The INTERSTROKE is an international case-control study of risk factors of first acute stroke, conducted in 32 countries. Cases were patients with CT- or MRI-confirmed incident acute hospitalized stroke, and controls were matched for age, sex, and within sites. Standardized questions asked about self-reported depressive symptoms during the previous 12 months and the use of prescribed antidepressant medications were recorded. Multivariable conditional logistic regression was used to determine the association of prestroke depressive symptoms with acute stroke risk. Adjusted ordinal logistic regression was used to explore the association of prestroke depressive symptoms with poststroke functional outcome, measured with the modified Rankin scale at 1 month after stroke.

Of 26,877 participants, 40.4% were women, and the mean age was 61.7 ± 13.4 years. The prevalence of depressive symptoms within the last 12 months was higher in cases compared with that in controls (18.3% vs 14.1%, p < 0.001) and differed by region (p interaction <0.001), with lowest prevalence in China (6.9% in controls) and highest in South America (32.2% of controls). In multivariable analyses, prestroke depressive symptoms were associated with greater odds of acute stroke (odds ratio [OR] 1.46, 95% CI 1.34–1.58), which was significant for both intracerebral hemorrhage (OR 1.56, 95% CI 1.28–1.91) and ischemic stroke (OR 1.44, 95% CI 1.31–1.58). A larger magnitude of association with stroke was seen in patients with a greater burden of depressive symptoms. While preadmission depressive symptoms were not associated with a greater odds of worse baseline stroke severity (OR 1.02, 95% CI 0.94–1.10), they were associated with a greater odds of poor functional outcome at 1 month after acute stroke (OR 1.09, 95% CI 1.01–1.19).

In this global study, we recorded that depressive symptoms are an important risk factor of acute stroke, including both ischemic and hemorrhagic stroke. Preadmission depressive symptoms were associated with poorer functional outcome, but not baseline stroke severity, suggesting an adverse role of depressive symptoms in poststroke recovery.

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 (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 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 (). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability.

Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. 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%, 42%), and majority IRR was substantial (PA 65%, 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 ( ): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ( : 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds.

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.

This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.

We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes.

SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts—ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively.

SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs.

This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

From 2015 to 2020, all consecutive adult ICU patients at a Swiss tertiary care center with isolated seizures or SE as reported by intensivists and/or consulting neurologists were identified by screening of all digital medical, ICU, and EEG records. Patients aged <18 years and patients with myoclonus due to hypoxic-ischemic encephalopathy but without seizures on EEG were excluded. The frequency of isolated seizures, SE, and clinical characteristics at seizure onset associated with SE were the primary outcomes. Uni- and multivariable logistic regression was performed to identify associations with the emergence of SE.

Among 404 patients with seizures, 51% had SE. Compared with patients with isolated seizures, patients with SE had a lower median Charlson Comorbidity Index (CCI) (3 vs 5, p < 0.001), fewer fatal etiologies (43.6% vs 80.5%, p < 0.001), higher median Glasgow coma scores (7 vs 5, p < 0.001), fever more frequently (27.5% vs 7.5%, p < 0.001), shorter median ICU and hospital stay (ICU: 4 vs 5 days, p = 0.039; hospital stay: 13 vs 15 days, p = 0.045), and recovered to premorbid function more often (36.8% vs 17%, p < 0.001). Multivariable analyses revealed decreased odds ratios (ORs) for SE with increasing CCI (OR 0.91, 95% CI 0.83–0.99), fatal etiology (OR 0.15, 95% CI 0.08–0.29), and epilepsy (OR 0.32, 95% CI 0.16–0.63). Systemic inflammation was an additional association with SE after excluding patients with seizures as the reason for ICU admission (OR_{for CRP} 1.01, 95% CI 1.00–1.01; OR_{for fever} 7.35, 95% CI 2.84–19.0). Although fatal etiologies and increasing CCI remained associated with low odds for SE after excluding anesthetized patients and hypoxic-ischemic encephalopathy, inflammation remained associated in all subgroups except patients with epilepsy.

Among all ICU patients with seizures, SE emerged frequently and seen in every second patient. Besides the unexpected low odds for SE with higher CCI, fatal etiology, and epilepsy, the association of inflammation with SE in the critically ill without epilepsy represents a potential treatment target and deserves further attention.

This was a retrospective study of nationwide Medicare claims from 2017 to 2019. We included patients older than 65 years hospitalized for the following broad categories based on diagnosis-related groups (DRGs): multiple sclerosis and cerebellar ataxia (DRG 058–060); stroke (061–072); degenerative nervous system disorders (056–057); epilepsy (100–101); traumatic coma (082–087), and nontraumatic coma (080–081). The exposure of interest was neighborhood SES, measured by the area deprivation index (ADI), which uses socioeconomic indicators, such as educational attainment, unemployment, infrastructure access, and income, to estimate area-level socioeconomic deprivation at the level of census block groups. Patients were grouped into high, middle, and low neighborhood-level SES based on ADI percentiles. Adjustment covariates included age, comorbidity burden, race/ethnicity, individual SES, and sex.

