Standardized EEG analysis to reduce the uncertainty of outcome prognostication after cardiac arrest

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.

Normal value of neuron-specific enolase for predicting good neurological outcomes in comatose out-of-hospital cardiac arrest survivors

Research on prognostic factors for good outcomes in out-of-hospital cardiac arrest (OHCA) survivors is lacking. We assessed whether normal levels of normal neuron-specific enolase (NSE) value would be useful for predicting good neurological outcomes in comatose OHCA survivors treated with targeted temperature management (TTM). This registry-based observational study with consecutive adult (≥18 years) OHCA survivors with TTM who underwent NSE measurement 48 hours after cardiac arrest was conducted from October 2015 to November 2022. Normal NSE values defined as the upper limit of the normal range by the manufacturer (NSE <16.3 μg/L) and guideline-suggested (NSE < 60 μg/L) were examined for good neurologic outcomes, defined as Cerebral Performance Categories ≤2, at 6 months post-survival. Among 226 OHCA survivors with TTM, 200 patients who underwent NSE measurement were enrolled. The manufacturer-suggested normal NSE values (<16.3 μg/L) had a specificity of 99.17% for good neurological outcomes with a very low sensitivity of 12.66%. NSE <60 μg/L predicted good outcomes with a sensitivity of 87.34% and specificity of 72.73%. However, excluding 14 poor-outcome patients who died from multi-organ dysfunction excluding hypoxic brain injury, the sensitivity and specificity of normal NSE values were 12.66% and 99.07% of NSE < 16.3 μg/L, and 87.34% and 82.24% of NSE < 60 μg/L. The manufacturer-suggested normal NSE had high specificity with low sensitivity, but the guideline-suggested normal NSE value had a comparatively low specificity for good outcome prediction in OHCA survivors. Our data demonstrate normal NSE levels can be useful as a tool for multimodal appropriation of good outcome prediction.

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.

Good outcome prediction after out-of-hospital cardiac arrest: A prospective multicenter observational study in Korea (the KORHN-PRO registry)

AIM

To assess the ability of clinical examination, biomarkers, electrophysiology and brain imaging, individually or in combination to predict good neurological outcomes at 6 months after CA.

METHODS

This was a retrospective analysis of the Korean Hypothermia Network Prospective Registry 1.0, which included adult out-of-hospital cardiac arrest (OHCA) patients (≥18 years). Good outcome predictors were defined as both pupillary light reflex (PLR) and corneal reflex (CR) at admission, Glasgow Coma Scale Motor score (GCS-M) >3 at admission, neuron-specific enolase (NSE) <17 µg/L at 24-72 h, a median nerve somatosensory evoked potential (SSEP) N20/P25 amplitude >4 µV, continuous background without discharges on electroencephalogram (EEG), and absence of anoxic injury on brain CT and diffusion-weighted imaging (DWI).

RESULTS

A total of 1327 subjects were included in the final analysis, and their median age was 59 years; among them, 412 subjects had a good neurological outcome at 6 months. GCS-M >3 at admission had the highest specificity of 96.7% (95% CI 95.3-97.8), and normal brain DWI had the highest sensitivity of 96.3% (95% CI 92.9-98.4). When the two predictors were combined, the sensitivities tended to decrease (ranging from 2.7-81.1%), and the specificities tended to increase, ranging from81.3-100%. Through the explorative variation of the 2021 European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) prognostication strategy algorithms, good outcomes were predicted, with a specificity of 83.2% and a sensitivity of 83.5% in patients by the algorithm.

CONCLUSIONS

Clinical examination, biomarker, electrophysiology, and brain imaging predicted good outcomes at 6 months after CA. When the two predictors were combined, the specificity further improved. With the 2021 ERC/ESICM guidelines, the number of indeterminate patients and the uncertainty of prognostication can be reduced by using a good outcome prediction algorithm.

Clinicians' approach to predicting post-cardiac arrest outcomes for patients enrolled in a United States clinical trial

PURPOSE

Perceived poor prognosis can lead to withdrawal of life-sustaining therapies (WLST) in patients who might otherwise recover. We characterized clinicians' approach to post-arrest prognostication in a multicenter clinical trial.

METHODS

Semi-structured interviews were conducted with clinicians who treated a comatose post-cardiac arrest patient enrolled in the Influence of Cooling Duration on Efficacy in Cardiac Arrest Patients (ICECAP) trial (NCT04217551). Two authors independently analyzed each interview using inductive and deductive coding. The clinician reported how they arrived at a prognosis for the specific patient. We summarized the frequency with which clinicians reported using objective diagnostics to formulate their prognosis, and compared the reported approaches to established guidelines. Each respondent provided demographic information and described local neuroprognostication practices.

RESULTS

We interviewed 30 clinicians at 19 US hospitals. Most claimed adherence to local hospital neuroprognostication protocols (n = 19). Prognostication led to WLST for perceived poor neurological prognosis in 15/30 patients, of whom most showed inconsistencies with guidelines or trial recommendations, respectively. In 10/15 WLST cases, clinicians reported relying on multimodal testing. A prevalent theme was the use of "clinical gestalt," defined as prognosticating based on a patient's overall appearance or a subjective impression in the absence of objective data. Many clinicians (21/30) reported using clinical gestalt for initial prognostication, with 9/21 expressing high confidence initially.

CONCLUSION

Clinicians in our study state they follow neuroprognostication guidelines in general but often do not do so in actual practice. They reported clinical gestalt frequently informed early, highly confident prognostic judgments, and few objective tests changed initial impressions. Subjective prognostication may undermine well-designed trials.

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.

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.

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.

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.

Cognitive Biases and Shared Decision Making in Acute Brain Injury

Many patients hospitalized after severe acute brain injury are comatose and require life-sustaining therapies. Some of these patients make favorable recoveries with continued intensive care, while others do not. In addition to providing medical care, clinicians must guide surrogate decision makers through high-stakes, emotionally charged decisions about whether to continue life-sustaining therapies. These consultations require clinicians first to assess a patient's likelihood of recovery given continued life-sustaining therapies (i.e., prognosticate), then to communicate that prediction to surrogates, and, finally, to elicit and interpret the patient's preferences. At each step, both clinicians and surrogates are vulnerable to flawed decision making. Clinicians can be imprecise, biased, and overconfident when prognosticating after brain injury. Surrogates can misperceive the choice and misunderstand or misrepresent a patient's wishes, which may never have been communicated clearly. These biases can undermine the ability to reach choices congruent with patients' preferences through shared decision making (SDM). Decision science has extensively studied these biases. In this article, we apply that research to improving SDM for patients who are comatose after acute brain injury. After introducing SDM and the medical context, we describe principal decision science results as they relate to neurologic prognostication and end-of-life decisions, by both clinicians and surrogates. Based on research regarding general processes that can produce imprecise, biased, and overconfident prognoses, we propose interventions that could improve SDM, supporting clinicians and surrogates in making these challenging decisions.

Value of EEG in outcome prediction of hypoxic-ischemic brain injury in the ICU: A narrative review

Prognostication of comatose patients after cardiac arrest aims to identify patients with a large probability of favourable or unfavouble outcome, usually within the first week after the event. Electroencephalography (EEG) is a technique that is increasingly used for this purpose and has many advantages, such as its non-invasive nature and the possibility to monitor the evolution of brain function over time. At the same time, use of EEG in a critical care environment faces a number of challenges. This narrative review describes the current role and future applications of EEG for outcome prediction of comatose patients with postanoxic encephalopathy.

