Gang Luo1, Flory L Nkoy2, Per H Gesteland3, Tiffany S Glasgow4, Bryan L Stone5. 1. Department of Biomedical Informatics, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, UT 84108, USA. Electronic address: gang.luo@utah.edu. 2. Department of Pediatrics, University of Utah, 100 N Mario Capecchi Drive, Salt Lake City, UT 84113, USA. Electronic address: flory.nkoy@hsc.utah.edu. 3. Department of Pediatrics, University of Utah, 100 N Mario Capecchi Drive, Salt Lake City, UT 84113, USA. Electronic address: per.gesteland@hsc.utah.edu. 4. Department of Pediatrics, University of Utah, 100 N Mario Capecchi Drive, Salt Lake City, UT 84113, USA. Electronic address: tiffany.glasgow@hsc.utah.edu. 5. Department of Pediatrics, University of Utah, 100 N Mario Capecchi Drive, Salt Lake City, UT 84113, USA. Electronic address: bryan.stone@hsc.utah.edu.
Abstract
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.
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.
Authors: Gang Luo; Flory L Nkoy; Bryan L Stone; Darell Schmick; Michael D Johnson Journal: BMC Med Inform Decis Mak Date: 2015-11-28 Impact factor: 2.796
Authors: Andrea Pisesky; Eric I Benchimol; Coralie A Wong; Charles Hui; Megan Crowe; Marc-Andre Belair; Supichaya Pojsupap; Tim Karnauchow; Katie O'Hearn; Abdool S Yasseen; James D McNally Journal: PLoS One Date: 2016-03-09 Impact factor: 3.240