Clinical features and genotype-phenotype correlations in epilepsy patients with de novo DYNC1H1 variants.

OBJECTIVE: DYNC1H1 variants are involved on a disease spectrum from neuromuscular disorders to neurodevelopmental disorders. DYNC1H1-related epilepsy has been reported in small cohorts. We dissect the electroclinical features of 34 patients harboring de novo DYNC1H1 pathogenic variants, identify subphenotypes on the DYNC1H1-related epilepsy spectrum, and compare the genotype-phenotype correlations observed in our cohort with the literature. METHODS: Patients harboring de novo DYNC1H1 pathogenic variants were recruited through international collaborations. Clinical data were retrospectively collected. Latent class analysis was performed to identify subphenotypes. Multivariable binary logistic regression analysis was applied to investigate the association with DYNC1H1 protein domains. RESULTS: DYNC1H1-related epilepsy presented with infantile epileptic spasms syndrome (IESS) in 17 subjects (50%), and in 25% of these individuals the epileptic phenotype evolved into Lennox-Gastaut syndrome (LGS). In 12 patients (35%), focal onset epilepsy was defined. In two patients, the epileptic phenotype consisted of generalized myoclonic epilepsy, with a progressive phenotype in one individual harboring a frameshift variant. In approximately 60% of our cohort, seizures were drug-resistant. Malformations of cortical development were noticed in 79% of our patients, mostly on the lissencephaly-pachygyria spectrum, particularly with posterior predominance in a half of them. Midline and infratentorial abnormalities were additionally reported in 45% and 27% of subjects. We have identified three main classes of subphenotypes on the DYNC1H1-related epilepsy spectrum. SIGNIFICANCE: We propose a classification in which pathogenic de novo DYNC1H1 variants feature drug-resistant IESS in half of cases with potential evolution to LGS (Class 1), developmental and epileptic encephalopathy other than IESS and LGS (Class 2), or less severe focal or genetic generalized epilepsy including a progressive phenotype (Class 3). We observed an association between stalk domain variants and Class 1 phenotypes. The variants p.Arg309His and p.Arg1962His were common and associated with Class 1 subphenotype in our cohort. These findings may aid genetic counseling of patients with DYNC1H1-related epilepsy.

Optimizing EEG monitoring in critically ill children at risk for electroencephalographic seizures.

OBJECTIVE: Strategies are needed to optimally deploy continuous EEG monitoring (CEEG) for electroencephalographic seizure (ES) identification and management due to resource limitations. We aimed to construct an efficient multi-stage prediction model guiding CEEG utilization to identify ES in critically ill children using clinical and EEG covariates. METHODS: The largest prospective single-center cohort of 1399 consecutive children undergoing CEEG was analyzed. A four-stage model was developed and trained to predict whether a subject required additional CEEG at the conclusion of each stage given their risk of ES. Logistic regression, elastic net, random forest, and CatBoost served as candidate methods for each stage and were evaluated using cross validation. An optimal multi-stage model consisting of the top-performing stage-specific models was constructed. RESULTS: When evaluated on a test set, the optimal multi-stage model achieved a cumulative specificity of 0.197 and cumulative F1 score of 0.326 while maintaining a high minimum cumulative sensitivity of 0.938. Overall, 11 % of test subjects with ES were removed from the model due to a predicted low risk of ES (falsely negative subjects). CEEG utilization would be reduced by 32 % and 47 % compared to performing 24 and 48 h of CEEG in all test subjects, respectively. We developed a web application called EEGLE (EEG Length Estimator) that enables straightforward implementation of the model. CONCLUSIONS: Application of the optimal multi-stage ES prediction model could either reduce CEEG utilization for patients at lower risk of ES or promote CEEG resource reallocation to patients at higher risk for ES.

Electrographic Seizure Characteristics and Electrographic Status Epilepticus Prediction.

