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Published on: May 14, 2012
A systematic review of predictive models for asthma development in children
Gang Luo1, Flory L Nkoy2, Bryan L Stone2
1Department of Biomedical Informatics, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, UT, 84108, USA. gang.luo@utah.edu.
Insights
Predictive models for childhood asthma show inadequate accuracy. Improving these models is crucial for early diagnosis and management, but a lack of a gold standard hinders progress in asthma prevention research.
Area of Science:
- Pediatric Pulmonology
- Computational Health
- Biostatistics
Background:
- Asthma is a leading chronic childhood illness in the US, affecting nearly 10% of children.
- Delayed diagnosis of asthma leads to ineffective management strategies.
- Predictive models are being developed to aid early asthma diagnosis and prevention research.
Purpose of the Study:
- To review existing predictive models for childhood asthma development.
- To identify limitations in current predictive models.
- To suggest avenues for future research in asthma prediction.
Main Methods:
- A systematic review of literature was performed using multiple databases (PubMed, EMBASE, etc.) up to June 2015.
- Search results were filtered for human subjects, specifically children aged 0-18 years.
- Two independent reviewers screened articles, extracted data, and assessed study quality.
Main Results:
- The review identified 32 relevant studies from over 13,000 initial references.
- Several limitations were identified in the existing predictive models for childhood asthma.
- Preliminary recommendations for addressing these limitations were proposed.
Conclusions:
- Current predictive models for childhood asthma lack sufficient accuracy.
- Enhancing the performance of these models is necessary for better clinical application.
- A significant limitation to improving model accuracy is the absence of a definitive gold standard for asthma development in children.
Background:
Asthma is the most common pediatric chronic disease affecting 9.6 % of American children. Delay in asthma diagnosis is prevalent, resulting in suboptimal asthma management. To help avoid delay in asthma diagnosis and advance asthma prevention research, researchers have proposed various models to predict asthma development in children. This paper reviews these models.
Methods:
A systematic review was conducted through searching in PubMed, EMBASE, CINAHL, Scopus, the Cochrane Library, the ACM Digital Library, IEEE Xplore, and OpenGrey up to June 3, 2015. The literature on predictive models for asthma development in children was retrieved, with search results limited to human subjects and children (birth to 18 years). Two independent reviewers screened the literature, performed data extraction, and assessed article quality.
Results:
The literature search returned 13,101 references in total. After manual review, 32 of these references were determined to be relevant and are discussed in the paper. We identify several limitations of existing predictive models for asthma development in children, and provide preliminary thoughts on how to address these limitations.
Conclusions:
Existing predictive models for asthma development in children have inadequate accuracy. Efforts to improve these models' performance are needed, but are limited by a lack of a gold standard for asthma development in children.
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