Evidence map›Paper›PMID 34868532›Full record

ArticleJournal of healthcare engineering2021

Children's Neurological Status Epilepticus and Poor Prognostic Factors through Electroencephalogram Image under Composite Domain Analysis Algorithm.

Runhan Zhang, Chao Gao, Junting Liu, Manting Zhao, Yongli Wu

Open access · hybridAbstract read
In one paragraph

Article in Journal of healthcare engineering, 2021. 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
0.1field-weighted citation impact, top 56% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

5 authors at 1 institution in 1 country.

Runhan ZhangDepartment of Child Healthcare, Cangzhou Central Hospital, Cangzhou 061000, Hebei, China.ORCID 0000-0003-3305-6513
Chao GaoDepartment of Child Healthcare, Cangzhou Central Hospital, Cangzhou 061000, Hebei, China.ORCID 0000-0001-6742-8931
Junting LiuDepartment of Child Healthcare, Cangzhou Central Hospital, Cangzhou 061000, Hebei, China.ORCID 0000-0002-3880-2266
Manting ZhaoDepartment of Child Healthcare, Cangzhou Central Hospital, Cangzhou 061000, Hebei, China.ORCID 0000-0002-0164-2609
Yongli WuDepartment of Child Healthcare, Cangzhou Central Hospital, Cangzhou 061000, Hebei, China.ORCID 0000-0003-3798-9871
Cangzhou Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to analyze the application of composite domain analysis algorithm for electroencephalogram (EEG) images of children with epilepsy and to investigate the risk factors related to poor prognosis. 70 children with neurological epilepsy admitted to the hospital were selected as the research objects. Besides, the EEG of the children during the intermittent and seizure phases of epilepsy were collected, so as to establish a composite domain analysis algorithm model. Then, the model was applied in EEG analysis. The clinical disease type and prognosis of children were statistically analyzed, and the risk factors that affected the prognosis of children were investigated. The results showed that the EEG signal values of the detail coefficients (d51 and d52) and the approximate coefficient (c5) during the epileptic seizure period were higher markedly than the signal values of the epileptic intermittent period; the EEG signal of the epileptic intermittent period was a transient waveform, which appeared as sharp waves or spikes. The EEG signal of epileptic seizures was continuous, with a composite waveform of sharp waves and spikes, and the change amplitude of the wavelet envelope spectrum during epileptic seizures was also higher hugely than that of intermittent epilepsy. The accurate identification rate, specificity, and sensitivity of EEG analysis with the composite domain algorithm were higher than those without the algorithm. Among the five types of epileptic seizures in children, the proportion of systemic tonic-clonic status was the largest, and the proportion of myoclonic status was equal to that of complex partial epileptic status, both of which were relatively small. The proportion of children with a better prognosis was 75.71% (53/70), which was higher than those with a poor prognosis 24.29% (17/70). Abnormal imaging examination (odds ratio (OR) = 3.823 and 95% confidence interval (CI) = 1.643-8.897); seizure duration greater than 1 hour (OR = 1.855 and 95% CI = 1.076-3.199); C-reactive protein (CRP) (OR = 5.089 and 95% CI = 1.507-17.187); and abnormal blood glucose (OR = 3.077, 95%CI = 1.640-5.773) were all independent risk factors for poor prognosis (all

Indexed as

EpilepsyStatus EpilepticusAlgorithmsChildElectroencephalographyHumansPrognosis

Identifiers

PMID34868532
PMCPMC8639250
OpenAlexW3215761405

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.