Evidence map›Paper›PMID 38736906›Full record

ArticleJournal of asthma and allergy2024

Predictive Value of Impulse Oscillometry Combined with Fractional Expiratory Nitric Oxide Test for Asthma in Preschool Children.

Junsong Chen, Jiying Xiao, Lingyue Liu, Kamran Ali, Suling Wu

Abstract read
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Article in Journal of asthma and allergy, 2024. 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

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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

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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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5 · Who and what money

Authors and funding

5 authors.

Junsong ChenDepartment of Pulmonology, Hangzhou Children's Hospital, Hangzhou, Zhejiang, 310015, People's Republic of China.
Jiying XiaoDepartment of Pulmonology, Hangzhou Children's Hospital, Hangzhou, Zhejiang, 310015, People's Republic of China.
Lingyue LiuDepartment of Pulmonology, Hangzhou Children's Hospital, Hangzhou, Zhejiang, 310015, People's Republic of China.ORCID 0009-0007-7004-8308
Kamran AliDepartment of Oncology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, Zhejiang, 322000, People's Republic of China.ORCID 0000-0002-0304-6130
Suling WuDepartment of Pulmonology, Hangzhou Children's Hospital, Hangzhou, Zhejiang, 310015, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Prediction of asthma in preschool children is challenging and lacks objective indicators. The aim is to observe and analyze the variances between impulse oscillometry (IOS) and fractional expiratory nitric oxide (FeNO) in preschool children with wheezing, establish a joint prediction model, and explore the diagnostic value of combining IOS with FeNO in diagnosing asthma among preschool children. Patients and methods: This study enrolled children aged 3-6 years with wheezing between June 2021 and June 2022. They were categorized as asthmatic (n=104) or non-asthmatic (n=109) after a 1-year follow-up. Clinical data, along with IOS and FeNO measurements from both groups, underwent univariate regression and multiple regression analyses to identify predictive factors and develop the most accurate model. The prediction model was built using the stepwise (stepAIC) method. The receiver operating characteristic curve (ROC), calibration curve, Hosmer-Lemeshow test, and decision curve analysis (DCA) were employed to validate and assess the model. Results: During univariate analysis, a history of allergic rhinitis, a history of eczema or atopic dermatitis, and measures including FeNO, R5, X5, R20, Fres, and R5-R20 were found to be associated with asthma diagnosis. Subsequent multivariate analysis revealed elevated FeNO, R5, and X5 as independent risk factors. The stepAIC method selected five factors (history of allergic rhinitis, history of eczema or atopic dermatitis, FeNO, R5, X5) and established a prediction model. The combined model achieved an AUROC of 0.94, with a sensitivity of 0.89 and specificity of 0.88, surpassing that of individual factors. Calibration plots and the HL test confirmed satisfactory accuracy. Conclusion: This study has developed a prediction model based on five factors, potentially aiding clinicians in early identification of asthma risk among preschool children.

Indexed as

asthmafractional expiratory nitric oxideimpulse oscillometryprediction modelpreschool children

Identifiers

PMID38736906
PMCPMC11088859

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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.