Quantification of Cerebral Vascular Autoregulation Immediately Following Resuscitation from Cardiac Arrest

Cerebral vascular autoregulation is impaired following resuscitation from cardiac arrest (CA), and its quantification may allow assessing CA-induced brain injury. However, hyperemia occurring immediately post-resuscitation limits the application of most metrics that quantify autoregulation. Therefore, to characterize autoregulation during this critical period, we developed three novel metrics based on how the cerebrovascular resistance (CVR) covaries with changes in cerebral perfusion pressure (CPP): (i) θCVR, which quantifies the CVR vs CPP gradient, (ii) a CVR-based transfer function analysis, and (iii) CVRx, the correlation coefficient between CPP and CVR. We tested these metrics in a model of asphyxia induced CA and resuscitation using seven adult male Wistar rats. Mean arterial pressure (MAP) and cortical blood flow recorded for 30 min post-resuscitation via arterial cannulation and laser speckle contrast imaging, were used as surrogates of CPP and cerebral blood flow (CBF), while CVR was computed as the CPP/CBF ratio. Using our metrics, we found that the status of cerebral vascular autoregulation altered substantially during hyperemia, with changes spread throughout the 0-0.05 Hz frequency band. Our metrics push the boundary of how soon autoregulation can be assessed, and if validated against outcome markers, may help develop a reliable metric of brain injury post-resuscitation.

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

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

Factors Associated with Early Withdrawal of Life-Sustaining Treatments After Out-of-Hospital Cardiac Arrest: A Subanalysis of a Randomized Trial of Prehospital Therapeutic Hypothermia

BACKGROUND

The objective of this study is to describe incidence and factors associated with early withdrawal of life-sustaining therapies based on presumed poor neurologic prognosis (WLST-N) and practices around multimodal prognostication after out-of-hospital cardiac arrest (OHCA).

METHODS

We performed a subanalysis of a randomized controlled trial assessing prehospital therapeutic hypothermia in adult patients admitted to nine hospitals in King County with nontraumatic OHCA between 2007 and 2012. Patients who underwent tracheal intubation and were unconscious following return of spontaneous circulation were included. Our outcomes were (1) incidence of early WLST-N (WLST-N within < 72 h from return of spontaneous circulation), (2) factors associated with early WLST-N compared with patients who remained comatose at 72 h without WLST-N, (3) institutional variation in early WLST-N, (4) use of multimodal prognostication, and (5) use of sedative medications in patients with early WLST-N. Analysis included descriptive statistics and multivariable logistic regression.

RESULTS

We included 1,040 patients (mean age was 65 years, 37% were female, 41% were White, and 44% presented with arrest due to ventricular fibrillation) admitted to nine hospitals. Early WLST-N accounted for 24% (n = 154) of patient deaths and occurred in half (51%) of patients with WLST-N. Factors associated with early WLST-N in multivariate regressions were older age (odds ratio [OR] 1.02, 95% confidence interval [CI]: 1.01-1.03), preexisting do-not-attempt-resuscitation orders (OR 4.67, 95% CI: 1.55-14.01), bilateral absent pupillary reflexes (OR 2.4, 95% CI: 1.42-4.10), and lack of neurological consultation (OR 2.60, 95% CI: 1.52-4.46). The proportion of patients with early WLST-N among all OHCA admissions ranged from 19-60% between institutions. A head computed tomography scan was obtained in 54% (n = 84) of patients with early WLST-N; 22% (n = 34) and 5% (n = 8) underwent ≥ 1 and ≥ 2 additional prognostic tests, respectively. Prognostic tests were more frequently performed when neurological consultation occurred. Most patients received sedating medications (90%) within 24 h before early WLST-N; the median time from last sedation to early WLST-N was 4.2 h (interquartile range 0.4-15).

CONCLUSIONS

Nearly one quarter of deaths after OHCA were due to early WLST-N. The presence of concerning neurological examination findings appeared to impact early WLST-N decisions, even though these are not fully reliable in this time frame. Lack of neurological consultation was associated with early WLST-N and resulted in underuse of guideline-concordant multimodal prognostication. Sedating medications were often coadministered prior to early WLST-N and may have further confounded the neurological examination. Standardizing prognostication, restricting early WLST-N, and a multidisciplinary approach including neurological consultation might improve outcomes after OHCA.

Cognition, emotional state, and quality of life of survivors after cardiac arrest with rhythmic and periodic EEG patterns

AIM

Rhythmic and periodic patterns (RPPs) on the electroencephalogram (EEG) in comatose patients after cardiac arrest have been associated with high case fatality rates. A good neurological outcome according to the Cerebral Performance Categories (CPC) has been reported in up to 10% of cases. Data on cognitive, emotional, and quality of life outcomes are lacking. We aimed to provide insight into these outcomes at one-year follow-up.

METHODS

We assessed outcome of surviving comatose patients after cardiac arrest with RPPs included in the 'treatment of electroencephalographic status epilepticus after cardiopulmonary resuscitation' (TELSTAR) trial at one-year follow-up, including the CPC for functional neurological outcome, a cognitive assessment, the hospital anxiety and depression scale (HADS) for emotional outcomes, and the 36-item short-form health survey (SF-36) for quality of life. Cognitive impairment was defined as a score of more than 1.5 SD below the mean on ≥ 2 (sub)tests within a cognitive domain.

RESULTS

Fourteen patients were included (median age 58 years, 21% female), of whom 13 had a cognitive impairment. Eleven of 14 were impaired in memory, 9/14 in executive functioning, and 7/14 in attention. The median scores on the HADS and SF-36 were all worse than expected. Based on the CPC alone, 8/14 had a good outcome (CPC 1-2).

CONCLUSION

Nearly all cardiac arrest survivors with RPPs during the comatose state have cognitive impairments at one-year follow-up. The incidence of anxiety and depression symptoms seem relatively high and quality of life relatively poor, despite 'good' outcomes according to the CPC.

Comparing Patients With Isolated Seizures and Status Epilepticus in Intensive Care Units: An Observational Cohort Study

BACKGROUND AND OBJECTIVES

To assess the frequency of status epilepticus (SE) among seizing critically ill adult patients and to determine clinical differences between patients with isolated seizures and patients with SE in the intensive care unit (ICU).

METHODS

From 2015 to 2020, all consecutive adult ICU patients at a Swiss tertiary care center with isolated seizures or SE as reported by intensivists and/or consulting neurologists were identified by screening of all digital medical, ICU, and EEG records. Patients aged <18 years and patients with myoclonus due to hypoxic-ischemic encephalopathy but without seizures on EEG were excluded. The frequency of isolated seizures, SE, and clinical characteristics at seizure onset associated with SE were the primary outcomes. Uni- and multivariable logistic regression was performed to identify associations with the emergence of SE.

RESULTS

Among 404 patients with seizures, 51% had SE. Compared with patients with isolated seizures, patients with SE had a lower median Charlson Comorbidity Index (CCI) (3 vs 5, p < 0.001), fewer fatal etiologies (43.6% vs 80.5%, p < 0.001), higher median Glasgow coma scores (7 vs 5, p < 0.001), fever more frequently (27.5% vs 7.5%, p < 0.001), shorter median ICU and hospital stay (ICU: 4 vs 5 days, p = 0.039; hospital stay: 13 vs 15 days, p = 0.045), and recovered to premorbid function more often (36.8% vs 17%, p < 0.001). Multivariable analyses revealed decreased odds ratios (ORs) for SE with increasing CCI (OR 0.91, 95% CI 0.83-0.99), fatal etiology (OR 0.15, 95% CI 0.08-0.29), and epilepsy (OR 0.32, 95% CI 0.16-0.63). Systemic inflammation was an additional association with SE after excluding patients with seizures as the reason for ICU admission (ORfor CRP 1.01, 95% CI 1.00-1.01; ORfor fever 7.35, 95% CI 2.84-19.0). Although fatal etiologies and increasing CCI remained associated with low odds for SE after excluding anesthetized patients and hypoxic-ischemic encephalopathy, inflammation remained associated in all subgroups except patients with epilepsy.

