environments.

Performance of the ERC/ESICM-recommendations for neuroprognostication after cardiac arrest: Insights from a prospective multicenter cohort

AIM

To investigate the performance of the 2021 ERC/ESICM-recommended algorithm for predicting poor outcome after cardiac arrest (CA) and potential tools for predicting neurological recovery in patients with indeterminate outcome.

METHODS

Prospective, multicenter study on out-of-hospital CA survivors from 28 ICUs of the AfterROSC network. In patients comatose with a Glasgow Coma Scale motor score ≤3 at ≥72 h after resuscitation, we measured: (1) the accuracy of neurological examination, biomarkers (neuron-specific enolase, NSE), electrophysiology (EEG and SSEP) and neuroimaging (brain CT and MRI) for predicting poor outcome (modified Rankin scale score ≥4 at 90 days), and (2) the ability of low or decreasing NSE levels and benign EEG to predict good outcome in patients whose prognosis remained indeterminate.

RESULTS

Among 337 included patients, the ERC-ESICM algorithm predicted poor neurological outcome in 175 patients, and the positive predictive value for an unfavourable outcome was 100% [98-100]%. The specificity of individual predictors ranged from 90% for EEG to 100% for clinical examination and SSEP. Among the remaining 162 patients with indeterminate outcome, a combination of 2 favourable signs predicted good outcome with 99[96-100]% specificity and 23[11-38]% sensitivity.

CONCLUSION

All comatose resuscitated patients who fulfilled the ERC-ESICM criteria for poor outcome after CA had poor outcome at three months, even if a self-fulfilling prophecy cannot be completely excluded. In patients with indeterminate outcome (half of the population), favourable signs predicted neurological recovery, reducing prognostic uncertainty.

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.

Heart rate variability for neuro-prognostication after CA: Insight from the Parisian registry

BACKGROUND

Hypoxic ischemic brain injury (HIBI) induced by cardiac arrest (CA) seems to predominate in cortical areas and to a lesser extent in the brainstem. These regions play key roles in modulating the activity of the autonomic nervous system (ANS), that can be assessed through analyses of heart rate variability (HRV). The objective was to evaluate the prognostic value of various HRV parameters to predict neurological outcome after CA.

METHODS

Retrospective monocentric study assessing the prognostic value of HRV markers and their association with HIBI severity. Patients admitted for CA who underwent EEG for persistent coma after CA were included. HRV markers were computed from 5 min signal of the ECG lead of the EEG recording. HRV indices were calculated in the time-, frequency-, and non-linear domains. Frequency-domain analyses differentiated very low frequency (VLF 0.003-0.04 Hz), low frequency (LF 0.04-0.15 Hz), high frequency (HF 0.15-0.4 Hz), and LF/HF ratio. HRV indices were compared to other prognostic markers: pupillary light reflex, EEG, N20 on somatosensory evoked potentials (SSEP) and biomarkers (neuron specific enolase-NSE). Neurological outcome at 3 months was defined as unfavorable in case of best CPC 3-4-5.

RESULTS

Between 2007 and 2021, 199 patients were included. Patients were predominantly male (64%), with a median age of 60 [48.9-71.7] years. 76% were out-of-hospital CA, and 30% had an initial shockable rhythm. Neurological outcome was unfavorable in 73%. Compared to poor outcome, patients with a good outcome had higher VLF (0.21 vs 0.09 ms2/Hz, p < 0.01), LF (0.07 vs 0.04 ms2/Hz, p = 0.003), and higher LF/HF ratio (2.01 vs 1.01, p = 0.008). Several non-linear domain indices were also higher in the good outcome group, such as SD2 (15.1 vs 10.2, p = 0.016) and DFA α1 (1.03 vs 0.78, p = 0.002). These indices also differed depending on the severity of EEG pattern and abolition of pupillary light reflex. These time-frequency and non-linear domains HRV parameters were predictive of poor neurological outcome, with high specificity despite a low sensitivity.

