New-onset refractory status epilepticus (NORSE) is “a clinical presentation, not a specific diagnosis, in a patient without active epilepsy or other preexisting relevant neurological disorder, with new onset of refractory status epilepticus without a clear acute or active structural, toxic, or metabolic cause.” Febrile infection related epilepsy syndrome (FIRES) is “a subcategory of NORSE that requires a prior febrile infection, with fever starting between 2 weeks and 24 h before the onset of refractory status epilepticus, with or without fever at the onset of status epilepticus.” These apply to all ages. Extensive testing of blood and CSF for infectious, rheumatologic, and metabolic conditions, neuroimaging, EEG, autoimmune/paraneoplastic antibody evaluations, malignancy screen, genetic testing, and CSF metagenomics may reveal the etiology in some patients, while a significant proportion of patients’ disease remains unexplained, known as NORSE of unknown etiology or cryptogenic NORSE. Seizures are refractory and usually super-refractory (i.e., persist despite 24 h of anesthesia), requiring a prolonged intensive care unit stay, often (but not always) with fair to poor outcomes. Management of seizures in the initial 24–48 h should be like any case of refractory status epilepticus. However, based on the published consensus recommendations, the first-line immunotherapy should begin within 72 h using steroids, intravenous immunoglobulins, or plasmapheresis. If there is no improvement, the ketogenic diet and second-line immunotherapy should start within seven days. Rituximab is recommended as the second-line treatment if there is a strong suggestion or proof of an antibody-mediated disease, while anakinra or tocilizumab are recommended for cryptogenic cases. Intensive motor and cognitive rehab are usually necessary after a prolonged hospital stay. Many patients will have pharmacoresistant epilepsy at discharge, and some may need continued immunologic treatments and an epilepsy surgery evaluation. Extensive research is in progress now via multinational consortia relating to the specific type(s) of inflammation involved, whether age and prior febrile illness affect this, and whether measuring and following serum and/or CSF cytokines can help determine the best treatment.
Background and Objectives:The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as “ictal-interictal-injury continuum" (IIIC). Prior inter-rater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC.Methods:This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as: “Seizure (SZ)”, “Lateralized Periodic Discharges (LPD)”, “Generalized Periodic Discharges (GPD)”, “Lateralized Rhythmic Delta Activity (LRDA)”, “Generalized Rhythmic Delta Activity (GRDA)”, or “Other”. EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 to 2020. Primary outcome measures were pairwise IRR (average percent agreement (PA) between pairs of experts) and majority IRR (average PA with group consensus) for each class; and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability.Results:Among 2,711 EEGs, 49% were from females, and median (IQR) age was 55 (41). In total experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false positive vs true positive characteristics (median [range] of variance explained (R2): 95 [93, 98]%), and for most variation in experts’ precision vs sensitivity characteristics (R2: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise, but rather to variation in decision thresholds.Discussion:Our results provide precise estimates of expert reliability from a large and diverse sample, and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts.Classification of Evidence:This study provides Class II evidence that independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared to expert consensus.
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