Cargando…
A systematic review of predictive models 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 m...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4662818/ https://www.ncbi.nlm.nih.gov/pubmed/26615519 http://dx.doi.org/10.1186/s12911-015-0224-9 |
_version_ | 1782403212106530816 |
---|---|
author | Luo, Gang Nkoy, Flory L. Stone, Bryan L. Schmick, Darell Johnson, Michael D. |
author_facet | Luo, Gang Nkoy, Flory L. Stone, Bryan L. Schmick, Darell Johnson, Michael D. |
author_sort | Luo, Gang |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-015-0224-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4662818 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-46628182015-11-29 A systematic review of predictive models for asthma development in children Luo, Gang Nkoy, Flory L. Stone, Bryan L. Schmick, Darell Johnson, Michael D. BMC Med Inform Decis Mak Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-015-0224-9) contains supplementary material, which is available to authorized users. BioMed Central 2015-11-28 /pmc/articles/PMC4662818/ /pubmed/26615519 http://dx.doi.org/10.1186/s12911-015-0224-9 Text en © Luo et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Luo, Gang Nkoy, Flory L. Stone, Bryan L. Schmick, Darell Johnson, Michael D. A systematic review of predictive models for asthma development in children |
title | A systematic review of predictive models for asthma development in children |
title_full | A systematic review of predictive models for asthma development in children |
title_fullStr | A systematic review of predictive models for asthma development in children |
title_full_unstemmed | A systematic review of predictive models for asthma development in children |
title_short | A systematic review of predictive models for asthma development in children |
title_sort | systematic review of predictive models for asthma development in children |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4662818/ https://www.ncbi.nlm.nih.gov/pubmed/26615519 http://dx.doi.org/10.1186/s12911-015-0224-9 |
work_keys_str_mv | AT luogang asystematicreviewofpredictivemodelsforasthmadevelopmentinchildren AT nkoyfloryl asystematicreviewofpredictivemodelsforasthmadevelopmentinchildren AT stonebryanl asystematicreviewofpredictivemodelsforasthmadevelopmentinchildren AT schmickdarell asystematicreviewofpredictivemodelsforasthmadevelopmentinchildren AT johnsonmichaeld asystematicreviewofpredictivemodelsforasthmadevelopmentinchildren AT luogang systematicreviewofpredictivemodelsforasthmadevelopmentinchildren AT nkoyfloryl systematicreviewofpredictivemodelsforasthmadevelopmentinchildren AT stonebryanl systematicreviewofpredictivemodelsforasthmadevelopmentinchildren AT schmickdarell systematicreviewofpredictivemodelsforasthmadevelopmentinchildren AT johnsonmichaeld systematicreviewofpredictivemodelsforasthmadevelopmentinchildren |