Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness

BACKGROUND

In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations.

METHODS

We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome.

RESULTS

Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]).

CONCLUSIONS

Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

Subclinical Epileptiform Activity in Dementia with Lewy Bodies

BACKGROUND

Patients with dementia with Lewy bodies (DLB) have a higher probability of seizures than in normal aging and in other types of neurodegenerative disorders. Depositions of α-synuclein, a pathological hallmark of DLB, can induce network excitability, which can escalate into seizure activity. Indicator of seizures are epileptiform discharges as observed using electroencephalography (EEG). However, no studies have so far investigated the occurrence of interictal epileptiform discharges (IED) in patients with DLB.

OBJECTIVES

To investigate if IED as measured with ear-EEG occurs with a higher frequency in patients with DLB compared to healthy controls (HC).

METHODS

In this longitudinal observational exploratory study, 10 patients with DLB and 15 HC were included in the analysis. Patients with DLB underwent up to three ear-EEG recordings, each lasting up to 2 days, over a period of 6 months.

RESULTS

At baseline, IED were detected in 80% of patients with DLB and in 46.7% of HC. The spike frequency (spikes or sharp waves/24 hours) was significantly higher in patients with DLB as compared to HC with a risk ratio of 2.52 (CI, 1.42-4.61; P-value = 0.001). Most IED occurred at night.

CONCLUSIONS

Long-term outpatient ear-EEG monitoring detects IED in most patients with DLB with an increased spike frequency compared to HC. This study extends the spectrum of neurodegenerative disorders in which epileptiform discharges occurs at an elevated frequency. It is possible that epileptiform discharges are, therefore, a consequence of neurodegeneration. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Detection of subclinical epileptiform discharges in Alzheimer's disease using long-term outpatient EEG monitoring

BACKGROUND

In patients with Alzheimer's disease (AD) without clinical seizures, up to half have epileptiform discharges on long-term in-patient electroencephalography (EEG) recordings. Long-term in-patient monitoring is obtrusive, and expensive as compared to outpatient monitoring. No studies have so far investigated if long-term outpatient EEG monitoring is able to identify epileptiform discharges in AD. Our aim is to investigate if epileptiform discharges as measured with ear-EEG are more common in patients with AD compared to healthy elderly controls (HC).

METHODS

In this longitudinal observational study, 24 patients with mild to moderate AD and 15 age-matched HC were included in the analysis. Patients with AD underwent up to three ear-EEG recordings, each lasting up to two days, within 6 months.

RESULTS

The first recording was defined as the baseline recording. At baseline, epileptiform discharges were detected in 75.0% of patients with AD and in 46.7% of HC (p-value = 0.073). The spike frequency (spikes or sharp waves/24 h) was significantly higher in patients with AD as compared to HC with a risk ratio of 2.90 (CI: 1.77-5.01, p < 0.001). Most patients with AD (91.7%) showed epileptiform discharges when combining all ear-EEG recordings.

CONCLUSIONS

Long-term ear-EEG monitoring detects epileptiform discharges in most patients with AD with a three-fold increased spike frequency compared to HC, which most likely originates from the temporal lobes. Since most patients showed epileptiform discharges with multiple recordings, elevated spike frequency should be considered a marker of hyperexcitability in AD.

Progressive slowing of clonic phase predicts postictal generalized EEG suppression

OBJECTIVE

Postictal generalized electroencephalography (EEG) suppression (PGES) is a surrogate marker of sudden unexpected death in epilepsy (SUDEP). It is still unclear which ictal phenomena lead to prolonged PGES and increased risk of SUDEP. Semiology features of generalized convulsive seizure (GCS type 1) have been reported as a predictor of prolonged PGES. Progressive slowing of clonic phase (PSCP) has been observed in GCSs, with gradually increasing inhibitory periods interrupting the tonic contractions. We hypothesized that PSCP is associated with prolonged PGES.

METHODS

We analyzed 90 bilateral convulsive seizures in 50 consecutive patients (21 female; age: 11-62 years, median: 31 years) recruited to video-EEG monitoring. Five raters, blinded to all other data, independently assessed the presence of PSCP. PGES and seizure semiology were evaluated independently. We determined inter-rater agreement (IRA) for the presence of PSCP, and we evaluated its association, as well as that of other ictal features, with the occurrence of PGES, prolonged PGES (≥20 s) and very prolonged PGES (≥50 s) using multivariate logistic regression analysis.

RESULTS

We found substantial IRA for the presence of PSCP (percent agreement: 80%; beyond-chance agreement coefficient: .655). PSCP was an independent predictor of the occurrence of PGES and prolonged PGES (p < .001). All seizures with very prolonged PGES had PSCP. GCS type 1 was an independent predictor of occurrence of PGES (p = .02) and prolonged PGES (p = .03) but not of very prolonged PGES. Only half of the seizures with very prolonged PGES were GCS type 1.

