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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.
BMC Medical Informatics and Decision Making
|November 30, 2015
Summary
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.
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