Evidence map›Paper›PMID 42088913›Full record

SynthesisFrontiers in neurology

A systematic review of non-invasive biomarkers for seizure forecasting in pediatric epilepsy patients.

Christine Esparza, Megan Thomas Hebdon, Jun Wang, Chumeng Wang, Rania Agrawal, Grace Do, Avery Bodden, Galilea Dupree, Jacqueline Rajotte, Gabriella Gonzalez Ciofuli and 3 more

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

13 authors.

Christine EsparzaBaylor College of Medicine, Houston, TX, United States.
Megan Thomas HebdonUniversity of Texas School of Nursing, Austin, TX, United States.
Jun WangDepartment of Speech, Language, and Hearing Sciences, University of Texas, Austin, TX, United States.
Chumeng WangDepartment of Neurology, University of Texas at Austin Dell Medical School, Austin, TX, United States.
Rania AgrawalDepartment of Neurology, University of Texas at Austin Dell Medical School, Austin, TX, United States.
Grace DoDepartment of Neurology, University of Texas at Austin Dell Medical School, Austin, TX, United States.
Avery BoddenDepartment of Neurology, University of Texas at Austin Dell Medical School, Austin, TX, United States.
Galilea DupreeUniversity of Texas School of Nursing, Austin, TX, United States.
Jacqueline RajotteBarry University, St. Petersburg, FL, United States.
Gabriella Gonzalez CiofuliUniversity of Texas, Austin, TX, United States.
Halena RiosUniversity of Texas, Austin, TX, United States.
Yingchao YuanDepartment of Neurology, University of Texas at Austin Dell Medical School, Austin, TX, United States.
Clifford CalleyDepartment of Neurology, University of Texas at Austin Dell Medical School, Austin, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Epilepsy is one of the most common neurological disorders globally. While medications, surgical interventions, and dietary changes can be successful in controlling seizures, a subset of individuals experience refractory epilepsy and are at increased risk for sudden unexpected death in epilepsy (SUDEP). Efforts to provide a detection system using devices have been successful at identifying seizures once they start, but there are no devices or systems on the market that predict seizures. The purpose of this systematic review (PROSPERO ID: CRD42024444250) is to determine non-invasive physiologic and environmental biomarkers that can be used to forecast seizures in pediatric epilepsy patients. Methods: A systematic search of relevant literature was conducted in PubMed, Web of Science, CINAHL Ultimate, and EMBASE in August 2023. Articles were reviewed by two investigators in a two-step process. Data extraction occurred using two independent extractors to identify study characteristics, patient characteristics, and forecasting results. Evidence quality was evaluated by two investigators using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool. Results: Eleven observational cohort studies were included and cardiovascular biomarkers using electrocardiogram (ECG) measurement were most commonly used. Pre-ictal anticipation algorithm times ranged from 21.8 s to 32 min, while correlational studies observed cardiovascular biomarker changes 3.59 s to 40 min before seizures. This systematic review provides a comprehensive overview of the current evidence for seizure forecasting. However, the evidence in 9/11 of reviewed studies were rated as either low or very low certainty using the GRADE tool due to methodological flaws, risk of bias, inconsistent results, and indirect or sparse evidence. Discussion: There are ongoing opportunities to build on our findings, including further testing of cardiovascular biomarkers with other physiologic and environmental factors, larger sample size studies, and a precision medicine approach to tailoring algorithms and biomarker measurements to individual patients. Systematic review registration: PROSPERO (ID: CRD42024444250).

Indexed as

algorithmsbiomarkersepilepsyseizure forecastingwearable electronic devices

Identifiers

PMID42088913
PMCPMC13135939

What OpenQuestion holds

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Registered trials

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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.