SIGNIFICANCE

PSCP predicts prolonged PGES, emphasizing the importance of gradually increasing inhibitory phenomena at the end of the seizures. Our findings shed more light on the ictal phenomena leading to increased risk of SUDEP. These phenomena may provide basis for algorithms implemented into wearable devices for identifying GCS with increased risk of SUDEP.

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

Validation of Pediatric Idiopathic Generalized Epilepsy Diagnoses from the Danish National Patient Register During 1994‒2019

Objective

To identify pediatric idiopathic generalized epilepsy (IGE) during 1994–2019 using ICD-10 codes in the Danish National Patient Register and anti-seizure prescriptions in the Danish Prescription Database.

Study Design and Setting

We reviewed the medical records in children with ICD-10 codes for IGE before 18 years of age, and pediatric neurologists confirmed that the International League Against Epilepsy criteria were met. We estimated positive predictive values (PPV) and sensitivity for ICD-10 alone, including combinations of codes, anti-seizure prescription, and age at first code registration using medical record-validated diagnoses as gold standard.

Results

We validated the medical record in 969 children with an ICD-10 code of IGE, and 431 children had IGE (115 childhood absence epilepsy, 97 juvenile absence epilepsy, 192 juvenile myoclonic epilepsy, 27 generalized tonic-clonic seizures alone). By combining ICD-10 codes with antiseizure prescription and age at epilepsy code registration, we found a PPV for childhood absence epilepsy at 44% (95% confidence interval [CI]=34%‒54%) and for juvenile absence epilepsy at 44% (95% CI=36%–52%). However, ethosuximide prescription, age at ethosuximide code registration before age 8 years and a combination of ICD-10 codes yielded a PPV of 59% (95% CI=42%‒75%) for childhood absence epilepsy but the sensitivity was only 17% (20/115 children identified). For juvenile myoclonic epilepsy the highest PPV was 68% (95% CI=62%‒74%) using the code G40.3F plus antiseizure prescription and age at epilepsy code registration after age 8 years, with sensitivity of 85% (164/192 children identified). For generalized tonic-clonic seizures alone the highest PPV was 31% (95% CI=15%‒51%) using G40.3G during 2006–2019 plus antiseizure prescription and age at code registration after age 5 years.

Conclusion

The Danish National Patient Register and the Danish Prescription Database are not suitable for identifying children with IGE subtypes, except for juvenile myoclonic epilepsy which can be identified with caution.

Consciousness in Neurocritical Care Cohort Study Using fMRI and EEG (CONNECT-ME): Protocol for a Longitudinal Prospective Study and a Tertiary Clinical Care Service

Aims and Objectives: To facilitate individualized assessment of unresponsive patients in the intensive care unit for signs of preserved consciousness after acute brain injury. Background: Physicians and neuroscientists are increasingly recognizing a disturbing dilemma: Brain-injured patients who appear entirely unresponsive at the bedside may show signs of covert consciousness when examined by functional MRI (fMRI) or electroencephalography (EEG). According to a recent meta-analysis, roughly 15% of behaviorally unresponsive brain-injured patients can participate in mental tasks by modifying their brain activity during EEG- or fMRI-based paradigms, suggesting that they are conscious and misdiagnosed. This has major ethical and practical implications, including prognosis, treatment, resource allocation, and end-of-life decisions. However, EEG- or fMRI-based paradigms have so far typically been tested in chronic brain injury. Hence, as a novel approach, CONNECT-ME will import the full range of consciousness paradigms into neurocritical care. Methods: We will assess intensive care patients with acute brain injury for preserved consciousness by serial and multimodal evaluation using active, passive and resting state fMRI and EEG paradigms, as well as state-of-the-art clinical techniques including pupillometry and sophisticated clinical rating scales such as the Coma Recovery Scale-Revised. In addition, we are establishing a biobank (blood, cerebrospinal fluid and brain tissue, where available) to facilitate future genomic and microbiomic research to search for signatures of consciousness recovery. Discussion: We anticipate that this multimodal approach will add vital clinical information, including detection of preserved consciousness in patients previously thought of as unconscious, and improved (i.e., personalized) prognostication of individual patients. Our aim is two-fold: We wish to establish a cutting-edge tertiary care clinical service for unresponsive patients in the intensive care unit and lay the foundation for a fruitful multidisciplinary research environment for the study of consciousness in acute brain injury. Of note, CONNECT-ME will not only enhance our understanding of consciousness disorders in acute brain injury but it will also raise awareness for these patients who, for obvious reasons, have lacked a voice so far. Trial registration: The study is registered with clinicaltrials.org (ClinicalTrials.gov Identifier: NCT02644265).