Neurological Prognostication Research Articles

Elevated Initial Serum Phosphate Levels Predict Higher Mortality and Impaired Neurological Outcome in Cardiac Arrest Patients with Return of Spontaneous Circulation.

Purpose: Although a moderate proportion of cardiac arrest (CA) patients achieve a return of spontaneous circulation (ROSC), few survive to discharge, mostly with poor neurological development. As serum phosphate levels were described as elevated after cardiopulmonary resuscitation (CPR), we asked whether these elevations would predict a higher risk of mortality and impaired neurological outcome in CA patients following ROSC. Methods: Initial serum phosphate levels, survival, and neurologic status at discharge of 488 non-traumatic CA patients treated at a single German hospital after achieving ROSC were analyzed. The cut-off value of phosphate for mortality prediction was determined using the receiver operator characteristic (ROC) curve, and patients were divided accordingly for comparison. Results were validated by analyzing phosphate levels in a multi-centric cohort containing 3299 CA patients from the eICU database of the United States. Results: In the German cohort, ROC analysis showed a 90% specificity for phosphate levels >2.7 mmol/L to predict mortality (AUC: 0.76, p < 0.0001), and phosphate level elevations were associated with higher in-hospital mortality (crude odds ratio 3.04, 95% CI 2.32 to 4.08). Patients with initial phosphate levels >2.7 mmol/L had significantly higher mortality in both analyzed collectives (p < 0.0001). Similarly, patients from the German cohort who initially had higher phosphate levels also showed a higher proportion of impaired neurological status at discharge and morphological signs of brain injury. Conclusions: In CA patients following ROSC, initial serum phosphate levels >2.7 mmol/L predict higher mortality and impaired neurological outcome. Our data suggests that phosphate determination might improve the preciseness of the overall and neurologic prognostication in patients after CPR following ROSC.

Brain injury markers in blood predict signs of hypoxic ischaemic encephalopathy on head computed tomography after cardiac arrest

Background/AimSigns of hypoxic ischaemic encephalopathy (HIE) on head computed tomography (CT) predicts poor neurological outcome after cardiac arrest. We explore whether levels of brain injury markers in blood could predict the likelihood of HIE on CT. MethodsRetrospective analysis of CT performed at 24–168 h post cardiac arrest on clinical indication within the Target Temperature Management after out-of-hospital cardiac arrest-trial. Biomarkers prospectively collected at 24- and 48 h post-arrest were analysed for neuron specific enolase (NSE), neurofilament light (NFL), total-tau and glial fibrillary acidic protein (GFAP). HIE was assessed through visual evaluation and quantitative grey-white-matter ratio (GWR) was retrospectively calculated on Swedish subjects with original images available. ResultsIn total, 95 patients were included. The performance to predict HIE on CT (performed at IQR 73–116 h) at 48 h was similar for all biomarkers, assessed as area under the receiving operating characteristic curve (AUC) NSE 0.82 (0.71–0.94), NFL 0.79 (0.67–0.91), total-tau 0.84 (0.74–0.95), GFAP 0.79 (0.67–0.90). The predictive performance of biomarker levels at 24 h was AUC 0.72–0.81. At 48 h biomarker levels below Youden Index accurately excluded HIE in 77.3–91.7% (negative predictive value) and levels above Youden Index correctly predicted HIE in 73.3–83.7% (positive predictive value). NSE cut-off at 48 h was 48 ng/ml. Elevated biomarker levels irrespective of timepoint significantly correlated with lower GWR. ConclusionBiomarker levels can assess the likelihood of a patient presenting with HIE on CT and could be used to select suitable patients for CT-examination during neurological prognostication in unconscious cardiac arrest patients.

Long-Term Outcomes of Patients with Stroke Predicted by Clinicians to have no Chance of Meaningful Recovery: A Japanese Cohort Study.