CONCLUSION

In comatose patients after CA, some HRV markers appear to be associated with unfavorable outcome, EEG severity and PLR abolition, although the sensitivity of these HRV markers remains limited.

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.

CT brain perfusion patterns and clinical outcome after successful cardiopulmonary resuscitation: A pilot study

AIM

CT perfusion is a valuable tool for evaluating cerebrovascular diseases, but its role in patients with hypoxic ischaemic encephalopathy is unclear. This study aimed to investigate 1) the patterns of cerebral perfusion changes that may occur early on after successful resuscitation, and 2) their correlation with clinical outcome to explore their value for predicting outcome.

METHODS

We conducted a retrospective analysis of perfusion maps from patients who underwent CT brain perfusion within 12 h following successful resuscitation. We classified the perfusion changes into distinct patterns. According to the cerebral performance category (CPC) score clinical outcome was categorised as favourable (CPC 1-2), or unfavourable (CPC 3-5).

RESULTS

A total of 87 patients were included of whom 33 had a favourable outcome (60.6% male, mean age 60 ± 16 years), whereas 54 exhibited an unfavourable outcome (59.3% male, mean age 60 ± 19 years). Of the patients in the favourable outcome group, 30.3% showed no characteristic perfusion changes, in contrast to the unfavourable outcome group where all patients exhibit changes in perfusion. Eighteen perfusion patterns were identified. The most significant patterns for prediction of unfavourable outcome in terms of their high specificity and frequency were hypoperfusion of the brainstem as well as coexisting hypoperfusion of the brainstem and thalamus.

CONCLUSION

This pilot study identified various perfusion patterns in patients after resuscitation, indicative of circulatory changes associated with post-cardiac-arrest brain injury. After validation, certain patterns could potentially be used in conjunction with other prognostic markers for stratifying patients and adjusting personalized treatment following cardiopulmonary resuscitation. Normal brain perfusion within 12 h after resuscitation is predictive of favourable outcome with high specificity.

Quantitative EEG reactivity induced by electrical stimulation predicts good outcome in comatose patients after cardiac arrest

BACKGROUND

EEG reactivity is a predictor for neurological outcome in comatose patients after cardiac arrest (CA); however, its application is limited by variability in stimulus types and visual assessment. We aimed to evaluate the prognostic value of the quantitative analysis of EEG reactivity induced by standardized electrical stimulation and for early prognostication in this population.

METHODS

This prospective observational study recruited post-CA comatose patients in Xuanwu Hospital, Capital Medical University (Beijing, China) between January 2016 and June 2023. EEG reactivity to electrical or traditional pain stimulation was randomly performed via visual and quantitative analysis. Neurological outcome within 6 months was dichotomized as good (Cerebral Performance Categories, CPC 1-2) or poor (CPC 3-5).

RESULTS

Fifty-eight post-CA comatose patients were admitted, and 52 patients were included in the final analysis, of which 19 (36.5%) had good outcomes. EEG reactivity induced with the electrical stimulation had superior performance to the traditional pain stimulation for good outcome prediction (quantitative analysis: AUC 0.932 vs. 0.849, p = 0.048). When using the electrical stimulation, the AUC of EEG reactivity to predict good outcome by visual analysis was 0.838, increasing to 0.932 by quantitative analysis (p = 0.039). Comparing to the traditional pain stimulation by visual analysis, the AUC of EEG reactivity for good prognostication by the electrical stimulation with quantitative analysis was significantly improved (0.932 vs. 0.770, p = 0.004).

CONCLUSIONS

EEG reactivity induced by the standardized electrical stimulation in combination with quantitative analysis is a promising formula for post-CA comatose patients, with increased predictive accuracy.

Emergent Management of Hypoxic-Ischemic Brain Injury

OBJECTIVE

This article outlines interventions used to improve outcomes for patients with hypoxic-ischemic brain injury after cardiac arrest.

