Evidence map›Paper›PMID 32268366›Full record

ArticleBrain : a journal of neurology2020

Electrographic predictors of successful weaning from anaesthetics in refractory status epilepticus.

Daniel B Rubin, Brigid Angelini, Maryum Shoukat, Catherine J Chu, Sahar F Zafar, M Brandon Westover, Sydney S Cash, Eric S Rosenthal

Open access · bronzeAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 28 citations in OpenAlex.

  1. Resting-state EEG for continuous prognostic monitoring and prediction of coma recovery after acute brain injury.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2026
    Article
  2. Review
  3. Article
  4. Review
  5. Status epilepticus in the ICU.Intensive care medicine · 2024
    Review
  6. Drug-resistant seizures associated with hyperinflammatory monocytes in FIRES.Annals of clinical and translational neurology · 2023
    Article
  7. Review
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Daniel B RubinDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Brigid AngeliniDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Maryum ShoukatDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Catherine J ChuDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Sahar F ZafarDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
M Brandon WestoverDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Sydney S CashDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Eric S RosenthalDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Harvard University · USBrigham and Women's Hospital · US

Funding

Neuroscience Resident Research ProgramR25NS065743 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI CASH, SYDNEY S, WILLIAMS, ZIV · 2009 to 2023
$11.8M
Neurophysiology of Human Cortical EpilepsyR01NS062092 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI CASH, SYDNEY S · 2010 to 2021
$4.7M
Multimodal Network Connectivity Architecture (MOCA) of the Brain and its Role in the Recovery of Consciousness in Comatose Cardiac Arrest PatientsR01NS102574 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI GREER, DAVID MATTHEW, WU, ONA · 2018 to 2022
$3.4M
Investigation of Sleep in the Intensive Care Unit (ICU-SLEEP)R01NS102190 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2018 to 2022
$3.2M
Big Data and Deep Learning for the Interictal-Ictal-Injury ContiuumR01NS107291 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2018 to 2022
$2.8M
Identification of Cortical Biomarkers for Seizure Risk in Childhood EpilepsyK23NS092923 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI CHU, CATHERINE J · 2015 to 2020
$1.0M
Real-Time Prediction of Delayed Cerebral Ischemia after Subarachnoid HemorrhageK23NS105950 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI ROSENTHAL, ERIC S. · 2018 to 2022
$1.0M
Quantitative Monitoring and Control of Sedation and Pain in the ICU EnvironmentK23NS090900 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2014 to 2018
$988k
Seizure focus delineation using spontaneous and stimulus evoked EEG featuresK24NS088568 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI CASH, SYDNEY S · 2015 to 2019
$923k
NINDS NIH HHS K23 NS090900NINDS NIH HHS K23 NS092923NINDS NIH HHS K23 NS105950NINDS NIH HHS K24 NS088568NINDS NIH HHS R01 NS062092NINDS NIH HHS R01 NS102190NINDS NIH HHS R01 NS102574NINDS NIH HHS R01 NS107291NINDS NIH HHS R25 NS065743
6 · The paper itself

Abstract

Intravenous third-line anaesthetic agents are typically titrated in refractory status epilepticus to achieve either seizure suppression or burst suppression on continuous EEG. However, the optimum treatment paradigm is unknown and little data exist to guide the withdrawal of anaesthetics in refractory status epilepticus. Premature withdrawal of anaesthetics risks the recurrence of seizures, whereas the prolonged use of anaesthetics increases the risk of treatment-associated adverse effects. This study sought to measure the accuracy of features of EEG activity during anaesthetic weaning in refractory status epilepticus as predictors of successful weaning from intravenous anaesthetics. We prespecified a successful anaesthetic wean as the discontinuation of intravenous anaesthesia without developing recurrent status epilepticus, and a wean failure as either recurrent status epilepticus or the resumption of anaesthesia for the purpose of treating an EEG pattern concerning for incipient status epilepticus. We evaluated two types of features as predictors of successful weaning: spectral components of the EEG signal, and spatial-correlation-based measures of functional connectivity. The results of these analyses were used to train a classifier to predict wean outcome. Forty-seven consecutive anaesthetic weans (23 successes, 24 failures) were identified from a single-centre cohort of patients admitted with refractory status epilepticus from 2016 to 2019. Spectral components of the EEG revealed no significant differences between successful and unsuccessful weans. Analysis of functional connectivity measures revealed that successful anaesthetic weans were characterized by the emergence of larger, more densely connected, and more highly clustered spatial functional networks, yielding 75.5% (95% confidence interval: 73.1-77.8%) testing accuracy in a bootstrap analysis using a hold-out sample of 20% of data for testing and 74.6% (95% confidence interval 73.2-75.9%) testing accuracy in a secondary external validation cohort, with an area under the curve of 83.3%. Distinct signatures in the spatial networks of functional connectivity emerge during successful anaesthetic liberation in status epilepticus; these findings are absent in patients with anaesthetic wean failure. Identifying features that emerge during successful anaesthetic weaning may allow faster and more successful anaesthetic liberation after refractory status epilepticus.

Indexed as

ElectroencephalographyModels, NeurologicalAdultAgedAged, 80 and overAnestheticsDrug Resistant EpilepsyFemaleHumansMaleMiddle AgedStatus EpilepticusYoung AdultAnestheticsEEGfunctional connectivitystatus epilepticus

Identifiers

PMID32268366
PMCPMC7174057
OpenAlexW3014882444

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.