Evidence map›Paper›PMID 38769496›Full record

ArticleBMC pediatrics2024

Identification of neurological complications in childhood influenza: a random forest model.

Suyun Li, Weiqiang Xiao, Huixian Li, Dandan Hu, Kuanrong Li, Qinglian Chen, Guangming Liu, Haomei Yang, Yongling Song, Qiuyan Peng and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  6. Review
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

19 authors.

Suyun Li *Pediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Weiqiang Xiao *Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Huixian Li *Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Dandan HuPediatric Neurology Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Kuanrong LiInstitute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Qinglian ChenPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Guangming LiuPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Haomei YangPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Yongling SongPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Qiuyan PengPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Qiang WangPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Shuyao NingNeuroelectrophysiology Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, No.9 Jinsui Road, Guangzhou, 510623, China.
Yumei XiongPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Wencheng MaPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Jun ShenPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Kelu ZhengPediatric Neurology Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Yan HongPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Sida YangNeuroelectrophysiology Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, No.9 Jinsui Road, Guangzhou, 510623, China. yangsida2013@126.com.
Peiqing LiPediatric Emergency Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China. annie_129@126.com.

Funding

Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, 510623, China Pre-NSFC-2019-002
6 · The paper itself

Abstract

backgroundAmong the neurological complications of influenza in children, the most severe is acute necrotizing encephalopathy (ANE), with a high mortality rate and neurological sequelae. ANE is characterized by rapid progression to death within 1-2 days from onset. However, the knowledge about the early diagnosis of ANE is limited, which is often misdiagnosed as simple seizures/convulsions or mild acute influenza-associated encephalopathy (IAE).

objectiveTo develop and validate an early prediction model to discriminate the ANE from two common neurological complications, seizures/convulsions and mild IAE in children with influenza.

methodsThis retrospective case-control study included patients with ANE (median age 3.8 (2.3,5.4) years), seizures/convulsions alone (median age 2.6 (1.7,4.3) years), or mild IAE (median age 2.8 (1.5,6.1) years) at a tertiary pediatric medical center in China between November 2012 to January 2020. The random forest algorithm was used to screen the characteristics and construct a prediction model.

resultsOf the 433 patients, 278 (64.2%) had seizures/convulsions alone, 106 (24.5%) had mild IAE, and 49 (11.3%) had ANE. The discrimination performance of the model was satisfactory, with an accuracy above 0.80 from both model development (84.2%) and internal validation (88.2%). Seizures/convulsions were less likely to be wrongly classified (3.7%, 2/54), but mild IAE (22.7%, 5/22) was prone to be misdiagnosed as seizures/convulsions, and a small proportion (4.5%, 1/22) of them was prone to be misdiagnosed as ANE. Of the children with ANE, 22.2% (2/9) were misdiagnosed as mild IAE, and none were misdiagnosed as seizures/convulsions.

conclusionThis model can distinguish the ANE from seizures/convulsions with high accuracy and from mild IAE close to 80% accuracy, providing valuable information for the early management of children with influenza.

Indexed as

Influenza, HumanSeizuresBrain DiseasesCase-Control StudiesChildChild, PreschoolChinaDiagnosis, DifferentialFemaleHumansInfantMaleRandom ForestRetrospective StudiesAcute febrileAcute necrotizing encephalopathyChildrenComplicationsEncephalitisInfluenzaPredictive model

Identifiers

PMID38769496
PMCPMC11103977

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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.