Little is known about the natural history of comatose patients with brain injury, as in many countries most of these patients die in the context of withdrawal of life-sustaining therapies (WLSTs). The accuracy of predicting recovery that is used to guide goals-of-care decisions is uncertain. We examined long-term outcomes of patients with ischemic or hemorrhagic stroke predicted by experienced clinicians to have no chance of meaningful recovery in Japan, where WLST in patients with isolated neurological disease is uncommon. We retrospectively reviewed the medical records of all patients admitted with acute ischemic stroke, intracerebral hemorrhage, or nontraumatic subarachnoid hemorrhage between January 2018 and December 2020 to a neurocritical care unit at Toda Medical Group Asaka Medical Center in Saitama, Japan. We screened for patients who were predicted by the attending physician on postinjury day 1-4 to have no chance of meaningful recovery. Primary outcome measures were disposition at hospital discharge and the ability to follow commands and functional outcomes measured by the Glasgow Outcome Scale-Extended (GOS-E), which was assessed 6months after injury. From 860 screened patients, we identified 40 patients (14 with acute ischemic stroke, 19 with intracerebral hemorrhage, and 7 with subarachnoid hemorrhage) who were predicted to have no chance of meaningful recovery. Median age was 77years (interquartile range 64-85), 53% (n = 21) were women, and 80% (n = 32) had no functional deficits prior to hospitalization. Six months after injury, 17 patients were dead, 14 lived in a long-term care hospital, 3 lived at home, 2 lived in a rehabilitation center, and 2 lived in a nursing home. Three patients reliably followed commands, two were in a vegetative state (GOS-E 2), four fully depended on others and required constant assistance (GOS-E 3), one could be left alone independently for 8h per day but remained dependent (GOS-E 4), and one was independent and able to return to work-like activities (GOS-E 5). In the absence of WLST, almost half of the patients predicted shortly after the injury to have no chance of meaningful recovery were dead 6months after the injury. A small minority of patients had good functional recovery, highlighting the need for more accurate neurological prognostication.

Role of diffusion tensor imaging in neurological prognostication in spinal tuberculosis – A prospective pilot study

PurposeThe current study aims to explore the correlation between the Diffusion Tensor Imaging (DTI) indices and neurological status of individuals with TB spine with neurological deficit (TBSND). Further, factors affecting the rate of post-operative neurological recovery were also analysed with special emphasis on DTI indices. MethodsThe current study included 51 individuals with TBSND undergoing posterior instrumentation and posterolateral decompression. All individuals underwent DTI scanning at the site of compression and at a normal level proximal to the disease. The relationship of the DTI parameters with the pre-operative motor, sensory and total scores were analysed. All clinical and radiological parameters were further analysed on the basis of their recovery patterns after decompression surgery. ResultsThe FA values at the compressed segment were significantly lower while the ADC values were significantly higher when compared with the non-compressed levels. Pre-operative motor and total neurological scores were found to be significantly positively correlated to FA values and ADC values. Faster neurological recovery after decompression was associated with high FA values and younger age while incomplete neurological recovery was associated with high ADC values. ConclusionsHigher FA indices along with lower age constitute independent predictors for faster neurological recovery following decompression. FA values correlate with the neurological status of individuals with TBSND and its use may be explored as an adjunct to objectively assess the cord damage and for predicting neurological recovery pattern after decompression in such individuals.

Bayesian Outcome Prediction After Resuscitation From Cardiac Arrest.

Postarrest prognostication research does not typically account for the sequential nature of real-life data acquisition and interpretation and reports nonintuitive estimates of uncertainty. Bayesian approaches offer advantages well suited to prognostication. We used Bayesian regression to explore the usefulness of sequential prognostic indicators in the context of prior knowledge and compared this with a guideline-concordant algorithm. We included patients hospitalized at a single center after cardiac arrest. We extracted prospective data and assumed these data accrued over time as in routine practice. We considered predictors demographic and arrest characteristics, initial and daily neurologic examination, laboratory results, therapeutic interventions, brain imaging, and EEG. We fit Bayesian hierarchical generalized linear multivariate models predicting discharge Cerebral Performance Category (CPC) 4 or 5 (poor outcomes) vs 1-3 including sequential clinical and prognostic data. We explored outcome posterior probability distributions (PPDs) for individual patients and overall. As a comparator, we applied the 2021 European Resuscitation Council and European Society of Intensive Care Medicine (ERC/ESICM) guidelines. We included 2,692 patients of whom 864 (35%) were discharged with a CPC 1-3. Patients' outcome PPDs became narrow and shifted toward 0 or 1 as sequentially acquired information was added to models. These changes were largest after arrest characteristics and initial neurologic examination were included. Using information typically available at or before intensive care unit admission, sensitivity predicting poor outcome was 51% with a 0.6% false-positive rate. In our most comprehensive model, sensitivity for poor outcome prediction was 76% with 0.6% false-positive rate (FPR). The ERC/ESICM algorithm applied to 547 of 2,692 patients and yielded 36% sensitivity with 0% FPR. Bayesian models offer advantages well suited to prognostication research. On balance, our findings support the view that in expert hands, accurate neurologic prognostication is possible in many cases before 72 hours postarrest. Although we caution against early withdrawal of life-sustaining therapies, rapid outcome prediction can inform clinical decision making and future clinical trials.