LATEST DEVELOPMENTS

Emergent management of patients after cardiac arrest requires prevention and treatment of primary and secondary brain injury. Primary brain injury is minimized by excellent initial resuscitative efforts. Secondary brain injury prevention requires the detection and correction of many pathophysiologic processes that may develop in the hours to days after the initial arrest. Key physiologic parameters important to secondary brain injury prevention include optimization of mean arterial pressure, cerebral perfusion, oxygenation and ventilation, intracranial pressure, temperature, and cortical hyperexcitability. This article outlines recent data regarding the treatment and prevention of secondary brain injury. Different patients likely benefit from different treatment strategies, so an individualized approach to treatment and prevention of secondary brain injury is advisable. Clinicians must use multimodal sources of data to prognosticate outcomes after cardiac arrest while recognizing that all prognostic tools have shortcomings.

ESSENTIAL POINTS

Neurologists should be involved in the postarrest care of patients with hypoxic-ischemic brain injury to improve their outcomes. Postarrest care requires nuanced and patient-centered approaches to the prevention and treatment of primary and secondary brain injury and neuroprognostication.

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.

The Role of Routine Electroencephalography in the Diagnosis of Seizures in Medical Intensive Care Units

Seizures should be diagnosed and treated to ensure optimal health outcomes in critically ill patients admitted in the medical intensive care unit (MICU). Continuous electroencephalography is still infrequently used in the MICU. We investigated the effectiveness of routine EEG (rEEG) in detecting seizures in the MICU. A total of 560 patients admitted to the MICU between October 2018 and March 2023 and who underwent rEEG were reviewed. Seizure-related rEEG constituted 47% of all rEEG studies. Totally, 39% of the patients experienced clinical seizures during hospitalization; among them, 48% experienced the seizure, and 13% experienced their first seizure after undergoing an rEEG study. Seventy-seven percent of the patients had unfavorable short-term outcomes. Patients with cardiovascular diseases were the most likely to have the suppression/burst suppression (SBS) EEG pattern and the highest mortality rate. The rhythmic and periodic patterns (RPPs) and electrographic seizure (ESz) EEG pattern were associated with seizures within 24 h after rEEG, which was also related to unfavorable outcomes. Significant predictors of death were age > 59 years, the male gender, the presence of cardiovascular disease, a Glasgow Coma Scale score ≤ 5, and the SBS EEG pattern, with a predictive performance of 0.737 for death. rEEG can help identify patients at higher risk of seizures. We recommend repeated rEEG in patients with ESz or RPP EEG patterns to enable a more effective monitoring of seizure activities.

Prognostic value of early EEG abnormalities in severe stroke patients requiring mechanical ventilation: a pre-planned analysis of the SPICE prospective multicenter study

INTRODUCTION

Prognostication of outcome in severe stroke patients necessitating invasive mechanical ventilation poses significant challenges. The objective of this study was to assess the prognostic significance and prevalence of early electroencephalogram (EEG) abnormalities in adult stroke patients receiving mechanical ventilation.

METHODS

This study is a pre-planned ancillary investigation within the prospective multicenter SPICE cohort study (2017-2019), conducted in 33 intensive care units (ICUs) in the Paris area, France. We included adult stroke patients requiring invasive mechanical ventilation, who underwent at least one intermittent EEG examination during their ICU stay. The primary endpoint was the functional neurological outcome at one year, determined using the modified Rankin scale (mRS), and dichotomized as unfavorable (mRS 4-6, indicating severe disability or death) or favorable (mRS 0-3). Multivariable regression analyses were employed to identify EEG abnormalities associated with functional outcomes.