After exclusions, 905,784 patients were included in the mortality analysis and 915,993 were included in the readmission analysis. After adjustment for age, sex, race/ethnicity, comorbidity burden, and individual SES, patients from low SES neighborhoods had higher 30-day mortality rates compared with patients from high SES neighborhoods for all disease categories except for multiple sclerosis: magnitudes of the effect ranged from an adjusted odds ratio of 2.46 (95% CI 1.60–3.78) for the nontraumatic coma group to 1.23 (95% CI 1.19–1.28) for the stroke group. After adjustment, no significant differences in readmission rates were observed for any of the groups.

Neighborhood SES is strongly associated with 30-day mortality for many common neurologic conditions even after accounting for baseline comorbidity burden and individual SES. Strategies to improve health equity should explicitly consider the effect of neighborhood environments on health outcomes.

Participants with an RMC had 2 visits: during menstruation on menstrual cycle day 2 ± 2 and in the periovulatory period on day 13 ± 2. Participants with COC were examined at day 4 ± 2 of the hormone-free interval (HFI) and between days 7 and 14 of hormone intake (HI). Postmenopausal participants were assessed once at a random time point. Plasma and tear fluid samples were collected at each visit for determination of CGRP levels with an ELISA.

A total of 180 female participants (n = 30 per group) completed the study. Participants with migraine and an RMC showed statistically significantly higher CGRP concentrations in plasma and tear fluid during menstruation compared with female participants without migraine (plasma: 5.95 pg/mL [IQR 4.37–10.44] vs 4.61 pg/mL [IQR 2.83–6.92], p = 0.020 [Mann-Whitney U test]; tear fluid: 1.20 ng/mL [IQR 0.36–2.52] vs 0.4 ng/mL [IQR 0.14–1.22], p = 0.005 [Mann-Whitney U test]). In contrast, female participants with COC and in the postmenopause had similar CGRP levels in the migraine and the control groups. In migraine participants with an RMC, tear fluid but not plasma CGRP concentrations during menstruation were statistically significantly higher compared with migraine participants under COC (p = 0.015 vs HFI and p = 0.029 vs HI, Mann-Whitney U test).

Different sex hormone profiles may influence CGRP concentrations in people, with current or past capacity to menstruate, with migraine. Measurement of CGRP in tear fluid was feasible and warrants further investigation.

We enrolled carriers of a pathologic ATXN1 or ATXN3 expansion and controls from 18 US and 2 European ataxia referral centers. Clinical, cognitive, quantitative motor, neuropsychological measures and plasma neurofilament light chain (NfL) measurements were compared between expansion carriers with and without ataxia and controls.

We enrolled 200 participants: 45 carriers of a pathologic ATXN1 expansion (31 patients with ataxia [median Scale for the Assessment and Rating of Ataxia: 9; 7–10] and 14 expansion carriers without ataxia [1; 0–2]) and 116 carriers of a pathologic ATXN3 expansion (80 patients with ataxia [7; 6–9] and 36 expansion carriers without ataxia [1; 0–2]). In addition, we enrolled 39 controls who did not carry a pathologic expansion in ATXN1 or ATXN3. Plasma NfL levels were significantly higher in expansion carriers without ataxia than controls, despite similar mean age (controls: 5.7 pg/mL, SCA1: 18.0 pg/mL [p < 0.0001], SCA3: 19.8 pg/mL [p < 0.0001]). Expansion carriers without ataxia differed from controls by significantly more upper motor signs (SCA1 p = 0.0003, SCA3 p = 0.003) and by the presence of sensor impairment and diplopia in SCA3 (p = 0.0448 and 0.0445, respectively). Functional scales, fatigue and depression scores, swallowing difficulties, and cognitive impairment were worse in expansion carriers with ataxia than those without ataxia. Ataxic SCA3 participants showed extrapyramidal signs, urinary dysfunction, and lower motor neuron signs significantly more often than expansion carriers without ataxia.

READISCA showed the feasibility of harmonized data acquisition in a multinational network. NfL alterations, early sensory ataxia, and corticospinal signs were quantifiable between preataxic participants and controls. Patients with ataxia differed in many parameters from controls and expansion carriers without ataxia, with a graded increase of abnormal measures from control to preataxic to ataxic cohorts.

ClinicalTrials.gov NCT03487367.