Prognostication after cardiac arrest: how EEG and evoked potentials may improve the challenge

Revolutionizing treatment for disorders of consciousness: a multidisciplinary review of advancements in deep brain stimulation

Among the existing research on the treatment of disorders of consciousness (DOC), deep brain stimulation (DBS) offers a highly promising therapeutic approach. This comprehensive review documents the historical development of DBS and its role in the treatment of DOC, tracing its progression from an experimental therapy to a detailed modulation approach based on the mesocircuit model hypothesis. The mesocircuit model hypothesis suggests that DOC arises from disruptions in a critical network of brain regions, providing a framework for refining DBS targets. We also discuss the multimodal approaches for assessing patients with DOC, encompassing clinical behavioral scales, electrophysiological assessment, and neuroimaging techniques methods. During the evolution of DOC therapy, the segmentation of central nuclei, the recording of single-neurons, and the analysis of local field potentials have emerged as favorable technical factors that enhance the efficacy of DBS treatment. Advances in computational models have also facilitated a deeper exploration of the neural dynamics associated with DOC, linking neuron-level dynamics with macroscopic behavioral changes. Despite showing promising outcomes, challenges remain in patient selection, precise target localization, and the determination of optimal stimulation parameters. Future research should focus on conducting large-scale controlled studies to delve into the pathophysiological mechanisms of DOC. It is imperative to further elucidate the precise modulatory effects of DBS on thalamo-cortical and cortico-cortical functional connectivity networks. Ultimately, by optimizing neuromodulation strategies, we aim to substantially enhance therapeutic outcomes and greatly expedite the process of consciousness recovery in patients.

Role of artificial intelligence in predicting neurological outcomes in postcardiac resuscitation

Being an extremely high mortality rate condition, cardiac arrest cases have rightfully been evaluated via various studies and scoring factors for effective resuscitative practices and neurological outcomes postresuscitation. This narrative review aims to explore the role of artificial intelligence (AI) in predicting neurological outcomes postcardiac resuscitation. The methodology involved a detailed review of all relevant recent studies of AI, different machine learning algorithms, prediction tools, and assessing their benefit in predicting neurological outcomes in postcardiac resuscitation cases as compared to more traditional prognostic scoring systems and tools. Previously, outcome determining clinical, blood, and radiological factors were prone to other influencing factors like limited accuracy and time constraints. Studies conducted also emphasized that to predict poor neurological outcomes, a more multimodal approach helped adjust for confounding factors, interpret diverse datasets, and provide a reliable prognosis, which only demonstrates the need for AI to help overcome challenges faced. Advanced machine learning algorithms like artificial neural networks (ANN) using supervised learning by AI have improved the accuracy of prognostic models outperforming conventional models. Several real-world cases of effective AI-powered algorithm models have been cited here. Studies comparing machine learning tools like XGBoost, AI Watson, hyperspectral imaging, ChatGPT-4, and AI-based gradient boosting have noted their beneficial uses. AI could help reduce workload, healthcare costs, and help personalize care, process vast genetic and lifestyle data and help reduce side effects from treatments. Limitations of AI have been covered extensively in this article, including data quality, bias, privacy issues, and transparency. Our objectives should be to use more diverse data sources, use interpretable data output giving process explanation, validation method, and implement policies to safeguard patient data. Despite the limitations, the advancements already made by AI and its potential in predicting neurological outcomes in postcardiac resuscitation cases has been quite promising and boosts a continually improving system, albeit requiring close human supervision with training and improving models, with plans to educate clinicians, the public and sharing collected data.

Simultaneous EEG recording of cortical tracking of speech and movement kinematics

RATIONALE

Cortical activity is coupled with streams of sensory stimulation. The coupling with the temporal envelope of heard speech is known as the cortical tracking of speech (CTS), and that with movement kinematics is known as the corticokinematic coupling (CKC). Simultaneous measurement of both couplings is desirable in clinical settings, but it is unknown whether the inherent dual-tasking condition has an impact on CTS or CKC.