RESULTS

Of the 364 patients enrolled in the SPICE study, 153 patients (49 ischemic strokes, 52 intracranial hemorrhages, and 52 subarachnoid hemorrhages) underwent at least one EEG at a median time of 4 (interquartile range 2-7) days post-stroke. Rates of diffuse slowing (70% vs. 63%, p = 0.37), focal slowing (38% vs. 32%, p = 0.15), periodic discharges (2.3% vs. 3.7%, p = 0.9), and electrographic seizures (4.5% vs. 3.7%, p = 0.4) were comparable between patients with unfavorable and favorable outcomes. Following adjustment for potential confounders, an unreactive EEG background to auditory and pain stimulations (OR 6.02, 95% CI 2.27-15.99) was independently associated with unfavorable outcomes. An unreactive EEG predicted unfavorable outcome with a specificity of 48% (95% CI 40-56), sensitivity of 79% (95% CI 72-85), and positive predictive value (PPV) of 74% (95% CI 67-81). Conversely, a benign EEG (defined as continuous and reactive background activity without seizure, periodic discharges, triphasic waves, or burst suppression) predicted favorable outcome with a specificity of 89% (95% CI 84-94), and a sensitivity of 37% (95% CI 30-45).

CONCLUSION

The absence of EEG reactivity independently predicts unfavorable outcomes at one year in severe stroke patients requiring mechanical ventilation in the ICU, although its prognostic value remains limited. Conversely, a benign EEG pattern was associated with a favorable outcome.

Postarrest Neuroprognostication: Practices and Opinions of Canadian Physicians

BACKGROUND

Objective, evidence-based neuroprognostication of postarrest patients is crucial to avoid inappropriate withdrawal of life-sustaining therapies or prolonged, invasive, and costly therapies that could perpetuate suffering when there is no chance of an acceptable recovery. Postarrest prognostication guidelines exist; however, guideline adherence and practice variability are unknown.

OBJECTIVE

To investigate Canadian practices and opinions regarding assessment of neurological prognosis in postarrest patients.

METHODS

An anonymous electronic survey was distributed to physicians who care for adult postarrest patients.

RESULTS

Of the 134 physicians who responded to the survey, 63% had no institutional protocols for neuroprognostication. While the use of targeted temperature management did not affect the timing of neuroprognostication, an increasing number of clinical findings suggestive of a poor prognosis affected the timing of when physicians were comfortable concluding patients had a poor prognosis. Variability existed in what factors clinicians' thought were confounders. Physicians identified bilaterally absent pupillary light reflexes (85%), bilaterally absent corneal reflexes (80%), and status myoclonus (75%) as useful in determining poor prognosis. Computed tomography, magnetic resonance imaging, and spot electroencephalography were the most useful and accessible tests. Somatosensory evoked potentials were useful, but logistically challenging. Serum biomarkers were unavailable at most centers. Most (79%) physicians agreed ≥2 definitive findings on neurologic exam, electrophysiologic tests, neuroimaging, and/or biomarkers are required to determine a poor prognosis with a high degree of certainty. Distress during the process of neuroprognostication was reported by 70% of physicians and 51% request a second opinion from an external expert.

CONCLUSION

Significant variability exists in post-cardiac arrest neuroprognostication practices among Canadian physicians.

The Evolving Role of Electroencephalography in Postarrest Care

Electroencephalography is an accessible, portable, noninvasive and safe means of evaluating a patient's brain activity. It can aid in diagnosis and management decisions for post-cardiac arrest patients with seizures, myoclonus and other non-epileptic movements. It also plays an important role in a multimodal approach to neuroprognostication predicting both poor and favorable outcomes. Individuals ordering, performing and interpreting these tests, regardless of the indication, should understand the supporting evidence, logistical considerations, limitations and impact the results may have on postarrest patients and their families as outlined herein.

Aberrant brain-heart coupling is associated with the severity of post cardiac arrest brain injury

OBJECTIVE

To investigate autonomic nervous system activity measured by brain-heart interactions in comatose patients after cardiac arrest in relation to the severity and prognosis of hypoxic-ischemic brain injury.

METHODS

Strength and complexity of bidirectional interactions between EEG frequency bands (delta, theta, and alpha) and ECG heart rate variability frequency bands (low frequency, LF and high frequency, HF) were computed using a synthetic data generation model. Primary outcome was the severity of brain injury, assessed by (i) standardized qualitative EEG classification, (ii) somatosensory evoked potentials (N20), and (iii) neuron-specific enolase levels. Secondary outcome was the 3-month neurological status, assessed by the Cerebral Performance Category score [good (1-2) vs. poor outcome (3-4-5)].