DISCUSSION

Among all ICU patients with seizures, SE emerged frequently and seen in every second patient. Besides the unexpected low odds for SE with higher CCI, fatal etiology, and epilepsy, the association of inflammation with SE in the critically ill without epilepsy represents a potential treatment target and deserves further attention.

Neuroprognostication in the Post Cardiac Arrest Patient: A Canadian Cardiovascular Society Position Statement

Cardiac arrest (CA) is associated with a low rate of survival with favourable neurologic recovery. The most common mechanism of death after successful resuscitation from CA is withdrawal of life-sustaining measures on the basis of perceived poor neurologic prognosis due to underlying hypoxic-ischemic brain injury. Neuroprognostication is an important component of the care pathway for CA patients admitted to hospital but is complex, challenging, and often guided by limited evidence. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to evaluate the evidence underlying factors or diagnostic modalities available to determine prognosis, recommendations were generated in the following domains: (1) circumstances immediately after CA; (2) focused neurologic exam; (3) myoclonus and seizures; (4) serum biomarkers; (5) neuroimaging; (6) neurophysiologic testing; and (7) multimodal neuroprognostication. This position statement aims to serve as a practical guide to enhance in-hospital care of CA patients and emphasizes the adoption of a systematic, multimodal approach to neuroprognostication. It also highlights evidence gaps.

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

AIM

The current EEG role for prognostication after cardiac arrest (CA) essentially aims at reliably identifying patients with poor prognosis ("highly malignant" patterns, defined by Westhall et al. in 2014). Conversely, "benign EEGs", defined by the absence of elements of "highly malignant" and "malignant" categories, has limited sensitivity in detecting good prognosis. We postulate that a less stringent "benign EEG" definition would improve sensitivity to detect patients with favorable outcomes.

METHODS

Retrospectively assessing our registry of unconscious adults after CA (1.2018-8.2021), we scored EEGs within 72 h after CA using a modified "benign EEG" classification (allowing discontinuity, low-voltage, or reversed anterio-posterior amplitude development), versus Westhall's "benign EEG" classification (not allowing the former items). We compared predictive performances towards good outcome (Cerebral Performance Category 1-2 at 3 months), using 2x2 tables (and binomial 95% confidence intervals) and proportions comparisons.

RESULTS

Among 381 patients (mean age 61.9 ± 15.4 years, 104 (27.2%) females, 240 (62.9%) having cardiac origin), the modified "benign EEG" definition identified a higher number of patients with potential good outcome (252, 66%, vs 163, 43%). Sensitivity of the modified EEG definition was 0.97 (95% CI: 0.92-0.97) vs 0.71 (95% CI: 0.62-0.78) (p < 0.001). Positive predictive values (PPV) were 0.53 (95% CI: 0.46-0.59) versus 0.59 (95% CI: 0.51-0.67; p = 0.17). Similar statistics were observed at definite recording times, and for survivors.

DISCUSSION

The modified "benign EEG" classification demonstrated a markedly higher sensitivity towards favorable outcome, with minor impact on PPV. Adaptation of "benign EEG" criteria may improve efficient identification of patients who may reach a good outcome.

Free water corrected diffusion tensor imaging discriminates between good and poor outcomes of comatose patients after cardiac arrest

OBJECTIVES

Approximately 50% of comatose patients after cardiac arrest never regain consciousness. Cerebral ischaemia may lead to cytotoxic and/or vasogenic oedema, which can be detected by diffusion tensor imaging (DTI). Here, we evaluate the potential value of free water corrected mean diffusivity (MD) and fractional anisotropy (FA) based on DTI, for the prediction of neurological recovery of comatose patients after cardiac arrest.

METHODS

A total of 50 patients after cardiac arrest were included in this prospective cohort study in two Dutch hospitals. DTI was obtained 2-4 days after cardiac arrest. Outcome was assessed at 6 months, dichotomised as poor (cerebral performance category 3-5; n = 20) or good (n = 30) neurological outcome. We calculated the whole brain mean MD and FA and compared between patients with good and poor outcomes. In addition, we compared a preliminary prediction model based on clinical parameters with or without the addition of MD and FA.

RESULTS

We found significant differences between patients with good and poor outcome of mean MD (good: 726 [702-740] × 10^-6 mm²/s vs. poor: 663 [575-736] × 10^-6 mm²/s; p = 0.01) and mean FA (0.30 ± 0.03 vs. 0.28 ± 0.03; p = 0.03). An exploratory prediction model combining clinical parameters, MD and FA increased the sensitivity for reliable prediction of poor outcome from 60 to 85%, compared to the model containing clinical parameters only, but confidence intervals are overlapping.

CONCLUSIONS

Free water-corrected MD and FA discriminate between patients with good and poor outcomes after cardiac arrest and hold the potential to add to multimodal outcome prediction.

KEY POINTS

• Whole brain mean MD and FA differ between patients with good and poor outcome after cardiac arrest. • Free water-corrected MD can better discriminate between patients with good and poor outcome than uncorrected MD. • A combination of free water-corrected MD (sensitive to grey matter abnormalities) and FA (sensitive to white matter abnormalities) holds potential to add to the prediction of outcome.

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

About 80% of patients resuscitated from CA are comatose at ICU admission and nearly 50% of survivors are still unawake at 72 h. Predicting neurological outcome of these patients is important to provide correct information to patient's relatives, avoid disproportionate care in patients with irreversible hypoxic-ischemic brain injury (HIBI) and inappropriate withdrawal of care in patients with a possible favorable neurological recovery. ERC/ESICM 2021 algorithm allows a classification as "poor outcome likely" in 32%, the outcome remaining "indeterminate" in 68%. The crucial question is to know how we could improve the assessment of both unfavorable but also favorable outcome prediction. Neurophysiological tests, i.e., electroencephalography (EEG) and evoked-potentials (EPs) are a non-invasive bedside investigations. The EEG is the record of brain electrical fields, characterized by a high temporal resolution but a low spatial resolution. EEG is largely available, and represented the most widely tool use in recent survey examining current neuro-prognostication practices. The severity of HIBI is correlated with the predominant frequency and background continuity of EEG leading to "highly malignant" patterns as suppression or burst suppression in the most severe HIBI. EPs differ from EEG signals as they are stimulus induced and represent the summated activities of large populations of neurons firing in synchrony, requiring the average of numerous stimulations. Different EPs (i.e., somato sensory EPs (SSEPs), brainstem auditory EPs (BAEPs), middle latency auditory EPs (MLAEPs) and long latency event-related potentials (ERPs) with mismatch negativity (MMN) and P300 responses) can be assessed in ICU, with different brain generators and prognostic values. In the present review, we summarize EEG and EPs signal generators, recording modalities, interpretation and prognostic values of these different neurophysiological tools. Finally, we assess the perspective for futures neurophysiological investigations, aiming to reduce prognostic uncertainty in comatose and disorders of consciousness (DoC) patients after CA.

Do changes in SSEP amplitude over time predict the outcome of comatose survivors of cardiac arrest?

AIM

To assess if the amplitude of the N20 wave (N20Amp) of somatosensory evoked potentials (SSEPs) changes between 12-24 h and 72 h from the return of spontaneous circulation (ROSC) after cardiac arrest and if an N20Amp decrease predicts poor neurological outcome (CPC 3-5) at six months.

SETTING

Retrospective analysis of the ProNeCA multicentre prognostication study dataset. (NCT03849911).

