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J Allergy Clin Immunol. 2018 Dec 7. pii: S0091-6749(18)31577-X. doi: 10.1016/j.jaci.2018.09.037. [Epub ahead of print]

A Pediatric Asthma Risk Score to better predict asthma development in young children.

Author information

1
Division of Asthma Research, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio; Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio.
2
Division of Asthma Research, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio; Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
3
Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
4
Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio; Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio.
5
Division of Asthma Research, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
6
Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio; Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio.
7
Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio.
8
Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio; Department of Internal Medicine, University of Cincinnati, Cincinnati, Ohio.
9
David Hide Asthma & Allergy Research Centre, St Mary's Hospital, Newport, Isle of Wight.
10
Division of Asthma Research, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio; Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio. Electronic address: Gurjit.Hershey@cchmc.org.

Abstract

BACKGROUND:

Asthma phenotypes are currently not amenable to primary prevention or early intervention because their natural history cannot be reliably predicted. Clinicians remain reliant on poorly predictive asthma outcome tools because of a lack of better alternatives.

OBJECTIVE:

We sought to develop a quantitative personalized tool to predict asthma development in young children.

METHODS:

Data from the Cincinnati Childhood Allergy and Air Pollution Study (n = 762) birth cohort were used to identify factors that predicted asthma development. The Pediatric Asthma Risk Score (PARS) was constructed by integrating demographic and clinical data. The sensitivity and specificity of PARS were compared with those of the Asthma Predictive Index (API) and replicated in the Isle of Wight birth cohort.

RESULTS:

PARS reliably predicted asthma development in the Cincinnati Childhood Allergy and Air Pollution Study (sensitivity = 0.68, specificity = 0.77). Although both the PARS and API predicted asthma in high-risk children, the PARS had improved ability to predict asthma in children with mild-to-moderate asthma risk. In addition to parental asthma, eczema, and wheezing apart from colds, variables that predicted asthma in the PARS included early wheezing (odds ratio [OR], 2.88; 95% CI, 1.52-5.37), sensitization to 2 or more food allergens and/or aeroallergens (OR, 2.44; 95% CI, 1.49-4.05), and African American race (OR, 2.04; 95% CI, 1.19-3.47). The PARS was replicated in the Isle of Wight birth cohort (sensitivity = 0.67, specificity = 0.79), demonstrating that it is a robust, valid, and generalizable asthma predictive tool.

CONCLUSIONS:

The PARS performed better than the API in children with mild-to-moderate asthma. This is significant because these children are the most common and most difficult to predict and might be the most amenable to prevention strategies.

KEYWORDS:

Asthma prediction score; childhood asthma; persistent wheezing; sensitization

PMID:
30554722
DOI:
10.1016/j.jaci.2018.09.037

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