RESULTS

Between January 2007 and July 2021, 181 patients were admitted to ICU for a resuscitated cardiac arrest. Poor neurological outcome was observed in 134 patients (74%). Qualitative EEG patterns suggesting high severity were associated with decreased LF/HF. Severity of EEG changes were proportional to higher absolute values of brain-to-heart coupling strength (p < 0.02 for all brain-to-heart frequencies) and lower values of alpha-to-HF complexity (p = 0.049). Brain-to-heart coupling strength was significantly higher in patients with bilateral absent N20 and correlated with neuron-specific enolase levels at Day 3. This aberrant brain-to-heart coupling (increased strength and decreased complexity) was also associated with 3-month poor neurological outcome.

INTERPRETATION

Our results suggest that autonomic dysfunctions may well represent hypoxic-ischemic brain injury post cardiac arrest pathophysiology. These results open avenues for integrative monitoring of autonomic functioning in critical care patients.

EEG Pattern With Spectral Analysis Can Prognosticate Good and Poor Neurologic Outcomes After Cardiac Arrest

PURPOSE

To investigate the prognostic value of a simple stratification system of electroencephalographical (EEG) patterns and spectral types for patients after cardiac arrest.

METHODS

In this prospectively enrolled cohort, using manually selected EEG segments, patients after cardiac arrest were stratified into five independent EEG patterns (based on background continuity and burden of highly epileptiform discharges) and four independent power spectral types (based on the presence of frequency components). The primary outcome is cerebral performance category (CPC) at discharge. Results from multimodal prognostication testing were included for comparison.

RESULTS

Of a total of 72 patients, 6 had CPC 1-2 by discharge, all of whom had mostly continuous EEG background without highly epileptiform activity at day 3. However, for the same EEG background pattern at day 3, 19 patients were discharged at CPC 3 and 15 patients at CPC 4-5. After adding spectral analysis, overall sensitivity for predicting good outcomes (CPC 1-2) was 83.3% (95% confidence interval 35.9% to 99.6%) and specificity was 97.0% (89.5% to 99.6%). In this cohort, standard prognostication testing all yielded 100% specificity but low sensitivity, with imaging being the most sensitive at 54.1% (36.9% to 70.5%).

CONCLUSIONS

Adding spectral analysis to qualitative EEG analysis may further improve the diagnostic accuracy of EEG and may aid developing novel measures linked to good outcomes in postcardiac arrest coma.

2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces

The International Liaison Committee on Resuscitation engages in a continuous review of new, peer-reviewed, published cardiopulmonary resuscitation and first aid science. Draft Consensus on Science With Treatment Recommendations are posted online throughout the year, and this annual summary provides more concise versions of the final Consensus on Science With Treatment Recommendations from all task forces for the year. Topics addressed by systematic reviews this year include resuscitation of cardiac arrest from drowning, extracorporeal cardiopulmonary resuscitation for adults and children, calcium during cardiac arrest, double sequential defibrillation, neuroprognostication after cardiac arrest for adults and children, maintaining normal temperature after preterm birth, heart rate monitoring methods for diagnostics in neonates, detection of exhaled carbon dioxide in neonates, family presence during resuscitation of adults, and a stepwise approach to resuscitation skills training. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces list priority knowledge gaps for further research. Additional topics are addressed with scoping reviews and evidence updates.

