Chimeric antigen receptor (CAR) T cells have emerged as a promising class of cell-based immunotherapy in refractory malignancies. Neurotoxicity represents a common and potentially life-threatening adverse effect of CAR T cells, and clinical experience is limited. Here, we describe the clinical presentation and management of 25 adult patients who presented with neurotoxic syndromes after CAR T-cell therapy at the Massachusetts General Hospital. This cohort includes 24 patients treated with CD19-directed CAR T cells for non-Hodgkin lymphoma (n = 23) and acute lymphoblastic leukemia (n = 1), and 1 patient treated with α-fetoprotein–directed CAR T cells for hepatocellular carcinoma (n = 1). Twelve of the 25 patients (48%) developed grade 1-2 neurotoxicity and 13 patients (52%) presented with grade 3-4 neurotoxicity. We found that lower platelet counts at time of CAR T-cell infusion were associated with more severe neurotoxicity (P = .030). Cytokine release syndrome occurred in 24 of 25 patients (96%). Serum levels of ferritin peaked with onset of neurologic symptoms, and higher ferritin levels were associated with higher neurotoxicity grade. Grade 3-4 neurotoxicity correlated negatively with overall survival (OS) (P = .013). Median OS of the entire cohort was 54.7 weeks. Eight patients (32%) with grade 3-4 neurotoxicity were deceased at database closure, whereas none died with neurotoxicity grade 1-2. High pretreatment lactate dehydrogenase was frequently encountered in lymphoma patients with grade 3-4 neurotoxicity and correlated negatively with progression-free survival (P = .048). We did not find evidence that steroid use ≥7 days altered the patient’s outcome when compared with <7 days of steroids. Management of CAR T cell–mediated neurotoxicity warrants evaluation in prospective clinical trials.

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discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability.OBJECTIVE To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs.DESIGN, SETTING, AND PARTICIPANTS A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built.MAIN OUTCOMES AND MEASURES SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation.RESULTS SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). CONCLUSIONS AND RELEVANCEIn this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.

The QOL in patients with MS is not solely determined by physical disability, but rather by the level of social support, living area, depression, level of education, employment, fatigue and religiosity. Accordingly, we suggest that these should be evaluated in every patient with MS as they may be modified by targeted interventions.

The validity of using electroencephalograms (EEGs) to diagnose epilepsy requires reliable detection of interictal epileptiform discharges (IEDs). Prior interrater reliability (IRR) studies are limited by small samples and selection bias.OBJECTIVE To assess the reliability of experts in detecting IEDs in routine EEGs. DESIGN, SETTING, AND PARTICIPANTSThis prospective analysis conducted in 2 phases included as participants physicians with at least 1 year of subspecialty training in clinical neurophysiology. In phase 1, 9 experts independently identified candidate IEDs in 991 EEGs (1 expert per EEG) reported in the medical record to contain at least 1 IED, yielding 87 636 candidate IEDs. In phase 2, the candidate IEDs were clustered into groups with distinct morphological features, yielding 12 602 clusters, and a representative candidate IED was selected from each cluster. We added 660 waveforms (11 random samples each from 60 randomly selected EEGs reported as being free of IEDs) as negative controls. Eight experts independently scored all 13 262 candidates as IEDs or non-IEDs. The 1051 EEGs in the study were recorded at the Massachusetts General Hospital between 2012 and 2016.MAIN OUTCOMES AND MEASURES Primary outcome measures were percentage of agreement (PA) and beyond-chance agreement (Gwet κ) for individual IEDs (IED-wise IRR) and for whether an EEG contained any IEDs (EEG-wise IRR). Secondary outcomes were the correlations between numbers of IEDs marked by experts across cases, calibration of expert scoring to group consensus, and receiver operating characteristic analysis of how well multivariate logistic regression models may account for differences in the IED scoring behavior between experts. RESULTS Among the 1051 EEGs assessed in the study, 540 (51.4%) were those of females and 511 (48.6%) were those of males. In phase 1, 9 experts each marked potential IEDs in a median of 65 (interquartile range [IQR], 28-332) EEGs. The total number of IED candidates marked was 87 636. Expert IRR for the 13 262 individually annotated IED candidates was fair, with the mean PA being 72.4% (95% CI, 67.0%-77.8%) and mean κ being 48.7% (95% CI, 37.3%-60.1%). The EEG-wise IRR was substantial, with the mean PA being 80.9% (95% CI, 76.2%-85.7%) and mean κ being 69.4% (95% CI, 60.3%-78.5%). A statistical model based on waveform morphological features, when provided with individualized thresholds, explained the median binary scores of all experts with a high degree of accuracy of 80% (range, 73%-88%).CONCLUSIONS AND RELEVANCE This study's findings suggest that experts can identify whether EEGs contain IEDs with substantial reliability. Lower reliability regarding individual IEDs may be largely explained by various experts applying different thresholds to a common underlying statistical model.