METHODS

In adult comatose cardiac arrest survivors whose SSEPs were recorded at both 12-24 h and 72 h after ROSC, we measured the median N20Amp at each timepoint and the individual change in N20Amp across the two timepoints. We identified their cutoffs for predicting poor outcome with 0% false positive rate (FPR) and compared their sensitivities.

RESULTS

We included 236 patients. The median [IQR] N20Amp increased from 1.90 [0.78-4.22] µV to 2.86 [1.52-5.10] µV between 12-24 h and 72 h (p = 0.0019). The N20Amp cutoff for 0% FPR increased from 0.6 µV at 12-24 h to 1.23 µV at 72 h, and its sensitivity increased from 56[48-64]% to 71[63-77]%. Between 12-24 h and 72 h, an N20Amp decrease > 53% predicted poor outcome with 0[0-5]% FPR and 26[19-35]% sensitivity. Its combination with an N20Amp < 1.23 µV at 72 h increased sensitivity by 1% to 72[64-79]%.

CONCLUSION

In comatose cardiac arrest survivors, the median N20Amp and its cutoff for predicting poor neurological outcome increase between 12-24 and 72 h after ROSC. An N20Amp decrease greater than 53% between these two timepoints predicts poor outcome with 0% FPR, confirming the unfavourable prognostic signal of a low N20Amp at 72 h.

Prognostic accuracy of head computed tomography for prediction of functional outcome after out-of-hospital cardiac arrest: Rationale and design of the prospective TTM2-CT-substudy

Background

Head computed tomography (CT) is a guideline recommended method to predict functional outcome after cardiac arrest (CA), but standardized criteria for evaluation are lacking. To date, no prospective trial has systematically validated methods for diagnosing hypoxic-ischaemic encephalopathy (HIE) on CT after CA. We present a protocol for validation of pre-specified radiological criteria for assessment of HIE on CT for neuroprognostication after CA.

Methods/design

This is a prospective observational international multicentre substudy of the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Patients still unconscious 48 hours post-arrest at 13 participating hospitals were routinely examined with CT. Original images will be evaluated by examiners blinded to clinical data using a standardized protocol. Qualitative assessment will include evaluation of absence/presence of "severe HIE". Radiodensities will be quantified in pre-specified regions of interest for calculation of grey-white matter ratios (GWR) at the basal ganglia level. Functional outcome will be dichotomized into good (modified Rankin Scale 0-3) and poor (modified Rankin Scale 4-6) at six months post-arrest. Prognostic accuracies for good and poor outcome will be presented as sensitivities and specificities with 95% confidence intervals (using pre-specified cut-offs for quantitative analysis), descriptive statistics (Area Under the Receiver Operating Characteristics Curve), inter- and intra-rater reliabilities according to STARD guidelines.

Conclusions

The results from this prospective trial will validate a standardized approach to radiological evaluations of HIE on CT for prediction of functional outcome in comatose CA patients.The TTM2 trial and the TTM2 CT substudy are registered at ClinicalTrials.gov NCT02908308 and NCT03913065.

MRI markers of brain network integrity relate to neurological outcome in postanoxic coma

AIM

Current multimodal approaches leave approximately half of the comatose patients after cardiac arrest with an indeterminate prognosis. Here we investigated whether early MRI markers of brain network integrity can distinguish between comatose patients with a good versus poor neurological outcome six months later.

METHODS

We performed a prospective cohort study in 48 patients after cardiac arrest submitted in a comatose state to the Intensive Care Unit of two Dutch hospitals. MRI was performed at three days after cardiac arrest, including resting state functional MRI and diffusion-tensor imaging (DTI). Resting state fMRI was used to quantify functional connectivity within ten resting-state networks, and DTI to assess mean diffusivity (MD) in these same networks. We contrasted two groups of patients, those with good (n = 29, cerebral performance category 1-2) versus poor (n = 19, cerebral performance category 3-5) outcome at six months. Mutual associations between functional connectivity, MD, and clinical outcome were studied.

RESULTS

Patients with good outcome show higher within-network functional connectivity (fMRI) and higher MD (DTI) than patients with poor outcome across 8/10 networks, most prominent in the default mode network, salience network, and visual network. While the anatomical distribution of outcome-related changes was similar for functional connectivity and MD, the pattern of inter-individual differences was very different: functional connectivity showed larger inter-individual variability in good versus poor outcome, while the opposite was observed for MD. Exploratory analyses suggested that it is possible to define network-specific cut-off values that could help in outcome prediction: (1) high functional connectivity and high MD, associated with good outcome; (2) low functional connectivity and low MD, associated with poor outcome; (3) low functional connectivity and high MD, associated with uncertain outcome.

DISCUSSION

Resting-state functional connectivity and mean diffusivity-three days after cardiac arrest are strongly associated with neurological recovery-six months later in a complementary fashion. The combination of fMRI and MD holds potential to improve prediction of outcome.

Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method

Background

Better outcome prediction could assist in reliable classification of the illnesses in neurological intensive care unit (ICU) severity to support clinical decision-making. We developed a multifactorial model including quantitative electroencephalography (QEEG) parameters for outcome prediction of patients in neurological ICU.

Methods

We retrospectively analyzed neurological ICU patients from November 2018 to November 2021. We used 3-month mortality as the outcome. Prediction models were created using a linear discriminant analysis (LDA) based on QEEG parameters, APACHEII score, and clinically relevant features. Additionally, we compared our best models with APACHEII score and Glasgow Coma Scale (GCS). The DeLong test was carried out to compare the ROC curves in different models.

Results

A total of 110 patients were included and divided into a training set (n=80) and a validation set (n = 30). The best performing model had an AUC of 0.85 in the training set and an AUC of 0.82 in the validation set, which were better than that of GCS (training set 0.64, validation set 0.61). Models in which we selected only the 4 best QEEG parameters had an AUC of 0.77 in the training set and an AUC of 0.71 in the validation set, which were similar to that of APACHEII (training set 0.75, validation set 0.73). The models also identified the relative importance of each feature.

Conclusion

Multifactorial machine learning models using QEEG parameters, clinical data, and APACHEII score have a better potential to predict 3-month mortality in non-traumatic patients in neurological ICU.

Top Ten Tips Palliative Care Clinicians Should Know About Disorders of Consciousness: A Focus on Traumatic and Anoxic Brain Injury

Palliative care (PC) teams commonly encounter patients with disorders of consciousness (DOC) following anoxic or traumatic brain injury (TBI). Primary teams may consult PC to help surrogates in making treatment choices for these patients. PC clinicians must understand the complexity of predicting neurologic outcomes, address clinical nihilism, and appropriately guide surrogates in making decisions that are concordant with patients' goals. The purpose of this article was to provide PC providers with a better understanding of caring for patients with DOC, specifically following anoxic or TBI. Many of the tips acknowledge the uncertainty of DOC and provide strategies to help tackle this dilemma.

External validation of the 2020 ERC/ESICM prognostication strategy algorithm after cardiac arrest

Purpose

To assess the performance of the post-cardiac arrest (CA) prognostication strategy algorithm recommended by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) in 2020.

Methods

This was a retrospective analysis of the Korean Hypothermia Network Prospective Registry 1.0. Unconscious patients without confounders at day 4 (72–96 h) after return of spontaneous circulation (ROSC) were included. The association between the prognostic factors included in the prognostication strategy algorithm, except status myoclonus and the neurological outcome, was investigated, and finally, the prognostic performance of the prognostication strategy algorithm was evaluated. Poor outcome was defined as cerebral performance categories 3–5 at 6 months after ROSC.