Critical Care Management of Patients After Cardiac Arrest: A Scientific Statement from the American Heart Association and Neurocritical Care Society

The critical care management of patients after cardiac arrest is burdened by a lack of high-quality clinical studies and the resultant lack of high-certainty evidence. This results in limited practice guideline recommendations, which may lead to uncertainty and variability in management. Critical care management is crucial in patients after cardiac arrest and affects outcome. Although guidelines address some relevant topics (including temperature control and neurological prognostication of comatose survivors, 2 topics for which there are more robust clinical studies), many important subject areas have limited or nonexistent clinical studies, leading to the absence of guidelines or low-certainty evidence. The American Heart Association Emergency Cardiovascular Care Committee and the Neurocritical Care Society collaborated to address this gap by organizing an expert consensus panel and conference. Twenty-four experienced practitioners (including physicians, nurses, pharmacists, and a respiratory therapist) from multiple medical specialties, levels, institutions, and countries made up the panel. Topics were identified and prioritized by the panel and arranged by organ system to facilitate discussion, debate, and consensus building. Statements related to postarrest management were generated, and 80% agreement was required to approve a statement. Voting was anonymous and web based. Topics addressed include neurological, cardiac, pulmonary, hematological, infectious, gastrointestinal, endocrine, and general critical care management. Areas of uncertainty, areas for which no consensus was reached, and future research directions are also included. Until high-quality studies that inform practice guidelines in these areas are available, the expert panel consensus statements that are provided can advise clinicians on the critical care management of patients after cardiac arrest.

Recovery and Survival of Patients After Out-of-Hospital Cardiac Arrest: A Literature Review Showcasing the Big Picture of Intensive Care Unit-Related Factors

As an important public health issue, out-of-hospital cardiac arrest (OHCA) requires several stages of high quality medical care, both on-field and after hospital admission. Post-cardiac arrest shock can lead to severe neurological injury, resulting in poor recovery outcome and increased risk of death. These characteristics make this condition one of the most important issues to deal with in post-OHCA patients hospitalized in intensive care units (ICUs). Also, the majority of initial post-resuscitation survivors have underlying coronary diseases making revascularization procedure another crucial step in early management of these patients. Besides keeping myocardial blood flow at a satisfactory level, other tissues must not be neglected as well, and maintaining mean arterial pressure within optimal range is also preferable. All these procedures can be simplified to a certain level along with using targeted temperature management methods in order to decrease metabolic demands in ICU-hospitalized post-OHCA patients. Additionally, withdrawal of life-sustaining therapy as a controversial ethical topic is under constant re-evaluation due to its possible influence on overall mortality rates in patients initially surviving OHCA. Focusing on all of these important points in process of managing ICU patients is an imperative towards better survival and complete recovery rates.

Outcome prediction in comatose cardiac arrest patients with initial shockable and non-shockable rhythms

BACKGROUND

Prognosis after out-of-hospital cardiac arrest (OHCA) is presumed poorer in patients with non-shockable than shockable rhythms, frequently leading to treatment withdrawal. Multimodal outcome prediction is recommended 72 h post-arrest in still comatose patients, not considering initial rhythms. We investigated accuracy of outcome predictors in all comatose OHCA survivors, with a particular focus on shockable vs. non-shockable rhythms.

METHODS

In this observational NORCAST sub-study, patients still comatose 72 h post-arrest were stratified by shockable vs. non-shockable rhythms for outcome prediction analyzes. Good outcome was defined as cerebral performance category 1-2 within 6 months. False positive rate (FPR) was used for poor and sensitivity for good outcome prediction accuracy.

RESULTS

Overall, 72/128 (56%) patients with shockable and 12/50 (24%) with non-shockable rhythms had good outcome (p < .001). For poor outcome prediction, absent pupillary light reflexes (PLR) and corneal reflexes (clinical predictors) 72 h after sedation withdrawal, PLR 96 h post-arrest, and somatosensory evoked potentials (SSEP), all had FPR <0.1% in both groups. Unreactive EEG and neuron-specific enolase (NSE) >60 μg/L 24-72 h post-arrest had better precision in shockable patients. For good outcome, the clinical predictors, SSEP and CT, had 86%-100% sensitivity in both groups. For NSE, sensitivity varied from 22% to 69% 24-72 h post-arrest. The outcome predictors indicated severe brain injury proportionally more often in patients with non-shockable than with shockable rhythms. For all patients, clinical predictors, CT, and SSEP, predicted poor and good outcome with high accuracy.

