Evidence map›Paper›PMID 33517166›Full record

ArticleEpilepsy research2021

DDESVSFS: A simple, rapid and comprehensive screening tool for the Differential Diagnosis of Epileptic Seizures VS Functional Seizures.

Nicholas J Janocko, Jin Jing, Ziwei Fan, Diane L Teagarden, Hannah K Villarreal, Matthew L Morton, Olivia Groover, David W Loring, Daniel L Drane, M Brandon Westover and 1 more

Abstract read
In one paragraph

Article in Epilepsy research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Nicholas J JanockoDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
Jin JingDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Ziwei FanDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Diane L TeagardenDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
Hannah K VillarrealDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
Matthew L MortonDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
Olivia GrooverDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
David W LoringDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
Daniel L DraneDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA; Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, University of Washington, Seattle, WA, USA.
M Brandon WestoverDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Ioannis KarakisDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA. Electronic address: ioannis.karakis@emory.edu.

Funding

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
NINDS NIH HHS R01 NS102190NINDS NIH HHS R01 NS107291
6 · The paper itself

Abstract

objectiveFunctional seizures (FS) are often misclassified as epileptic seizures (ES). This study aimed to create an easy to use but comprehensive screening tool to guide further evaluation of patients presenting with this diagnostic dilemma. MATERIALS AND

methodsDemographic, clinical and diagnostic data were collected on patients admitted for video-EEG monitoring for clarification of their diagnosis. Upon discharge, patients were classified as having ES vs FS. Using the collected characteristics and video-EEG diagnosis, we created a multivariable logistic regression model to identify predictors of ES. Then, we trained an integer-coefficient model with the most frequently selected predictors, creating a pointing system coined DDESVSFS, with scores ranging from -17 to +8 points.

results43 patients with FS and 165 patients with ES were recruited. In the final integer-coefficient model, 8 predictors were identified as significant in differentiating ES from FS: normal electroencephalogram (-3 points), predisposing factors for FS (-3 points), increased number of comorbidities (-3 points), semiology suggestive of FS (-4 points), increased seizure frequency (-4 points), longer disease duration (+3 points), antiepileptic polypharmacy (+2 points) and compliance with antiepileptic drugs (+3 points). Cumulative scores of ≤ -9 points carried <5% predictive value for ES, while cumulative scores of ≥ -1 points carried >95% predictive value. The model performed well (AUC: 0.923, sensitivity: 0.945, specificity: 0.698).

conclusionsWe propose DDESVSFS as a simple, rapid and comprehensive prediction score for the Differential Diagnosis of Epileptic Seizures VS Functional Seizures. Large prospective studies are needed to evaluate its utility in clinical practice.

Indexed as

EpilepsySeizuresDiagnosis, DifferentialElectroencephalographyHumansProspective StudiesEpilepsyFunctional seizuresPrediction modelPsychogenic non-epileptic seizuresScreening toolSeizures

Identifiers

PMID33517166
PMCPMC8092190

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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.