Results

A total of 660 patients were included in the final analysis. Of those, 108 (16.4%) patients had a good neurological outcome at 6 months after CA. The 2020 ERC/ESICM prognostication strategy algorithm identified patients with poor neurological outcome with 60.2% sensitivity (95% CI 55.9–64.4) and 100% specificity (95% CI 93.9–100) among patients who were unconscious or had a GCS_M score ≤ 3 and with 58.2% sensitivity (95% CI 53.9–62.3) and 100% specificity (95% CI 96.6–100) among unconscious patients. When two prognostic factors were combined, any combination of prognostic factors had a false positive rate (FPR) of 0 (95% CI 0–5.6 for combination of no PR/CR and poor CT, 0–30.8 for combination of No SSEP N20 and NSE 60).

Conclusion

The 2020 ERC/ESICM prognostication strategy algorithm predicted poor outcome without an FPR and with sensitivities of 58.2–60.2%. Any combinations of two predictors recommended by ERC/ESICM showed 0% of FPR.

Gray-White Matter Ratio at the Level of the Basal Ganglia as a Predictor of Neurologic Outcomes in Cardiac Arrest Survivors: A Literature Review

Loss of gray-white matter discrimination is the primary early imaging finding within of cranial computed tomography in cardiac arrest survivors, and this has been also regarded as a novel predictor for evaluating neurologic outcome. As displayed clearly on computed tomography and based on sensitivity to hypoxia, the gray-white matter ratio at basal ganglia (GWR-BG) region was frequently detected to assess the neurologic outcome by several studies. The specificity of GWR-BG is 72.4 to 100%, while the sensitivity is significantly different. Herein we review the mechanisms mediating cerebral edema following cardiac arrest, demonstrate the determination procedures with respect to GWR-BG, summarize the related researches regarding GWR-BG in predicting neurologic outcomes within cardiac arrest survivors, and discuss factors associated with predicting the accuracy of this methodology. Finally, we describe the effective measurements to increase the sensitivity of GWR-BG in predicting neurologic outcome.

Neurologic Prognostication After Cardiac Arrest Using Brain Biomarkers: A Systematic Review and Meta-analysis

IMPORTANCE

Brain injury biomarkers released into circulation from the injured neurovascular unit are important prognostic tools in patients with cardiac arrest who develop hypoxic ischemic brain injury (HIBI) after return of spontaneous circulation (ROSC).

OBJECTIVE

To assess the neuroprognostic utility of bloodborne brain injury biomarkers in patients with cardiac arrest with HIBI.

DATA SOURCES

Studies in electronic databases from inception to September 15, 2021. These databases included MEDLINE, Embase, Evidence-Based Medicine Reviews, CINAHL, Cochrane Database of Systematic Reviews, and the World Health Organization Global Health Library.

STUDY SELECTION

Articles included in this systmatic review and meta-analysis were independently assessed by 2 reviewers. We included studies that investigated neuron-specific enolase, S100 calcium-binding protein β, glial fibrillary acidic protein, neurofilament light, tau, or ubiquitin carboxyl hydrolase L1 in patients with cardiac arrest aged 18 years and older for neurologic prognostication. We excluded studies that did not (1) dichotomize neurologic outcome as favorable vs unfavorable, (2) specify the timing of blood sampling or outcome determination, or (3) report diagnostic test accuracy or biomarker concentration.

DATA EXTRACTION AND SYNTHESIS

Data on the study design, inclusion and exclusion criteria, brain biomarkers levels, diagnostic test accuracy, and neurologic outcome were recorded. This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline.

MAIN OUTCOMES AND MEASURES

Summary receiver operating characteristic curve analysis was used to calculate the area under the curve, sensitivity, specificity, and optimal thresholds for each biomarker. Risk of bias and concerns of applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.

RESULTS

We identified 2953 studies, of which 86 studies with 10 567 patients (7777 men [73.6] and 2790 women [26.4]; pooled mean [SD] age, 62.8 [10.2] years) were included. Biomarker analysis at 48 hours after ROSC demonstrated that neurofilament light had the highest predictive value for unfavorable neurologic outcome, with an area under the curve of 0.92 (95% CI, 0.84-0.97). Subgroup analyses of patients treated with targeted temperature management and those who specifically had an out-of-hospital cardiac arrest showed similar results (targeted temperature management, 0.92 [95% CI, 0.86-0.95] and out-of-hospital cardiac arrest, 0.93 [95% CI, 0.86-0.97]).

CONCLUSIONS AND RELEVANCE

Neurofilament light, which reflects white matter damage and axonal injury, yielded the highest accuracy in predicting neurologic outcome in patients with HIBI at 48 hours after ROSC.

TRIAL REGISTRATION

PROSPERO Identifier: CRD42020157366.

SSEP N20 and P25 amplitudes predict poor and good neurologic outcomes after cardiac arrest

Background

To assess in comatose patients after cardiac arrest (CA) if amplitudes of two somatosensory evoked potentials (SSEP) responses, namely, N20-baseline (N20-b) and N20–P25, are predictive of neurological outcome.

Methods

Monocentric prospective study in a tertiary cardiac center between Nov 2019 and July-2021. All patients comatose at 72 h after CA with at least one SSEP recorded were included. The N20-b and N20–P25 amplitudes were automatically measured in microvolts (µV), along with other recommended prognostic markers (status myoclonus, neuron-specific enolase levels at 2 and 3 days, and EEG pattern). We assessed the predictive value of SSEP for neurologic outcome using the best Cerebral Performance Categories (CPC1 or 2 as good outcome) at 3 months (main endpoint) and 6 months (secondary endpoint). Specificity and sensitivity of different thresholds of SSEP amplitudes, alone or in combination with other prognostic markers, were calculated.

Results

Among 82 patients, a poor outcome (CPC 3–5) was observed in 78% of patients at 3 months. The median time to SSEP recording was 3(2–4) days after CA, with a pattern “bilaterally absent” in 19 patients, “unilaterally present” in 4, and “bilaterally present” in 59 patients. The median N20-b amplitudes were different between patients with poor and good outcomes, i.e., 0.93 [0–2.05]µV vs. 1.56 [1.24–2.75]µV, respectively (p < 0.0001), as the median N20–P25 amplitudes (0.57 [0–1.43]µV in poor outcome vs. 2.64 [1.39–3.80]µV in good outcome patients p < 0.0001). An N20-b > 2 µV predicted good outcome with a specificity of 73% and a moderate sensitivity of 39%, although an N20–P25 > 3.2 µV was 93% specific and only 30% sensitive. A low voltage N20-b < 0.88 µV and N20–P25 < 1 µV predicted poor outcome with a high specificity (sp = 94% and 93%, respectively) and a moderate sensitivity (se = 50% and 66%). Association of “bilaterally absent or low voltage SSEP” patterns increased the sensitivity significantly as compared to “bilaterally absent” SSEP alone (se = 58 vs. 30%, p = 0.002) for prediction of poor outcome.

Conclusion

In comatose patient after CA, both N20-b and N20–P25 amplitudes could predict both good and poor outcomes with high specificity but low to moderate sensitivity. Our results suggest that caution is needed regarding SSEP amplitudes in clinical routine, and that these indicators should be used in a multimodal approach for prognostication after cardiac arrest.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-022-00999-6.

Prediction of good neurological outcome in comatose survivors of cardiac arrest: a systematic review

PURPOSE

To assess the ability of clinical examination, blood biomarkers, electrophysiology or neuroimaging assessed within 7 days from return of spontaneous circulation (ROSC) to predict good neurological outcome, defined as no, mild, or moderate disability (CPC 1-2 or mRS 0-3) at discharge from intensive care unit or later, in comatose adult survivors from cardiac arrest (CA).