AIM

We aim to determine whether and how CTS and CKC levels are affected when recorded simultaneously.

METHODS

Twenty-three healthy young adults underwent 64-channel EEG recordings while listening to stories and while performing repetitive finger-tapping movements in 3 conditions: separately (audio- or tapping-only) or simultaneously (audio-tapping). CTS and CKC values were estimated using coherence analysis between each EEG signal and speech temporal envelope (CTS) or finger acceleration (CKC). CTS was also estimated as the reconstruction accuracy of a decoding model.

RESULTS

Across recordings, CTS assessed with reconstruction accuracy was significant in 85 % of the subjects at phrasal frequency (0.5 Hz) and in 68 % at syllabic frequencies (4-8 Hz), and CKC was significant in over 85 % of the subjects at movement frequency and its first harmonic. Comparing CTS and CKC values evaluated in separate recordings to those in simultaneous recordings revealed no significant difference and moderate-to-high levels of correlation.

CONCLUSION

Despite the subtle behavioral effects, CTS and CKC are not evidently altered by the dual-task setting inherent to recording them simultaneously and can be evaluated simultaneously using EEG in clinical settings.

EEG reactivity in neurologic prognostication in post-cardiac arrest patients: A narrative review

Electroencephalographic reactivity (EEG-R) is a promising early predictor of arousal in comatose patients after cardiac arrest. Despite recent guidelines advocating for the integration of EEG-R into the multimodal prognostication model, EEG-R testing methods remain heterogeneous across studies. While efforts towards standardization have been made to reduce interrater variability by the development of quantitative approaches and machine learning models, future validation studies are needed to increase clinical applicability. Furthermore, the specific neurophysiological mechanisms and neuroanatomical correlates underlying EEG-R are not fully understood. In this narrative review, we explore the value and possible mechanisms of EEG-R, focusing on post-cardiac arrest comatose patients. We aim to discuss the current standard of knowledge and future directions, as well as elucidate possible implications for patient care and research.

Acupuncture-assisted therapy for prolonged disorders of consciousness: study protocol for a randomized, conventional-controlled, assessor-and-statistician-blinded trial

Background

Acupuncture is a promising non-pharmaceutical complementary therapy in treating prolonged Disorders of consciousness (pDOC), but solid evidence to support its effectiveness and safety is still lacking. Thus, the purpose of this study is to investigate the efficacy and safety of acupuncture-assisted therapy for pDOC patients.

Methods

A single-center, prospective, randomized, conventional-controlled, assessor-and-statistician-blinded trial has been designed and is being conducted at West China Hospital of Sichuan University. A total of 110 participants will be randomly assigned to the experimental group and the control group in a 1:1 allocation ratio and evaluated using Coma Recovery Scale-Revised (CRS-R) at 8 a.m., 12 p.m., and 4 p.m. on 2 consecutive days before enrollment to determine the consciousness level. The experimental group will receive acupuncture combined with conventional treatment, while the control group will receive only conventional treatment during the trial observation period. The treatment duration of both groups will be 20 days. Among them, the frequency of acupuncture-assisted therapy is once a day, with 10 consecutive sessions followed by a day's rest for a total of 24 days. Data will be collected separately during baseline and after the final treatment. For data analysis, both Full Analysis Set (FAS) and Per Protocol Set (PPS) principles will be performed together by applying SPSS 27.0 software. The primary outcome measures are the changes of CRS-R before and after treatment, while the secondary outcome measures are the changes of Full Outline of Unresponsiveness Scale (FOUR), the changes of Nociception Coma Scale-Revised (NCS-R), the changes of Disability Rating Scale (DRS), the changes of Mismatch Negativity (MMN) and P300 before and after treatment, respectively.

Discussion

This trial aims to rationally assess the consciousness level from multiple 2 perspectives through subjective evaluation and objective detection by selecting several standardized clinical scales combined with Event-Related Potential (ERP) detection technology. In this way, we will be able to reduce the subjectivity of consciousness assessment and objectively evaluate the clinical efficacy of acupuncture-assisted therapy for pDOC. The study, if proven to be effective and safe enough, will provide a favorable evidence to guide medical decision-making choices and future researches.