CONCLUSION

Outcome prediction accuracy was comparable for shockable and non-shockable rhythms. PLR and corneal reflexes had best precision 72 h after sedation withdrawal and 96 h post-arrest.

Post Cardiac Arrest Care in the Cardiac Intensive Care Unit

PURPOSE OF REVIEW

Cardiac arrests constitute a leading cause of mortality in the adult population and cardiologists are often tasked with the management of patients following cardiac arrest either as a consultant or primary provider in the cardiac intensive care unit. Familiarity with evidence-based practice for post-cardiac arrest care is a requisite for optimizing outcomes in this highly morbid group. This review will highlight important concepts necessary to managing these patients.

RECENT FINDINGS

Emerging evidence has further elucidated optimal care of post-arrest patients including timing for routine coronary angiography, utility of therapeutic hypothermia, permissive hypercapnia, and empiric aspiration pneumonia treatment. The complicated state of multi-organ failure following cardiac arrest needs to be carefully optimized by the clinician to prevent further neurologic injury and promote systemic recovery. Future studies should be aimed at understanding if these findings extend to specific patient populations, especially those at the highest risk for poor outcomes.

Critical Care Management of Patients After Cardiac Arrest: A Scientific Statement From the American Heart Association and Neurocritical Care Society

The critical care management of patients after cardiac arrest is burdened by a lack of high-quality clinical studies and the resultant lack of high-certainty evidence. This results in limited practice guideline recommendations, which may lead to uncertainty and variability in management. Critical care management is crucial in patients after cardiac arrest and affects outcome. Although guidelines address some relevant topics (including temperature control and neurological prognostication of comatose survivors, 2 topics for which there are more robust clinical studies), many important subject areas have limited or nonexistent clinical studies, leading to the absence of guidelines or low-certainty evidence. The American Heart Association Emergency Cardiovascular Care Committee and the Neurocritical Care Society collaborated to address this gap by organizing an expert consensus panel and conference. Twenty-four experienced practitioners (including physicians, nurses, pharmacists, and a respiratory therapist) from multiple medical specialties, levels, institutions, and countries made up the panel. Topics were identified and prioritized by the panel and arranged by organ system to facilitate discussion, debate, and consensus building. Statements related to postarrest management were generated, and 80% agreement was required to approve a statement. Voting was anonymous and web based. Topics addressed include neurological, cardiac, pulmonary, hematological, infectious, gastrointestinal, endocrine, and general critical care management. Areas of uncertainty, areas for which no consensus was reached, and future research directions are also included. Until high-quality studies that inform practice guidelines in these areas are available, the expert panel consensus statements that are provided can advise clinicians on the critical care management of patients after cardiac arrest.

The predictive value of highly malignant EEG patterns after cardiac arrest: evaluation of the ERC-ESICM recommendations

PURPOSE

The 2021 guidelines endorsed by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) recommend using highly malignant electroencephalogram (EEG) patterns (HMEP; suppression or burst-suppression) at > 24 h after cardiac arrest (CA) in combination with at least one other concordant predictor to prognosticate poor neurological outcome. We evaluated the prognostic accuracy of HMEP in a large multicentre cohort and investigated the added value of absent EEG reactivity.

METHODS

This is a pre-planned prognostic substudy of the Targeted Temperature Management trial 2. The presence of HMEP and background reactivity to external stimuli on EEG recorded > 24 h after CA was prospectively reported. Poor outcome was measured at 6 months and defined as a modified Rankin Scale score of 4-6. Prognostication was multimodal, and withdrawal of life-sustaining therapy (WLST) was not allowed before 96 h after CA.

