The Ictal–Interictal Continuum: To Treat or Not to Treat (and How)?

Rubiños, Clio; Reynolds, Alexandra S.; Claassen, Jan

doi:10.1007/s12028-017-0477-5

Cited by 42 publications

(27 citation statements)

References 60 publications

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“…EEG is already subject to substantial inter-rater variability, and asymmetric access to clinical information exaggerates the bias introduced by this variability [18][19][20]. Although consensus guidelines [21] have established criteria for classifying pathological, but non-ictal, EEG activity, such as rhythmic and periodic patterns, these can be difficult to distinguish from ictal activity in critically ill patients without full access to clinical information [22][23][24][25].…”

Section: Confounding Factors In Prior Researchmentioning

confidence: 99%

Diagnostic Value of Electroencephalography with Ten Electrodes in Critically Ill Patients

et al. 2020

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Background: In critical care settings, electroencephalography (EEG) with reduced number of electrodes (reduced montage EEG, rm-EEG) might be a timely alternative to the conventional full montage EEG (fm-EEG). However, past studies have reported variable accuracies for detecting seizures using rm-EEG. We hypothesized that the past studies did not distinguish between differences in sensitivity from differences in classification of EEG patterns by different readers. The goal of the present study was to revisit the diagnostic value of rm-EEG when confounding issues are accounted for. Methods: We retrospectively collected 212 adult EEGs recorded at Massachusetts General Hospital and reviewed by two epileptologists with access to clinical, trending, and video information. In Phase I of the study, we re-configured the first 4 h of the EEGs in lateral circumferential montage with ten electrodes and asked new readers to interpret the EEGs without access to any other ancillary information. We compared their rating to the reading of hospital clinicians with access to ancillary information. In Phase II, we measured the accuracy of the same raters reading representative samples of the discordant EEGs in full and reduced configurations presented randomly by comparing their performance to majority consensus as the gold standard. Results: Of the 95 EEGs without seizures in the selected fm-EEG, readers of rm-EEG identified 92 cases (97%) as having no seizure activity. Of 117 EEGs with "seizures" identified in the selected fm-EEG, none of the cases was labeled as normal on rm-EEG. Readers of rm-EEG reported pathological activity in 100% of cases, but labeled them as seizures (N = 77), rhythmic or periodic patterns (N = 24), epileptiform spikes (N = 7), or burst suppression (N = 6). When the same raters read representative epochs of the discordant EEG cases (N = 43) in both fm-EEG and rm-EEG configurations, we found high concordance (95%) and intra-rater agreement (93%) between fm-EEG and rm-EEG diagnoses. Conclusions: Reduced EEG with ten electrodes in circumferential configuration preserves key features of the traditional EEG system. Discrepancies between rm-EEG and fm-EEG as reported in some of the past studies can be in part due to methodological factors such as choice of gold standard diagnosis, asymmetric access to ancillary clinical information, and inter-rater variability rather than detection failure of rm-EEG as a result of electrode reduction per se.

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Section: Confounding Factors In Prior Researchmentioning

confidence: 99%

Diagnostic Value of Electroencephalography with Ten Electrodes in Critically Ill Patients

et al. 2020

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“…First, these patterns exist on a spectrum of pathologic activity along with seizures-the socalled ictal-interictal continuum (IIC)-and at times, they can be difficult to distinguish from seizures, especially absent ancillary clinical information [26][27][28]. Second, these patterns might benefit from treatment with antiseizure medications, and measuring the performance of the algorithm in these cases would have clinical significance [26,[29][30][31]. See Fig.…”

Section: Reference Standard Defined By Expert Neurologists' Review Ofmentioning

confidence: 99%

Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method

Kamousi¹,

Karunakaran²,

Gururangan

et al. 2020

Neurocrit Care

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Introduction Current electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients’ clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures. We aimed to test the performance of a recently FDA-cleared machine learning method (Claritγ, Ceribell Inc.) that measures the burden of seizure activity in real time and generates bedside alerts for possible status epilepticus (SE). Methods We retrospectively identified adult patients (n = 353) who underwent evaluation of possible seizures with Rapid Response EEG system (Rapid-EEG, Ceribell Inc.). Automated detection of seizure activity and seizure burden throughout a recording (calculated as the percentage of ten-second epochs with seizure activity in any 5-min EEG segment) was performed with Claritγ, and various thresholds of seizure burden were tested (≥ 10% indicating ≥ 30 s of seizure activity in the last 5 min, ≥ 50% indicating ≥ 2.5 min of seizure activity, and ≥ 90% indicating ≥ 4.5 min of seizure activity and triggering a SE alert). The sensitivity and specificity of Claritγ’s real-time seizure burden measurements and SE alerts were compared to the majority consensus of at least two expert neurologists. Results Majority consensus of neurologists labeled the 353 EEGs as normal or slow activity (n = 249), highly epileptiform patterns (HEP, n = 87), or seizures [n = 17, nine longer than 5 min (e.g., SE), and eight shorter than 5 min]. The algorithm generated a SE alert (≥ 90% seizure burden) with 100% sensitivity and 93% specificity. The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82% for ≥ 50% seizure burden and 88% and 60% for ≥ 10% seizure burden. Of the 179 EEG recordings in which the algorithm detected no seizures, seizures were identified by the expert reviewers in only two cases, indicating a negative predictive value of 99%. Discussion Claritγ detected SE events with high sensitivity and specificity, and it demonstrated a high negative predictive value for distinguishing nonepileptiform activity from seizure and highly epileptiform activity. Conclusions Ruling out seizures accurately in a large proportion of cases can help prevent unnecessary or aggressive over-treatment in critical care settings, where empiric treatment with antiseizure medications is currently prevalent. Claritγ’s high sensitivity for SE and high negative predictive value for cases without epileptiform activity make it a useful tool for triaging treatment and the need for urgent neurological consultation.

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“…49 Up to 29% of patients undergoing cEEG in the ICU were found to have PDs with a variety of underlying pathologies. 4,18,50 PDs are associated with seizures, worse functional outcome, and higher mortality. [51][52][53] PDs may also have similar metabolic consequences to seizures-such as increased regional glucose metabolism, elevated lactate/pyruvate ratio, and tissue hypoxia.…”

Section: Seizures and Periodic Discharges On Eegmentioning

confidence: 99%

Electroencephalogram Monitoring in Critical Care

2020

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Seizures are common in critically ill patients. Electroencephalogram (EEG) is a tool that enables clinicians to provide continuous brain monitoring and to guide treatment decisions—brain telemetry. EEG monitoring has particular utility in the intensive care unit as most seizures in this setting are nonconvulsive. Despite the increased use of EEG monitoring in the critical care unit, it remains underutilized. In this review, we summarize the utility of EEG and different EEG modalities to monitor patients in the critical care setting.

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The Ictal–Interictal Continuum: To Treat or Not to Treat (and How)?

Cited by 42 publications

References 60 publications

Diagnostic Value of Electroencephalography with Ten Electrodes in Critically Ill Patients

Diagnostic Value of Electroencephalography with Ten Electrodes in Critically Ill Patients

Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method

Electroencephalogram Monitoring in Critical Care

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