The biomarkers NSE and S100b have high predictive value regarding mortality in children resuscitated after cardiac arrest

Abstract Funding Acknowledgements Type of funding sources: None. Background Children suffering from cardiac arrest (CA) have high mortality due to neurological injury and prognostication tools are sparse. The biomarkers neuron-specific enolase (NSE) and S100 calcium-binding protein b (S100b) are known to predict outcome in adult CA, but the use in children is not well investigated. Purpose To assess the predictive value of the biomarkers NSE and S100b in children resuscitated from CA the first 72 hours following admission. Methods In-hospital and out-of-hospital CA patients, age &amp;lt;18 and comatose at intensive care unit admission, were included consecutively at our hospital from 2011-2021 and analyses of NSE and S100b levels were made at three timepoints following admission: 12 to 24 hours, 24 to 48 hours and 48 to 72 hours. Patients were divided into groups of "survivors" and "non-survivors". The primary endpoint was 30-day mortality. Biomarker levels in the two groups were compared using the Mann-Whitney U test. Predictive value of NSE and S100b was determined by applying receiver operating characteristics (ROC) curves with area under the curves (AUC) presented for each timepoint. Results We included 32 resuscitated patients with 12 (38%) patients dying within 30 days from resuscitation, i.e. 20 (62%) patients surviving. 14 patients were male sex and the median age was 15 years (6-16) in survivors. In non-survivors 8 patients were male sex and the median age was 7 years (2-15). Significantly higher levels of NSE and S100b were found in non-survivors compared to survivors at all timepoints following CA (Figure 1). For NSE the ROC curves obtained AUC values of 0.91 (0.75-1.0), 0.97 (0.92-1.0) and 0.98 (0.93-1.0) for prediction of 30-day mortality at the three timepoints. AUCs for S100b were 0.93 (0.84-1.0), 0.94 (0.86-1.0) and 0.94 (0.85-1.0), respectively. Conclusion Levels of NSE and S100b in children resuscitated from CA are associated with 30-day mortality and both biomarkers appear to be strong predictors of mortality.

Neurological Prognostication After Hypoglycemic Coma: Role of Clinical and EEG Findings.

Hypoglycemic coma (HC) is an uncommon but severe clinical condition associated with poor neurological outcome. There is a dearth of robust neurological prognostic factors after HC. On the other hand, there is an increasing body of literature on reliable prognostic markers in the postanoxic coma, a similar-albeit not identical-situation. The objective of this study was thus to investigate the use and predictive value of these markers in HC. We conducted a retrospective, multicenter, cohort study within five centers of the Critical Care EEG Monitoring Research Consortium. We queried our electroencephalography (EEG) databases to identify all patients undergoing continuous EEG monitoring after admission to an intensive care unit with HC (defined as Glasgow Coma Scale < 8 on admission and a first blood glucose level < 50mg/dL or not documented but in an obvious clinical context) between 01/01/2010 and 12/31/2020. We studied the association of findings at neurological examination (Glasgow Coma Scale motor subscale, pupillary light and corneal reflexes) and at continuous EEG monitoring(highly malignant patterns, reactivity, periodic discharges, seizures) with best neurological outcome within 3months after hospital discharge, defined by the Cerebral Performance Category as favorable (1-3: recovery of consciousness) versus unfavorable (4-5: lack of recovery of consciousness). We identified 60 patients (30 [50%] women; age 62 [51-72] years). Thirty-one and 29 patients had a favorable and unfavorable outcome, respectively. The presence of pupillary reflexes (24 [100%] vs. 17 [81%]; p value 0.04) and a motor subscore > 2 (22 [92%] vs. 12 [63%]; p value 0.03) at 48-72h were associated with a favorable outcome. A highly malignant EEG pattern was observed in 7 of 29 (24%) patients with unfavorable outcome versus 0 of 31 (0%) with favorable outcome, whereas the presence of EEG reactivity was observed in 28 of 31 (90%) patients with favorable outcome versus 13 of 29 (45%) with unfavorable outcome (p < 0.001 for comparison of all background categories). This preliminary study suggests that highly malignant EEG patterns might be reliable prognostic markers of unfavorable outcome after HC. Other EEG findings, including lack of EEG reactivity and seizures and clinical findings appear less accurate. These findings should be replicated in a larger multicenter prospective study.