RESULTS

845 patients at 59 sites were included. Of these, 579 (69%) had poor outcome, including 304 (36%) with WLST due to poor neurological prognosis. EEG was recorded at a median of 71 h (interquartile range [IQR] 52-93) after CA. HMEP at > 24 h from CA had 50% [95% confidence interval [CI] 46-54] sensitivity and 93% [90-96] specificity to predict poor outcome. Specificity was similar (93%) in 541 patients without WLST. When HMEP were unreactive, specificity improved to 97% [94-99] (p = 0.008).

CONCLUSION

The specificity of the ERC-ESICM-recommended EEG patterns for predicting poor outcome after CA exceeds 90% but is lower than in previous studies, suggesting that large-scale implementation may reduce their accuracy. Combining HMEP with an unreactive EEG background significantly improved specificity. As in other prognostication studies, a self-fulfilling prophecy bias may have contributed to observed results.

2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces

The International Liaison Committee on Resuscitation engages in a continuous review of new, peer-reviewed, published cardiopulmonary resuscitation and first aid science. Draft Consensus on Science With Treatment Recommendations are posted online throughout the year, and this annual summary provides more concise versions of the final Consensus on Science With Treatment Recommendations from all task forces for the year. Topics addressed by systematic reviews this year include resuscitation of cardiac arrest from drowning, extracorporeal cardiopulmonary resuscitation for adults and children, calcium during cardiac arrest, double sequential defibrillation, neuroprognostication after cardiac arrest for adults and children, maintaining normal temperature after preterm birth, heart rate monitoring methods for diagnostics in neonates, detection of exhaled carbon dioxide in neonates, family presence during resuscitation of adults, and a stepwise approach to resuscitation skills training. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces list priority knowledge gaps for further research. Additional topics are addressed with scoping reviews and evidence updates.

Longitudinal serum neurofilament light kinetics in post-anoxic encephalopathy

Serum neurofilament light (sNfL) is a promising marker of outcome after cardiac arrest, but its kinetics are unclear. We prospectively measured sNfL concentrations in 62 patients at 0, 1, 3, 5, 7 and 10 days after cardiac arrest. Survivors and non-survivors had similar sNfL at admission (14.2 [8.6-21.9] vs. 22.5 [14.2-46.9] pg/mL) but largely different at 24 h (16.4 [10.2-293] vs. 464.3 [151.8-1658.2], respectively). The AUC for sNfL concentrations predicting death was above 0.95 from Day 1 to 10 (highest on Day 3). Late sNfL measurements may exert prognostic value, especially when early samples are unavailable or prognosis remains unclear.

Measurement of thalamus and cortical damages in hypoxic ischemic encephalopathy

Background

The thalamic gray-white matter ratios (GWRs) on CT and quantitative suppression ratios (SRs) of background activities on EEG may reflect damages in the thalamus and cerebral hemispheres in patients with hypoxic-ischemic encephalopathy (HIE).

Methods

The inclusion criteria were (1) cardiac arrest patients over the age of 20 years from March 2010 to March 2020, and (2) patients who had both EEG and brain CT within 7 days after cardiac arrest. The thalamic GWRs were semi-quantitatively measured by using the region of interest (ROI). SRs of background were analyzed with the installed software (Persyst® v13) in EEG machine.

Results

175 patients were included among 686 patients with HIE and the thalamic GWRs of 168 patients were successfully measured. 155 patients (89 %) showed poor outcomes. The poor outcome group revealed not only higher SRs, but also lower thalamic GWRs. The thalamic GWRs showed a negative correlation to the SRs (ρ (rho) = -0.36, p < 0.0001 for right side, ρ (rho) = -0.31, p < 0.0001 for left side). The good outcome group showed neither beyond the cut-off values of thalamic GWRs nor SRs [40 % (59/148) VS 0 % (0/20) in right side, p = 0.0005 %, and 28 % (42/148) VS 0 % (0/20) in left side, p = 0.0061].

Conclusion

The thalamic GWRs and SRs may reflect the damage in the thalamus and cerebral hemispheres in patients with HIE. Insults in the thalamocortical circuit (TCC) or the thalamus might be responsible for the poor outcome.