METHODS

PubMed, EMBASE, Web of Science and the Cochrane Database of Systematic Reviews were searched. Sensitivity and specificity for good outcome were calculated for each predictor. The risk of bias was assessed using the QUIPS tool.

RESULTS

A total of 37 studies were included. Due to heterogeneities in recording times, predictor thresholds, and definition of some predictors, meta-analysis was not performed. A withdrawal or localisation motor response to pain immediately or at 72-96 h after ROSC, normal blood values of neuron-specific enolase (NSE) at 24 h-72 h after ROSC, a short-latency somatosensory evoked potentials (SSEPs) N20 wave amplitude > 4 µV or a continuous background without discharges on electroencephalogram (EEG) within 72 h from ROSC, and absent diffusion restriction in the cortex or deep grey matter on MRI on days 2-7 after ROSC predicted good neurological outcome with more than 80% specificity and a sensitivity above 40% in most studies. Most studies had moderate or high risk of bias.

CONCLUSIONS

In comatose cardiac arrest survivors, clinical, biomarker, electrophysiology, and imaging studies identified patients destined to a good neurological outcome with high specificity within the first week after cardiac arrest (CA).

Early Clinical and Electrophysiological Brain Dysfunction Is Associated With ICU Outcomes in COVID-19 Critically Ill Patients With Acute Respiratory Distress Syndrome: A Prospective Bicentric Observational Study

OBJECTIVES:

Describe the prevalence of acute cerebral dysfunction and assess the prognostic value of an early clinical and electroencephalography (EEG) assessment in ICU COVID-19 patients.

DESIGN:

Prospective observational study.

SETTING:

Two tertiary critical care units in Paris, France, between April and December 2020.

PATIENTS:

Adult critically ill patients with COVID-19 acute respiratory distress syndrome.

INTERVENTIONS:

Neurologic examination and EEG at two time points during the ICU stay, first under sedation and second 4–7 days after sedation discontinuation.

MEASUREMENTS AND MAIN RESULTS:

Association of EEG abnormalities (background reactivity, continuity, dominant frequency, and presence of paroxystic discharges) with day-28 mortality and neurologic outcomes (coma and delirium recovery). Fifty-two patients were included, mostly male (81%), median (interquartile range) age 68 years (56–74 yr). Delayed awakening was present in 68% of patients (median awakening time of 5 d [2–16 d]) and delirium in 74% of patients who awoke from coma (62% of mixed delirium, median duration of 5 d [3–8 d]). First, EEG background was slowed in the theta-delta range in 48 (93%) patients, discontinuous in 25 patients (48%), and nonreactive in 17 patients (33%). Bifrontal slow waves were observed in 17 patients (33%). Early nonreactive EEG was associated with lower day-28 ventilator-free days (0 vs 16; p = 0.025), coma-free days (6 vs 22; p = 0.006), delirium-free days (0 vs 17; p = 0.006), and higher mortality (41% vs 11%; p = 0.027), whereas discontinuous background was associated with lower ventilator-free days (0 vs 17; p = 0.010), coma-free days (1 vs 22; p < 0.001), delirium-free days (0 vs 17; p = 0.001), and higher mortality (40% vs 4%; p = 0.001), independently of sedation and analgesia.

CONCLUSIONS:

Clinical and neurophysiologic cerebral dysfunction is frequent in COVID-19 ARDS patients. Early severe EEG abnormalities with nonreactive and/or discontinuous background activity are associated with delayed awakening, delirium, and day-28 mortality.

Associated Factors and Prognostic Implications of Non-convulsive Status Epilepticus in Ischemic Stroke Patients With Impaired Consciousness

Background and Purpose: Non-convulsive status epilepticus (NCSE) is common in patients with disorders of consciousness and can cause secondary brain injury. Our study aimed to explore the determinants and prognostic significance of NCSE in stroke patients with impaired consciousness.

Method: Consecutive ischemic stroke patients with impaired consciousness who were admitted to a neuro intensive care unit were enrolled for this study. Univariate and multivariable logistic regression were used to identify factors associated with NCSE and their correlation with prognosis.

Results: Among the 80 patients studied, 20 (25%) died during hospitalization, and 51 (63.75%) had unfavorable outcomes at the 3-month follow-up. A total of 31 patients (38.75%) developed NCSE during 24-h electroencephalogram (EEG) monitoring. Logistic regression revealed that NCSE was significantly associated with an increased risk of death during hospital stay and adverse outcomes at the 3-month follow-up. Patients with stroke involving the cerebral cortex or those who had a severely depressed level of consciousness were more prone to epileptogenesis after stroke.

Conclusion: Our results suggest that NCSE is a common complication of ischemic stroke, and is associated with both in-hospital mortality and dependency at the 3-month follow-up. Long-term video EEG monitoring of stroke patients is, therefore required, especially for those with severe consciousness disorders (stupor or coma) or cortical injury.

The accuracy of various neuro-prognostication algorithms and the added value of neurofilament light chain dosage for patients resuscitated from shockable cardiac arrest: An ancillary analysis of the ISOCRATE study

PURPOSE

In current guidelines, neurological prognostication after cardiopulmonary resuscitation is based on a multimodal approach bundled in algorithms. Biomarkers are of particular interest because they are unaffected by interpretation bias. We assessed the predictive value of serum neurofilament light chains (NF-L) in patients with a shockable rhythm who received cardiopulmonary resuscitation, and evaluated the predictive value of a modified algorithm where NF-L dosage is included.

METHODS

All patients who were included participated in the randomized ISOCRATE trial. NF-L values 48 h after ROSC were compared for patients with a good (Cerebral Performance Category (CPC) 1 or 2) and a poor prognosis (CPC 3 to 5 or death). The benefit of adding NF-L dosage to the current guideline algorithm was then assessed for NF-L thresholds of 500 and 1,200 pg/ml as previously described.

RESULTS

NF-L was assayed for 49 patients. In patients with good versus those with poor outcomes, median NF-L values at 48 h were 72 ± 78 and 7,755 ± 9,501 pg/ml respectively (P < 0.0001; AUC [95 %CI] = 0.87 [0.74;0.99]). The sensitivity of the modified ESICM/ERC 2021 algorithm after adding NF-L with thresholds of 500 and 1,200 pg/ml was 0.74 (CI 95% 0.51-0.88) and 0.68 (CI 95% 0.46-0.86), respectively, versus 0.53 (CI 95% 0.32-0.73) for the unmodified algorithm. In three instances the specificity was 1.

CONCLUSION

High NF-L plasma levels 48 h after cardiac arrest was significantly associated with a poor outcome. Adjunction to the current guideline algorithm of an NF-L assay with a 500 pg/ml threshold 48 h after cardiac arrest provided the best sensitivity compared to the algorithm alone, while specificity remained excellent.

Heart rate complexity: An early prognostic marker of patient outcome after cardiac arrest

OBJECTIVE

Early prognostication in comatose patients after cardiac arrest (CA) is difficult but essential to inform relatives and optimize treatment. Here we investigate the predictive value of heart-rate variability captured by multiscale entropy (MSE) for long-term outcomes in comatose patients during the first 24 hours after CA.

METHODS

In this retrospective analysis of prospective multi-centric cohort, we analyzed MSE of the heart rate in 79 comatose patients after CA while undergoing targeted temperature management and sedation during the first day of coma. From the MSE, two complexity indices were derived by summing values over short and long time scales (CI_s and CI_l). We splitted the data in training and test datasets for analysing the predictive value for patient outcomes (defined as best cerebral performance category within 3 months) of CI_s and CI_l.

RESULTS

Across the whole dataset, CI_l provided the best sensitivity, specificity, and accuracy (88%, 75%, and 82%, respectively). Positive and negative predictive power were 81% and 84%.

