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.

School performance and psychiatric comorbidity in childhood absence epilepsy: A Danish cohort study.

The aim was to determine school performance and psychiatric comorbidity in children with childhood absence epilepsy (CAE). We reviewed the medical records in children with ICD-10 codes for idiopathic generalized epilepsy before 18 years of age, and pediatric neurologists confirmed the International League Against Epilepsy criteria for CAE were met. Control groups were the general pediatric population or children with non-neurological chronic disease. Outcomes were from nationwide and population-based registers on school performance and psychiatric comorbidity. We compared the mean grade point average using linear regression and estimated hazard ratios (HR) using Cox regression for the other outcomes. Analyses were adjusted for the child's sex, and year of birth, and parental highest education, receipt of cash benefits or early disability pension. We included 114 children with CAE with a median age at onset of 5.9 years (interquartile range = 4.5-7.3 years). Compared with both population controls and non-neurological chronically ill children, children with CAE had increased hazard of special needs education (HR = 2.7, 95% confidence interval (CI) = 1.8-4.1, p < 0.0001), lower grade point average at 9th grade by 1.7 grade points (95% CI = -2.5 to -1.0, p < 0.001), increased ADHD medicine use (HR = 4.4, 95% CI = 2.7-7.2, p < 0.001), increased sleep medicine use (HR = 2.7, 95% CI = 1.7-4.3, p < 0.001), and increased psychiatry visits (HR = 2.1, 95% CI = 1.1-4.0, p = 0.03). In conclusion, children with CAE have increased psychiatric comorbidity and a considerable proportion of these children receive special needs education in primary/secondary school, albeit insufficient to normalize their considerably lower grade point average in the 9th grade.

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.

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.

School performance and psychiatric comorbidity in juvenile absence epilepsy and juvenile myoclonic epilepsy: a Danish population-based cohort study.

BACKGROUND: We aimed to determine school performance and psychiatric comorbidity in juvenile absence epilepsy (JAE), juvenile myoclonic epilepsy (JME), and generalized tonic-clonic seizures (GTCS) alone. METHODS: All children (< 18 years) fulfilled International League Against Epilepsy criteria after review of their medical records. Control groups were the pediatric background population or children with non-neurological chronic disease. Outcomes were on school performance and psychiatric comorbidity. We compared mean grade point averages using linear regression and estimated hazard ratios using Cox regression in the remaining analyses. We adjusted for the child's sex, age, and year of birth; and parental highest education, receipt of cash benefits or early retirement. RESULTS: We included 92 JAE, 190 JME, 27 GTCS alone, 15,084 non-neurological chronic disease controls, and population controls. JAE had two times increased hazard for special needs education compared with age-matched population controls (hazard ratio 2.2, 95% CI = 1.1‒4.6, p = 0.03); this was not seen in JME. Compared with population controls, both JAE and JME had lower grade point average in secondary and high school (JME: 9th grade: - 0.5 points, 95% CI = -0.9 to -0.06, p = 0.03; high school: - 0.6 points, 95% CI = -1.3 to -0.1, p = 0.04), and 8% fewer JME and 15% fewer JAE attended high school. Both JME and JAE had higher hazard for redeeming sleep medication compared with non-neurological chronic disease; additionally, JAE had increased hazard for ADHD medicine redemptions. CONCLUSIONS: Both JAE and JME had marginally poorer school performance; performance seemed worse in JAE than in JME. Both JAE and JME had increased use of sleep medication.

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.

Diagnostic yield of standard-wake and sleep EEG recordings.

OBJECTIVE: To investigate whether Posterior Dominant Rhythm (PDR) can be reliably assessed in sleep-EEG recordings and to investigate the diagnostic yield of standard-wake and sleep-recordings. METHODS: EEG recordings of 303 consecutive patients aged 18-88 years were analyzed. All patients had both standard-wake and sleep-recordings, including patients who had abnormal standard recordings. Melatonin was used in 6% of sleep EEGs, and sleep deprivation in 94%. The mean duration of sleep was 41 min. We measured the PDR frequency in standard and sleep-recordings, both before and after sleep. We compared the diagnostic yield of standard-wake and sleep EEG recordings. RESULTS: Compared to standard EEG, sleep-recordings showed a significantly lower PDR frequency, both when measured before and after sleep (p < 0.001). One-hundred-fifty-six patients (51%) had normal standard recordings, and 35 of them (22%) had abnormal findings in the sleep-recording. One-hundred-forty-seven patients had abnormal standard recordings and in 16 of them (11%) these abnormalities were not present in sleep-recording. CONCLUSIONS: PDR is significantly slower in the wake periods of sleep-recordings, compared to standard wake recordings. SIGNIFICANCE: Sleep and standard wake recordings are complementary.

Do patients need to stay in bed all day in the Epilepsy Monitoring Unit? Safety data from a non-restrictive setting.

PURPOSE: To assess whether injuries occur more often in an Epilepsy Monitoring Unit (EMU) where portable EEG amplifiers are used, and where patients can freely move within a large area during the monitoring. METHODS: Patients were monitored at the Danish Epilepsy Center, in an EMU specifically designed for this purpose, and they were under continuous surveillance by personnel dedicated to the EMU. Adverse events (AEs) - including injuries, were prospectively noted, as part of the safety policy of the hospital. Other data were retrospectively extracted from the electronic database, for a 5-year period (January 2012-December 2016). RESULTS: 976 patients were admitted to the EMU. Falls occurred in 19 patients (1.9%) but none of them resulted in injury. Only one serious AE occurred: a patient had a convulsive status epilepticus, which did not respond to first-line treatment in the EMU and was transferred to the intensive care unit. The rate of AEs were similar or lower than previously reported by other centers, where the mobility of the patients had been restricted during monitoring. CONCLUSION: In an EMU specially designed for this purpose, where patients are under continuous surveillance by personnel dedicated to the EMU, injuries can be avoided even when the mobility of the patients is not restricted.