《for asthma development in children》.pdfVIP

Luo et al. BMC Medical Informatics and Decision Making (2015) 15:99 DOI 10.1186/s12911-015-0224-9 RESEARCH ARTICLE Open Access A systematic review of predictive models for asthma development in children 1* 2 2 3 2 Gang Luo , Flory L. Nkoy , Bryan L. Stone , Darell Schmick and Michael D. Johnson Abstract 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 impr