Clinical trial registration

https://www.chictr.org.cn/, identifier ChiCTR2300076180.

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.

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.

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.

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.

Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

Auditory processing and the complexity of neural activity can both indicate residual consciousness levels and differentiate states of arousal. However, how measures of neural signal complexity manifest in neural activity following environmental stimulation and, more generally, how the electrophysiological characteristics of auditory responses change in states of reduced consciousness remain under-explored. Here, we tested the hypothesis that measures of neural complexity and the spectral slope would discriminate stages of sleep and wakefulness not only in baseline electroencephalography (EEG) activity but also in EEG signals following auditory stimulation. High-density EEG was recorded in 21 participants to determine the spatial relationship between these measures and between EEG recorded pre- and post-auditory stimulation. Results showed that the complexity and the spectral slope in the 2-20 Hz range discriminated between sleep stages and had a high correlation in sleep. In wakefulness, complexity was strongly correlated to the 20-40 Hz spectral slope. Auditory stimulation resulted in reduced complexity in sleep compared to the pre-stimulation EEG activity and modulated the spectral slope in wakefulness. These findings confirm our hypothesis that electrophysiological markers of arousal are sensitive to sleep/wake states in EEG activity during baseline and following auditory stimulation. Our results have direct applications to studies using auditory stimulation to probe neural functions in states of reduced consciousness.

A Comprehensive Review and Practical Guide of the Applications of Evoked Potentials in Neuroprognostication After Cardiac Arrest

Cardiorespiratory arrest is a very common cause of morbidity and mortality nowadays, and many therapeutic strategies, such as induced coma or targeted temperature management, are used to reduce patient sequelae. However, these procedures can alter a patient's neurological status, making it difficult to obtain useful clinical information for the reliable estimation of neurological prognosis. Therefore, complementary investigations are conducted in the early stages after a cardiac arrest to clarify functional prognosis in comatose cardiac arrest survivors in the first few hours or days. Current practice relies on a multimodal approach, which shows its greatest potential in predicting poor functional prognosis, whereas the data and tools to identify patients with good functional prognosis remain relatively limited in comparison. Therefore, there is considerable interest in investigating alternative biological parameters and advanced imaging technique studies. Among these, somatosensory evoked potentials (SSEPs) remain one of the simplest and most reliable tools. In this article, we discuss the technical principles, advantages, limitations, and prognostic implications of SSEPs in detail. We will also review other types of evoked potentials that can provide useful information but are less commonly used in clinical practice (e.g., visual evoked potentials; short-, medium-, and long-latency auditory evoked potentials; and event-related evoked potentials, such as mismatch negativity or P300).

Prognosis of comatose patients with reduced EEG montage by combining quantitative EEG features in various domains

Objective

As the frontoparietal network underlies recovery from coma, a limited frontoparietal montage was used, and the prognostic values of EEG features for comatose patients were assessed.

Methods

Collected with a limited frontoparietal EEG montage, continuous EEG recordings of 81 comatose patients in ICU were used retrospectively. By the 60-day Glasgow outcome scale (GOS), the patients were dichotomized into favorable and unfavorable outcome groups. Temporal-, frequency-, and spatial-domain features were automatically extracted for comparison. Partial correlation analysis was applied to eliminate redundant factors, and multiple correspondence analysis was used to explore discrimination between groups. Prognostic characteristics were calculated to assess the performance of EEG feature-based predictors established by logistic regression. Analyses were performed on all-patients group, strokes subgroup, and traumatic brain injury (TBI) subgroup.

Results

By analysis of all patients, raised burst suppression ratio (BSR), suppressed root mean square (RMS), raised power ratio of β to α rhythm (β/α), and suppressed phase-lag index between F3 and P4 (PLI [F3, P4]) were associated with unfavorable outcome, and yielded AUC of 0.790, 0.811, 0.722, and 0.844, respectively. For the strokes subgroup, the significant variables were BSR, RMS, θ/total, θ/δ, and PLI (F3, P4), while for the TBI subgroup, only PLI (F3, P4) was significant. BSR combined with PLI (F3, P4) gave the best predictor by cross-validation analysis in the all-patients group (AUC = 0.889, 95% CI: 0.819-0.960).