CONCLUSIONS

Characterizing the complexity of the ECG in patients after CA provides an accurate prediction of both favorable and unfavorable outcomes.

SIGNIFICANCE

The analysis of heartrate variability by means of MSE provides accurate outcome prediction on the first day of coma.

Postresuscitation care and prognostication

PURPOSE OF REVIEW

Most patients who are successfully resuscitated after cardiac arrest are initially comatose and require mechanical ventilation and other organ support in an ICU. Knowledge about the optimal strategy for treating these patients is evolving rapidly. This review will summarize the evidence on key aspects of postarrest care and prognostication, with a focus on actionable parameters that may impact patient survival and neurologic outcomes.

RECENT FINDINGS

Optimal targets for arterial blood oxygen and carbon dioxide in comatose postcardiac arrest patients remain uncertain. Observational data are conflicting and the few randomized controlled trials to date have failed to show that different ranges of blood oxygen and carbon dioxide values impact on biomarkers of neurological injury. The Targeted Temperature Management 2 (TTM-2) trial has documented no difference in 6-month mortality among comatose postcardiac arrest patients managed at 33 oC versus controlled normothermia. An extensive systematic review of the evidence on prognostication of outcome among comatose postcardiac arrest patients underpins new prognostication guidelines.

SUMMARY

Clinical guidelines for postresuscitation care have recently been updated and incorporate all the available science supporting the treatment of postcardiac arrests. At a minimum, fever should be strictly avoided in comatose postcardiac patients. Prognostication must involve multiple modalities and should not be attempted until assessment confounders have been sufficiently excluded.

Background frequency can enhance the prognostication power of EEG patterns categories in comatose cardiac arrest survivors: a prospective, multicenter, observational cohort study

Background

We assessed the prognostic accuracy of the standardized electroencephalography (EEG) patterns (“highly malignant,” “malignant,” and “benign”) according to the EEG timing (early vs. late) and investigated the EEG features to enhance the predictive power for poor neurologic outcome at 1 month after cardiac arrest.

Methods

This prospective, multicenter, observational, cohort study using data from Korean Hypothermia Network prospective registry included adult patients with out-of-hospital cardiac arrest (OHCA) treated with targeted temperature management (TTM) and underwent standard EEG within 7 days after cardiac arrest from 14 university-affiliated teaching hospitals in South Korea between October 2015 and December 2018. Early EEG was defined as EEG performed within 72 h after cardiac arrest. The primary outcome was poor neurological outcome (Cerebral Performance Category score 3–5) at 1 month.

Results

Among 489 comatose OHCA survivors with a median EEG time of 46.6 h, the “highly malignant” pattern (40.7%) was most prevalent, followed by the “benign” (33.9%) and “malignant” (25.4%) patterns. All patients with the highly malignant EEG pattern had poor neurologic outcomes, with 100% specificity in both groups but 59.3% and 56.1% sensitivity in the early and late EEG groups, respectively. However, for patients with “malignant” patterns, 84.8% sensitivity, 77.0% specificity, and 89.5% positive predictive value for poor neurologic outcome were observed. Only 3.5% (9/256) of patients with background EEG frequency of predominant delta waves or undetermined had good neurologic survival. The combination of “highly malignant” or “malignant” EEG pattern with background frequency of delta waves or undetermined increased specificity and positive predictive value, respectively, to up to 98.0% and 98.7%.

Conclusions

The “highly malignant” patterns predicted poor neurologic outcome with a high specificity regardless of EEG measurement time. The assessment of predominant background frequency in addition to EEG patterns can increase the prognostic value of OHCA survivors.

Trial registration KORHN-PRO, NCT02827422. Registered 11 September 2016—Retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03823-y.

Brain injury after cardiac arrest: pathophysiology, treatment, and prognosis

Post-cardiac arrest brain injury (PCABI) is caused by initial ischaemia and subsequent reperfusion of the brain following resuscitation. In those who are admitted to intensive care unit after cardiac arrest, PCABI manifests as coma, and is the main cause of mortality and long-term disability. This review describes the mechanisms of PCABI, its treatment options, its outcomes, and the suggested strategies for outcome prediction.

EEG patterns and their correlations with short- and long-term mortality in patients with hypoxic encephalopathy

OBJECTIVE

To analyze the association between electroencephalographic (EEG) patterns and overall, short- and long-term mortality in patients with hypoxic encephalopathy (HE).

METHODS

Retrospective, mono-center analysis of 199 patients using univariate log-rank tests (LR) and multivariate cox regression (MCR).

RESULTS

Short-term mortality, defined as death within 30-days post-discharge was 54.8%. Long-term mortality rates were 69.8%, 71.9%, and 72.9%, at 12-, 24-, and 36-months post-HE, respectively. LR revealed a significant association between EEG suppression (SUP) and short-term mortality, and identified low voltage EEG (LV), burst suppression (BSP), periodic discharges (PD) and post-hypoxic status epilepticus (PSE) as well as missing (aBA) or non-reactive background activity (nrBA) as predictors for overall, short- and long-term mortality. MCR indicated SUP, LV, BSP, PD, aBA and nrBA as significantly associated with overall and short-term mortality to varying extents. LV and BSP were significant predictors for long-term mortality in short-term survivors. Rhythmic delta activity, stimulus induced rhythmic, periodic or ictal discharges and sharp waves were not significantly associated with a higher mortality.

CONCLUSION

The presence of several specific EEG patterns can help to predict overall, short- and long-term mortality in HE patients.

SIGNIFICANCE

The present findings may help to improve the challenging prognosis estimation in HE patients.

Serum markers of brain injury can predict good neurological outcome after out-of-hospital cardiac arrest

Purpose

The majority of unconscious patients after cardiac arrest (CA) do not fulfill guideline criteria for a likely poor outcome, their prognosis is considered “indeterminate”. We compared brain injury markers in blood for prediction of good outcome and for identifying false positive predictions of poor outcome as recommended by guidelines.

Methods

Retrospective analysis of prospectively collected serum samples at 24, 48 and 72 h post arrest within the Target Temperature Management after out-of-hospital cardiac arrest (TTM)-trial. Clinically available markers neuron-specific enolase (NSE) and S100B, and novel markers neurofilament light chain (NFL), total tau, ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) were analysed. Normal levels with a priori cutoffs specified by reference laboratories or defined from literature were used to predict good outcome (no to moderate disability, Cerebral Performance Category scale 1–2) at 6 months.

Results

Seven hundred and seventeen patients were included. Normal NFL, tau and GFAP had the highest sensitivities (97.2–98% of poor outcome patients had abnormal serum levels) and NPV (normal levels predicted good outcome in 87–95% of patients). Normal S100B and NSE predicted good outcome with NPV 76–82.2%. Normal NSE correctly identified 67/190 (35.3%) patients with good outcome among those classified as “indeterminate outcome” by guidelines. Five patients with single pathological prognostic findings despite normal biomarkers had good outcome.

Conclusion

Low levels of brain injury markers in blood are associated with good neurological outcome after CA. Incorporating biomarkers into neuroprognostication may help prevent premature withdrawal of life-sustaining therapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-021-06481-4.

Frequency of Withdrawal of Life-Sustaining Therapy for Perceived Poor Neurologic Prognosis

Supplemental Digital Content is available in the text.

OBJECTIVES:

To measure the frequency of withdrawal of life-sustaining therapy for perceived poor neurologic prognosis among decedents in hospitals of different sizes and teaching statuses.

DESIGN:

We performed a multicenter, retrospective cohort study.

SETTING:

Four large teaching hospitals, four affiliated small teaching hospitals, and nine affiliated nonteaching hospitals in the United States.