Routine reporting of grey-white matter differentiation in early brain computed tomography in comatose patients after cardiac arrest: A substudy of the COACT trial

AimA multimodal approach is advised for neurological prognostication in comatose patients after out-of-hospital cardiac arrest (OHCA). Grey-white matter differentiation (grey-white ratio, GWR) obtained from a brain CT scan performed < 24 hours after return of circulation can be part of this approach. The aims of this study were to investigate the frequency and method of reporting the GWR in brain CT scan reports and their association with outcome. MethodsThis is a post-hoc descriptive analysis of the COACT trial. The primary endpoint was the reporting of GWR by the radiologist. Secondary endpoints were APACHE IV score, Cerebral Performance Categories at discharge and 90-day follow-up, Glasgow Coma Scale at discharge, GWR-stratified 1-year survival, and RAND-36 stratified by normal versus abnormal GWR. Associations were analysed using multivariable analysis. ResultsA total of 427 OHCA patients were included in this study, 234 (55%) of whom underwent a brain CT scan within 24 hours after ROSC. Median time between arrest and initial CT scan was 12 hours. In 195 patients (83%), the GWR was described in the reports, but always expressed qualitatively. The GWR was deemed abnormal in 57 (29%) CT scans. No differences were found in secondary endpoints between the two groups. ConclusionGWR was frequently described in CT scan reports. Early abnormal GWR, as assessed qualitatively by a radiologist within 24 hours after ROSC, was a poor predictor of neurological prognosis.

Ethical considerations in the management of infants with severe intraventricular hemorrhage

Intrinsic and extrinsic factors unique to neonatal care can complicate predictions of neurological outcomes for infants who suffer from severe intraventricular hemorrhage. While care decisions are driven by the same bioethical principles used in other domains, neurological prognostication can challenge concepts of futility, require careful examination of parental values, uncover biases and/or potentially compromise the best interests of the future child. In the following chapter we will review bioethical principles and relevant concepts, explore challenges to decision-making surrounding diagnoses of severe intraventricular hemorrhage and conclude with a brief review of practical approaches for counseling parents about neurodevelopmental impairment given the constraints of prognostic uncertainty and assumptions related to quality of life. We will argue that neurological findings alone, even in the setting of severe intraventricular hemorrhage, often do not constitute enough evidence for redirection of care but can be permissible when the entire neonatal condition is considered.

480 Deep Learning Analysis of Early MRI Scans After Severe Traumatic Brain Injury Predicts Long-term Outcomes

INTRODUCTION: Nearly three quarters of deaths in clinical trials of severe traumatic brain injury (TBI) occur due to withdrawal of life sustaining therapies, often due to perceived poor neurological prognosis. Most of these deaths occur within the first week when physicians historically lack enough accuracy to make these decisions. Prognostic models, such as the International Mission for Prognosis and Analysis of Clinical Trials in TBI, are designed to prognose outcomes upon admission and fail to utilize clinical information accumulated after admission. Quantitative prognostic models capturing this informaiton are needed to help guide care decisions. METHODS: We built our model using 266 patients from a prospective database of severe TBI patients from 2002 through 2018 with an MRI obtained 1 to 9 days post-trauma. We developed a customized, multi-channel, deep convolutional neural network model with an AlexNet backbone and pre-trained with ImageNet. The cohort was split into 80% training, 10% validation, and 10% independent testing set. We built separate models using T2 FLAIR and DWI sequences. To reduce the heterogeneity of imaging after a decompressive hemicraniectomy (DHC), we only used DHC patients for model training. RESULTS: No adverse clinical events occurred while obtaining early MRIs. The overall mortality was 25% (68) and 20% (34) in the 170 patients who did not have a DHC. In the independent test cohort, the T2 FLAIR model performed best, with an area under the curve (AUC) of 0.80 (95% Confidence Interval: 0.57-0.94) and accuracy of 83%. The DWI model had an AUC of 0.64 (0.50 – 0.79) with an accuracy of 78%. With a specificity of 100% (i.e., never recommending withdrawal of care in a patient who would otherwise survive), the sensitivity for mortality was 57%. For identifying patients with favorable recovery potential, the sensitivity was 83% with a specificity of 80%. CONCLUSION: Early MRIs are safe to obtain in the severe TBI patient population. Deep learning analysis of these MRIs can predict 6-month mortality to help guide neurological prognostication.