Conclusion

Features extracted from limited frontoparietal montage EEG served as valuable coma prognostic tools, where PLI (F3, P4) was always significant. Combining PLI (F3, P4) with features in other domains may achieve better performance.

Significance

A limited-montage EEG coupled with an automated algorithm is valuable for coma prognosis.

Electroencephalography Prediction of Neurological Outcomes After Hypoxic-Ischemic Brain Injury: A Systematic Review and Meta-Analysis

Background. Predicting neurological outcomes after hypoxic-ischemic brain injury (HIBI) is difficult. Objective. Electroencephalography (EEG) can identify acute and subacute brain abnormalities after hypoxic brain injury and predict HIBI recovery. We examined EEG's ability to predict neurologic outcomes following HIBI. Method. A PRISMA-compliant search was conducted in the Medline, Embase, Cochrane, and Central databases until January 2023. EEG-predicted neurological outcomes in HIBI patients were selected from relevant perspective and retrospective cohort studies. RevMan did meta-analysis, while QDAS2 assessed research quality. Results. Eleven studies with 3761 HIBI patients met the inclusion and exclusion criteria. We aggregated study-level estimates of sensitivity and specificity for EEG patterns determined a priori using random effect bivariate and univariate meta-analysis when appropriate. Positive indicators and anatomical area heterogeneity impacted prognosis accuracy. Funnel plots analyzed publication bias. Significant heterogeneity of greater than 80% was among the included studies with P < 0.001. The area under the curve was 0.94, the threshold effect was P < 0.001, and the sensitivity and specificity, with 95% confidence intervals, were 0.91 (0.84-0.99) and 0.86 (0.75-0.97). EEG detects status epilepticus and burst suppression with good sensitivity, specificity, and little probability of false-negative impairment result attribution. Study quality varied by domain, but patient flow and timing were well conducted in all. Conclusion. EEG can predict the outcome of HIBI with good prognostic accuracy, but more standardized cross-study protocols and descriptions of EEG patterns are needed to better evaluate its prognostic use for patients with HIBI.

The spectrum of sepsis-associated encephalopathy: a clinical perspective

Sepsis-associated encephalopathy is a severe neurologic syndrome characterized by a diffuse dysfunction of the brain caused by sepsis. This review provides a concise overview of diagnostic tools and management strategies for SAE at the acute phase and in the long term. Early recognition and diagnosis of SAE are crucial for effective management. Because neurologic evaluation can be confounded by several factors in the intensive care unit setting, a multimodal approach is warranted for diagnosis and management. Diagnostic tools commonly employed include clinical evaluation, metabolic tests, electroencephalography, and neuroimaging in selected cases. The usefulness of blood biomarkers of brain injury for diagnosis remains limited. Clinical evaluation involves assessing the patient's mental status, motor responses, brainstem reflexes, and presence of abnormal movements. Electroencephalography can rule out non-convulsive seizures and help detect several patterns of various severity such as generalized slowing, epileptiform discharges, and triphasic waves. In patients with acute encephalopathy, the diagnostic value of non-contrast computed tomography is limited. In septic patients with persistent encephalopathy, seizures, and/or focal signs, magnetic resonance imaging detects brain injury in more than 50% of cases, mainly cerebrovascular complications, and white matter changes. Timely identification and treatment of the underlying infection are paramount, along with effective control of systemic factors that may contribute to secondary brain injury. Upon admission to the ICU, maintaining appropriate levels of oxygenation, blood pressure, and metabolic balance is crucial. Throughout the ICU stay, it is important to be mindful of the potential neurotoxic effects associated with specific medications like midazolam and cefepime, and to closely monitor patients for non-convulsive seizures. The potential efficacy of targeted neurocritical care during the acute phase in optimizing patient outcomes deserves to be further investigated. Sepsis-associated encephalopathy may lead to permanent neurologic sequelae. Seizures occurring in the acute phase increase the susceptibility to long-term epilepsy. Extended ICU stays and the presence of sepsis-associated encephalopathy are linked to functional disability and neuropsychological sequelae, underscoring the necessity for long-term surveillance in the comprehensive care of septic patients.