PATIENTS:

We included a sample of all adult inpatient decedents between August 2017 and August 2019.

MEASUREMENTS AND MAIN RESULTS:

We reviewed inpatient notes and categorized the immediately preceding circumstances as withdrawal of life-sustaining therapy for perceived poor neurologic prognosis, withdrawal of life-sustaining therapy for nonneurologic reasons, limitations or withholding of life support or resuscitation, cardiac death despite full treatment, or brain death. Of 2,100 patients, median age was 71 years (interquartile range, 60–81 yr), median hospital length of stay was 5 days (interquartile range, 2–11 d), and 1,326 (63%) were treated at four large teaching hospitals. Withdrawal of life-sustaining therapy for perceived poor neurologic prognosis occurred in 516 patients (25%) and was the sole contributing factor to death in 331 (15%). Withdrawal of life-sustaining therapy for perceived poor neurologic prognosis was common in all hospitals: 30% of deaths at large teaching hospitals, 19% of deaths in small teaching hospitals, and 15% of deaths at nonteaching hospitals. Withdrawal of life-sustaining therapy for perceived poor neurologic prognosis happened frequently across all hospital units. Withdrawal of life-sustaining therapy for perceived poor neurologic prognosis contributed to one in 12 deaths in patients without a primary neurologic diagnosis. After accounting for patient and hospital characteristics, significant between-hospital variability in the odds of withdrawal of life-sustaining therapy for perceived poor neurologic prognosis persisted.

CONCLUSIONS:

A quarter of inpatient deaths in this cohort occurred after withdrawal of life-sustaining therapy for perceived poor neurologic prognosis. The rate of withdrawal of life-sustaining therapy for perceived poor neurologic prognosis occurred commonly in all type of hospital settings. We observed significant unexplained variation in the odds of withdrawal of life-sustaining therapy for perceived poor neurologic prognosis across participating hospitals.

Multimodal Approach to Predict Neurological Outcome after Cardiac Arrest: A Single-Center Experience

Introduction: The aims of this study were to assess the concordance of different tools and to describe the accuracy of a multimodal approach to predict unfavorable neurological outcome (UO) in cardiac arrest patients. Methods: Retrospective study of adult (>18 years) cardiac arrest patients who underwent multimodal monitoring; UO was defined as cerebral performance category 3–5 at 3 months. Predictors of UO were neurological pupillary index (NPi) ≤ 2 at 24 h; highly malignant patterns on EEG (HMp) within 48 h; bilateral absence of N20 waves on somato-sensory evoked potentials; and neuron-specific enolase (NSE) > 75 μg/L. Time-dependent decisional tree (i.e., NPi on day 1; HMp on day 1–2; absent N20 on day 2–3; highest NSE) and classification and regression tree (CART) analysis were used to assess the prediction of UO. Results: Of 137 patients, 104 (73%) had UO. Abnormal NPi, HMp on day 1 or 2, the bilateral absence of N20 or NSE >75 mcg/L had a specificity of 100% to predict UO. The presence of abnormal NPi was highly concordant with HMp and high NSE, and absence of N20 or high NSE with HMp. However, HMp had weak to moderate concordance with other predictors. The time-dependent decisional tree approach identified 73/103 patients (70%) with UO, showing a sensitivity of 71% and a specificity of 100%. Using the CART approach, HMp on EEG was the only variable significantly associated with UO. Conclusions: This study suggests that patients with UO had often at least two predictors of UO, except for HMp. A multimodal time-dependent approach may be helpful in the prediction of UO after CA. EEG should be included in all multimodal prognostic models.

Application of a standardized EEG pattern classification in the assessment of neurological prognosis after cardiac arrest: A retrospective analysis

INTRODUCTION

Electroencephalogram (EEG) is used in the neurological prognostication after cardiac arrest. "Highly malignant" EEG patterns classified according to Westhall have a high specificity for poor neurological outcome when applied within protocols of recent studies. However, their predictive performance when applied in everyday clinical practice has not been investigated. We studied the prognostic accuracy and the interrater agreement when standardized EEG patterns were analysed and compared to neurological outcome in a patient cohort at a tertiary centre not involved in the original study of the standardized EEG pattern classification.

METHODS

Comatose patients treated for out-of-hospital cardiac arrest were included. Poor outcome was defined as Cerebral Performance Category 3-5. Two senior consultants and one resident in clinical neurophysiology, blinded to clinical data and outcome, independently reviewed their EEG registrations and categorised the pattern as "highly malignant", "malignant" or "benign". These categories were compared to neurological outcome at hospital discharge. Interrater agreement was assessed using Cohen's Kappa.

RESULTS

In total, 62 patients were included. The median (IQR) time to EEG was 59 (42-91) h after return of spontaneous circulation. Poor outcome was found in 52 (84%) patients. In 21 patients at least one of the raters considered the EEG to contain a "highly malignant" pattern, all with poor outcome (42% sensitivity, 100% specificity). The interrater agreement varied from kappa 0.62 to 0.29.

CONCLUSION

"Highly malignant" patterns predict poor neurological outcome with a high specificity in everyday practice. However, interrater agreement may vary substantially even between experienced EEG interpreters.

Targeted temperature management and early neuro-prognostication after cardiac arrest

Targeted temperature management (TTM) is a recommended neuroprotective intervention for coma after out-of-hospital cardiac arrest (OHCA). However, controversies exist concerning the proper implementation and overall efficacy of post-CA TTM, particularly related to optimal timing and depth of TTM and cooling methods. A review of the literature finds that optimizing and individualizing TTM remains an open question requiring further clinical investigation. This paper will summarize the preclinical and clinical trial data to-date, current recommendations, and future directions of this therapy, including new cooling methods under investigation. For now, early induction, maintenance for at least 24 hours, and slow rewarming utilizing endovascular methods may be preferred. Moreover, timely and accurate neuro-prognostication is valuable for guiding ethical and cost-effective management of post-CA coma. Current evidence for early neuro-prognostication after TTM suggests that a combination of initial prediction models, biomarkers, neuroimaging, and electrophysiological methods is the optimal strategy in predicting neurological functional outcomes.

Stimulus-Induced Rhythmic or Periodic Intermittent Discharges (SIRPIDs) in patients with triphasic waves and Creutzfeldt-Jakob disease

Since the term Stimulus-Induced Rhythmic, Periodic, or Ictal Discharges (SIRPIDs) was introduced into the vocabulary of electrophysiologists/neurologists, there has been an ongoing debate about its significance, as well as its correlation with outcomes. SIRPIDs are frequently seen in patients who are critically ill from various causes. The literature reflects the findings of triphasic morphology, with the generalized periodic discharge (GPD) classification in many patients with SIRPIDs: toxic/metabolic encephalopathies, septic, and hypoxemic/hypercapnic encephalopathies, but also sharp periodic complexes in Creutzfeldt-Jakob disease and advanced Alzheimer's disease. In these settings, GPDs disappear when patients fall asleep and reappear when patients spontaneously wake up, or are awoken by an external stimulus, or sometimes because of a respiratory event, with the possibility of the appearance of GPDs with a cyclic alternating pattern. SIRPIDs may be seen as a transitional pattern between sleep and waking states, corresponding to a postarousal/awakening phenomenon. As SIRPIDs are a transient phenomenon and can usually be recorded repeatedly with each stimulation, the word "Ictal" could be replaced by "Intermittent": Stimulus-Induced Rhythmic or Periodic Intermittent Discharges. However, considering that SIRPIDs may be "potentially ictal" or on an "ictal-interictal continuum" in some situations, the "plus" modifier may be added: SIRPIDs-plus.