Neurologic Prognostication After Cardiac Arrest Using Brain Biomarkers

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). To assess the neuroprognostic utility of bloodborne brain injury biomarkers in patients with cardiac arrest with HIBI. 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. 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 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. 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. 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]). 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. PROSPERO Identifier: CRD42020157366.

Physicians’ cognitive approach to prognostication after cardiac arrest

ObjectiveElucidate how physicians formulate a neurological prognosis after cardiac arrest and compare differences between experts and general providers. MethodsWe performed semi-structured interviews with experts in post-arrest care and general physicians. We created an initial model and interview guide based on professional society guidelines. Two authors independently coded interviews based on this initial model, then identified new topics not included in it. To describe individual physicians’ cognitive approach to prognostication, we created a graphical representation. We summarized these individual “mental models” into a single overall model, as well as two models stratified by expertise. ResultsWe performed 36 interviews (17 experts and 19 generalists), most of whom practice in Europe (23) or North America (12). Participants described their approach to prognosis formulation as complex and iterative, with sequential and repeated data acquisition, interpretation, and prognosis formulation. Eventually, this cycle results in a final prognosis and treatment recommendation. Commonly mentioned factors were diagnostic test performance, time from arrest, patient characteristics. Participants also discussed factors rarely discussed in prognostication research including physician and hospital characteristics. We found no substantial differences between experts and general physicians. ConclusionPhysicians’ cognitive approach to neurologic prognostication is complex and influenced by many factors, including some rarely considered in current research. Understanding these processes better could inform interventions designed to aid physicians in prognostication.

Role of Hypothermia in Cardiogenic Shock

While patient management in the intensive care unit has undoubtedly improved over the last 20 years, many questions remain. Neurological prognostication has become very important and aided in improving survival outcomes over time. The limited number of randomized control trials and limitations from currently completed studies leave the field with little certainty regarding targeted temperature management. In addition, implementing hypothermia can use multiple methods and protocols that impact the interpretation and comparison of results.

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

PurposeIn 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. MethodsAll 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. ResultsNF-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. ConclusionHigh 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.

Prognostic role of serum neutrophil gelatinase-associated lipocalin in cardiac arrest patients: A prospective observational study.

Accurate neurological prognostication is of the utmost importance to avoid futile treatments in patients treated with targeted temperature management (TTM) after out-of-hospital cardiac arrest (OHCA). This study aimed to investigate the prognostic value of serum neutrophil gelatinase-associated lipocalin (NGAL) by comparing with neuron-specific enolase (NSE), which is currently recommended by international guidelines in patients treated with TTM after OHCA.The study included 85 comatose adult patients with OHCA who underwent TTM between May 2018 and December 2020. Serum NGAL and NSE were measured at 24-hour intervals until 72 hours after return of spontaneous circulation (ROSC). The primary outcome was their prognostic performance for poor neurological outcome at 3 months after OHCA.Forty-nine patients (57.6%) had a poor neurological outcome; NGAL levels at all time points measured were significantly higher in these patients than in those with a good outcome (P < .01). NGAL showed lower maximal sensitivity (95% confidence interval [CI]) under a false-positive rate of 0% for the primary outcome compared with NSE (18.2% [95% CI 8.2–32.7] vs 66.7% [95% CI 50.5–80.4]). The combination of NGAL with NSE at 48 h showed the highest sensitivity (69.1% [95% CI 52.9–82.4]) and had the highest area under the curve (0.91 [95% CI 0.81–0.96]) for a poor outcome. The prognostic performance of NGAL alone was inadequate at all time points. However, NGAL combined with NSE at 24 and 28 hours after ROSC showed improved sensitivity compared to NGAL alone.NGAL should be considered a supplementary biomarker in combination with NSE for prognostication in patients with OHCA treated with TTM.

Mapping the Unconscious Brain: Insights From Advanced Neuroimaging.

