A systematic review of predictive modeling for bronchiolitis
Gang Luo1, Flory L Nkoy2, Per H Gesteland2
1Department of Biomedical Informatics, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, UT 84108, USA.
Insights
Predictive models for childhood bronchiolitis aim to standardize care, but many challenges remain. Further research is needed to develop optimal models for predicting disease course and guiding treatment decisions for this common childhood illness.
Area of Science:
- Pediatrics
- Medical Informatics
- Epidemiology
Background:
- Bronchiolitis is a leading cause of hospitalization in young children.
- Current management decisions for bronchiolitis are often subjective, leading to practice variations.
- Standardizing care requires reliable methods for predicting disease progression.
Purpose of the Study:
- To review the current state of predictive modeling for bronchiolitis.
- To identify limitations and open problems in existing predictive models.
- To stimulate future research for developing optimal predictive models.
Main Methods:
- Systematic literature review using PubMed.
- Comprehensive search query developed iteratively.
- Inclusion criteria: human subjects, English language, children (0-18 years).
Main Results:
- Initial search yielded 2312 references.
- 168 relevant references were identified and discussed.
- Identified limitations and open problems in current predictive modeling.
Conclusions:
- Significant challenges persist in developing effective predictive models for bronchiolitis.
- Future studies must address these limitations to achieve optimal predictive accuracy.
- Improved models are crucial for standardizing bronchiolitis management and improving patient outcomes.
Purpose:
Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease course to help determine a proper management plan. This paper reviews the existing state of the art of predictive modeling for bronchiolitis. Predictive modeling for respiratory syncytial virus (RSV) infection is covered whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases.
Methods:
A systematic review was conducted through a PubMed search up to April 25, 2014. The literature on predictive modeling for bronchiolitis was retrieved using a comprehensive search query, which was developed through an iterative process. Search results were limited to human subjects, the English language, and children (birth to 18 years).
Results:
The literature search returned 2312 references in total. After manual review, 168 of these references were determined to be relevant and are discussed in this paper. We identify several limitations and open problems in predictive modeling for bronchiolitis, and provide some preliminary thoughts on how to address them, with the hope to stimulate future research in this domain.
Conclusions:
Many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models.