PURPOSE: We aimed to characterize electrographic seizures (ES) and electrographic status epilepticus (ESE) and determine whether a model predicting ESE exclusively could effectively guide continuous EEG monitoring (CEEG) utilization in critically ill children. METHODS: This was a prospective observational study of consecutive critically ill children with encephalopathy who underwent CEEG. We used descriptive statistics to characterize ES and ESE, and we developed a model for ESE prediction. RESULTS: ES occurred in 25% of 1,399 subjects. Among subjects with ES, 23% had ESE, including 37% with continuous seizures lasting >30 minutes and 63% with recurrent seizures totaling 30 minutes within a 1-hour epoch. The median onset of ES and ESE occurred 1.8 and 0.18 hours after CEEG initiation, respectively. The optimal model for ESE prediction yielded an area under the receiver operating characteristic curves of 0.81. A cutoff selected to emphasize sensitivity (91%) yielded specificity of 56%. Given the 6% ESE incidence, positive predictive value was 11% and negative predictive value was 99%. If the model were applied to our cohort, then 53% of patients would not undergo CEEG and 8% of patients experiencing ESE would not be identified. CONCLUSIONS: ESE was common, but most patients with ESE had recurrent brief seizures rather than long individual seizures. A model predicting ESE might only slightly improve CEEG utilization over models aiming to identify patients at risk for ES but would fail to identify some patients with ESE. Models identifying ES might be more advantageous for preventing ES from evolving into ESE.

Burr Hole Hemispherotomy-Modification of Trans-Sylvian Peri-Insular Technique: 2-Dimensional Operative Video.

Trans-sylvian peri-insular hemispherotomy represents a functional hemispherectomy with minimal brain removal used for treatment of refractory hemispheric epilepsy.1 Exposure for this procedure is achieved by craniotomy. Refinement in the hemispherotomy technique, including trends toward minimizing cortical resection, has contributed to a substantial drop in complication rates.2 We present a refinement of this technique, allowing for complete hemispheric disconnection through a single burr hole. In this instance, this technique was applied in the case of a 4-year-old girl who presented with medically refractory epilepsy, which had developed on the first day of life due to a perinatal incomplete left middle cerebral artery stroke. Postoperatively, the patient experienced no worsening of her preexisting right-sided hemiparesis and remains seizure-free 18 months postoperatively, now off medication. While the trans-sylvian peri-insular hemispherotomy represents an established surgical technique, this is the first report of this procedure performed in a minimally invasive fashion through a single burr hole. Beyond the minimal incision and small aperture in the skull, seldom appreciated nuances of hemispheric disconnection are described and demonstrated, including amygdala disconnection, hippocampal tail disconnection directly into splenium disconnection, concomitant intermediate disconnection and callosotomy, and frontobasal disconnection landmarks. Consent was obtained from the patient's parents for the surgical procedure, use of outcome videos, and for publication of this video and associated materials. The participants and patient's parents consented to publication of their images and that of the patient.

Late-Onset Findings During Extended EEG Monitoring Are Rare in Critically Ill Children.

PURPOSE: Electrographic seizures (ES) are common in critically ill children undergoing continuous EEG (CEEG) monitoring, and previous studies have aimed to target limited CEEG resources to children at highest risk of ES. However, previous studies have relied on observational data in which the duration of CEEG was clinically determined. Thus, the incidence of late occurring ES is unknown. The authors aimed to assess the incidence of ES for 24 hours after discontinuation of clinically indicated CEEG. METHODS: This was a single-center prospective study of nonconsecutive children with acute encephalopathy in the pediatric intensive care unit who underwent 24 hours of extended research EEG after the end of clinical CEEG. The authors assessed whether there were new findings that affected clinical management during the extended research EEG, including new-onset ES. RESULTS: Sixty-three subjects underwent extended research EEG. The median duration of the extended research EEG was 24.3 hours (interquartile range 24.0-25.3). Three subjects (5%) had an EEG change during the extended research EEG that resulted in a change in clinical management, including an increase in ES frequency, differential diagnosis of an event, and new interictal epileptiform discharges. No subjects had new-onset ES during the extended research EEG. CONCLUSIONS: No subjects experienced new-onset ES during the 24-hour extended research EEG period. This finding supports observational data that patients with late-onset ES are rare and suggests that ES prediction models derived from observational data are likely not substantially underrepresenting the incidence of late-onset ES after discontinuation of clinically indicated CEEG.

Diagnostic Yield of CSF Testing in Infants for Disorders of Biogenic Amine Neurotransmitter Metabolism.