Recent advances in neuroimaging have been a preeminent factor in the scientific effort to unravel mechanisms of conscious awareness and the pathophysiology of disorders of consciousness. In the first part of this review, we selectively discuss operational models of consciousness, the biophysical signal that is measured using different imaging modalities, and knowledge on disorders of consciousness that has been gleaned with each neuroimaging modality. Techniques considered include diffusion-weighted imaging, diffusion tensor imaging, different types of nuclear medicine imaging, functional MRI, magnetoencephalography, and the combined transcranial magnetic stimulation-electroencephalography approach. In the second part of this article, we provide an overview of how advanced neuroimaging can be leveraged to support neurological prognostication, the use of machine learning to process high-dimensional imaging data, potential applications in clinical practice, and future directions.

Continuous Amplitude-Integrated Electroencephalography During Neonatal and Pediatric Extracorporeal Membrane Oxygenation.

Early prognostication of neurologic outcome in neonates and children supported with extra-corporeal membrane oxygenation (ECMO) is challenging. Amplitude-integrated EEG (aEEG) offers the advantages of continuous monitoring and 24-hours availability at the bedside for intensive care unit providers. The objective of this study was to describe the early electrophysiological background patterns of neonates and children undergoing ECMO and their association with neurologic outcomes. This was a retrospective review of neonates and children undergoing ECMO and monitored with aEEG. Amplitude-integrated EEG was summarized as an aEEG background score determined within the first 24 hours of ECMO and divided in 3-hour periods. Screening for electrical seizures was performed throughout the full ECMO duration. Neurologic outcome was defined by the Pediatric Cerebral Performance Category score at hospital discharge. Seventy-three patients (median age 79 days [8-660], median weight 4.78 kg [3.24-10.02]) were included in the analysis. Thirty-two patients had a favorable neurologic outcome and 41 had an unfavorable neurologic outcome group at hospital discharge. A 24-hour aEEG background score >17 was associated with an unfavorable outcome with a sensitivity of 44%, a specificity of 97%, a positive predictive value of 95%, and a negative predictive value of 57%. In multivariate analysis, 24-hour aEEG background score was associated with unfavorable outcome (hazard ratio, 6.1; p = 0.001; 95% confidence interval, 2.31-16.24). The presence of seizures was not associated with neurologic outcome at hospital discharge. Continuous aEEG provides accurate neurologic prognostication in neonates and children supported with ECMO. Early aEEG monitoring may help intensive care unit providers to guide clinical care and family counseling.

Prognostic Effects of Vasomotor Reactivity during Targeted Temperature Management in Post-Cardiac Arrest Patients: A Retrospective Observational Study.

Early and precise neurological prognostication without self-fulfilling prophecy is challenging in post-cardiac arrest syndrome (PCAS), particularly during the targeted temperature management (TTM) period. This study aimed to investigate the feasibility of vasomotor reactivity (VMR) using transcranial Doppler (TCD) to determine whether final outcomes of patients with comatose PCAS are predicted. This study included patients who had out-of-hospital cardiac arrest in a tertiary referral hospital over 4 years. The eligible criteria included age ≥18 years, successful return of spontaneous circulation, TTM application, and bedside TCD examination within 72 h. Baseline demographics and multimodal prognostic parameters, including imaging findings, electrophysiological studies, and TCD-VMR parameters, were assessed. The final outcome parameter was cerebral performance category scale (CPC) at 1 month. Potential determinants were compared between good (CPC 1–2) and poor (CPC 3–5) outcome groups. The good outcome group (n = 41) (vs. poor (n = 117)) showed a higher VMR value (54.4% ± 33.0% vs. 25.1% ± 35.8%, p < 0.001). The addition of VMR to conventional prognostic parameters significantly improved the prediction power of good outcomes. This study suggests that TCD-VMR is a useful tool at the bedside to evaluate outcomes of patients with comatose PCAS during the TTM.

Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest

ObjectiveTo test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA). MethodsSeveral types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3–5 within 6 months. ResultsThe Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80–86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40–51) and 89% specificity (95%-CI 86–92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83–88) at 24 h after CA, with 62% sensitivity (95%-CI 57–67) and 84% specificity (95%-CI 79–88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives. ConclusionsQuantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data. SignificanceQuantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data.

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

IntroductionElectroencephalogram (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. MethodsComatose 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. ResultsIn 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.