Brain-derived extracellular vesicles as serologic markers of brain injury following cardiac arrest: A pilot feasibility study

AIM

Assessment of neurologic injury within the immediate hours following out-of-hospital cardiac arrest (OHCA) resuscitation remains a major clinical challenge. Extracellular vesicles (EVs), small bodies derived from cytosolic contents during injury, may provide the opportunity for "liquid biopsy" within hours following resuscitation, as they contain proteins and RNA linked to cell type of origin. We evaluated whether micro-RNA (miRNA) from serologic EVs were associated with post-arrest neurologic outcome.

METHODS

We obtained serial blood samples in an OHCA cohort. Using novel microfluidic techniques to isolate EVs based on EV surface marker GluR2 (present on excitatory neuronal dendrites enriched in hippocampal tissue), we employed reverse transcription quantitative polymerase chain reaction (RT-qPCR) methods to measure a panel of miRNAs and tested association with dichotomized modified Rankin Score (mRS) at discharge.

RESULTS

EVs were assessed in 27 post-arrest patients between 7/3/2019 and 7/21/2022; 9 patients experienced good outcomes. Several miRNA species including miR-124 were statistically associated with mRS at discharge when measured within 6 hours of resuscitation (AUC = 0.84 for miR-124, p < 0.05). In a Kendall ranked correlation analysis, miRNA associations with outcome were not strongly correlated with standard serologic marker measurements, or amongst themselves, suggesting that miRNA provide distinct information from common protein biomarkers.

CONCLUSIONS

This study explores the associations between miRNAs from neuron-derived EVs (NDEs) and circulating protein biomarkers within 6 hours with neurologic outcome, suggesting a panel of very early biomarker may be useful during clinical care. Future work will be required to test larger cohorts with a broader panel of miRNA species.

Discrepancies in the late auditory potentials of post-anoxic patients: watch out for focal brain lesions, a pilot retrospective study

AIMS

Late auditory evoked potentials, and notably mismatch negativity (MMN) and P3 responses, can be used as part of the multimodal prognostic evaluation in post-anoxic disorders of consciousness (DOC). MMN response preferentially stems from the temporal cortex and the arcuate fasciculus. Situations with discrepant evaluations, for example MMN absent but P3 present, are frequent and difficult to interpret. We hypothesize that discrepant MMN-/P3+ results could reflect a higher prevalence of lesions in MMN generating regions. This study presents correlations between neurophysiological and neuroradiological results.

METHODS

This retrospective study was conducted on 38 post-anoxic DOC patients. Brain lesions were analyzed on 3T MRI both anatomically and through computation of the local arcuate fasciculus fractional anisotropy values on Diffusion Tensor Imaging sequences. Neurophysiological data and outcome were also analyzed.

RESULTS

Our cohort included 8 MMN-/P3+, 7 MMN+/P3+, 21 MMN-/P3- and 2 MMN-/P3+ patients, assessed at a median delay of 20.5 days since cardiac arrest. Our results show that MMN-/P3+ patients tended to have fewer temporal and basal ganglia lesions than MMN-/P3- patients, and more than MMN+/P3+ patients (p-values for trend: p=0.02 for temporal and p=0.02 for basal ganglia lesions). There was a statistical difference across groups for mean fractional anisotropy values in the arcuate fasciculus (p=0.008). The percentage of patients regaining consciousness at three months in MMN-/P3+ patients was higher than in MMN-/P3- patients and lower than in MMN+/P3+ patients.

CONCLUSION

This study suggests that discrepancies in late auditory evoked potentials may be linked to focal post-anoxic brain lesions, visible on brain MRI.