BACKGROUND AND OBJECTIVES: Biochemical testing of CSF for neurotransmitter metabolites and their cofactors is often used in the diagnostic evaluation of infants with neurologic disorders but requires an invasive, labor-intensive procedure with many potential sources of error. Our aim was to determine the diagnostic yield of CSF testing for biogenic amines (serotonin, norepinephrine, epinephrine, and dopamine) and their cofactors in identifying inborn errors of neurotransmitter metabolism among infants. METHODS: We evaluated all infants aged 1 year or younger who underwent CSF biogenic amine neurotransmitter (CSFNT) testing at Children's Hospital of Philadelphia (CHOP) and Boston Children's Hospital (BCH) between 2008 and 2017 in this cross-sectional study. The primary outcome was the proportion of individuals who received a diagnostic result from CSFNT testing. Secondary assessments included the proportion of infants who obtained a diagnostic result from other types of diagnostic testing. RESULTS: The cohort included 323 individuals (191 from CHOP and 232 from BCH). The median age at presentation was 110 days (range 36-193). The most common presenting features were seizures (71%), hypotonia (47%), and developmental delay (43%). The diagnostic yield of CSFNT testing was zero. When CSF pyridoxal-5-phosphate level was assayed with CSFNT testing, 1 patient had a diagnostic result. An etiologic diagnosis was identified in 163 patients (50%) of the cohort, with genetic testing having the highest yield (120 individuals, 37%). DISCUSSION: Our findings support the case for deimplementation of CSFNT testing as a standard diagnostic test of etiology in infants aged 1 year or younger presenting with neurologic disorders.

Association of EEG Background With Diffusion-Weighted Magnetic Resonance Neuroimaging and Short-Term Outcomes After Pediatric Cardiac Arrest.

BACKGROUND AND OBJECTIVES: EEG and MRI features are independently associated with pediatric cardiac arrest (CA) outcomes, but it is unclear whether their combination improves outcome prediction. We aimed to assess the association of early EEG background category with MRI ischemia after pediatric CA and determine whether addition of MRI ischemia to EEG background features and clinical variables improves short-term outcome prediction. METHODS: This was a single-center retrospective cohort study of pediatric CA with EEG initiated ≤24 hours and MRI obtained ≤7 days of return of spontaneous circulation. Initial EEG background was categorized as normal, slow/disorganized, discontinuous/burst-suppression, or attenuated-featureless. MRI ischemia was defined as percentage of brain tissue with apparent diffusion coefficient (ADC) <650 × 10-6 mm2/s and categorized as high (≥10%) or low (<10%). Outcomes were mortality and unfavorable neurologic outcome (Pediatric Cerebral Performance Category increase ≥1 from baseline resulting in ICU discharge score ≥3). The Kruskal-Wallis test evaluated the association of EEG with MRI. Area under the receiver operating characteristic (AUROC) curve evaluated predictive accuracy. Logistic regression and likelihood ratio tests assessed multivariable outcome prediction. RESULTS: We evaluated 90 individuals. EEG background was normal in 16 (18%), slow/disorganized in 42 (47%), discontinuous/burst-suppressed in 12 (13%), and attenuated-featureless in 20 (22%) individuals. The median percentage of MRI ischemia was 5% (interquartile range 1-18); 32 (36%) individuals had high MRI ischemia burden. Twenty-eight (31%) individuals died, and 58 (64%) had unfavorable neurologic outcome. Worse EEG background category was associated with more MRI ischemia (p < 0.001). The combination of EEG background and MRI ischemia burden had higher predictive accuracy than EEG alone (AUROC: mortality: 0.92 vs 0.87, p = 0.03) or MRI alone (AUROC: mortality: 0.92 vs 0.84, p = 0.02; unfavorable: 0.83 vs 0.73, p < 0.01). Addition of percentage of MRI ischemia to clinical variables and EEG background category improved prediction for mortality (χ2 = 19.1, p < 0.001) and unfavorable neurologic outcome (χ2 = 4.8, p = 0.03) and achieved high predictive accuracy (AUROC: mortality: 0.97; unfavorable: 0.92). DISCUSSION: Early EEG background category was associated with MRI ischemia after pediatric CA. Combining EEG and MRI data yielded higher outcome predictive accuracy than either modality alone. The addition of MRI ischemia to clinical variables and EEG background improved short-term outcome prediction.