The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

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

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

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.

0091-6749
1803-1810.e2
Biagini Myers, Jocelyn M.
377c0d6f-a230-4915-9e20-e6d978285da3
Schauberger, Eric
465a4658-a065-470e-b7dc-0b713f911922
He, Hua
78110bdb-2b38-43e9-b542-ee395e7dedb0
Martin, Lisa J.
a993799e-9a89-433a-ab4a-b56313b90163
Kroner, John
2f5f5c9e-2f3b-4727-9f8c-09a80386a0d3
Hill, Gregory M.
0668d9e9-7eea-40a0-901e-b5c8ccfe4649
Ryan, Patrick H.
14e1004d-3a10-4bf9-bf50-cb12237ee24f
LeMasters, Grace K.
5bfc0c86-899e-4260-9691-cde65d125d8a
Bernstein, David I.
09f7ab15-a526-4e48-9d8f-2653c3baf86b
Lockey, James E.
48993f06-bb7e-46a7-948f-6a26adeb8c50
Arshad, S. Hasan
917e246d-2e60-472f-8d30-94b01ef28958
Kurukulaaratchy, Ramesh
9c7b8105-2892-49f2-8775-54d4961e3e74
Khurana Hershey, Gurjit K.
53061fb3-f236-4e53-84c6-e2e5f1c89d2e
Biagini Myers, Jocelyn M.
377c0d6f-a230-4915-9e20-e6d978285da3
Schauberger, Eric
465a4658-a065-470e-b7dc-0b713f911922
He, Hua
78110bdb-2b38-43e9-b542-ee395e7dedb0
Martin, Lisa J.
a993799e-9a89-433a-ab4a-b56313b90163
Kroner, John
2f5f5c9e-2f3b-4727-9f8c-09a80386a0d3
Hill, Gregory M.
0668d9e9-7eea-40a0-901e-b5c8ccfe4649
Ryan, Patrick H.
14e1004d-3a10-4bf9-bf50-cb12237ee24f
LeMasters, Grace K.
5bfc0c86-899e-4260-9691-cde65d125d8a
Bernstein, David I.
09f7ab15-a526-4e48-9d8f-2653c3baf86b
Lockey, James E.
48993f06-bb7e-46a7-948f-6a26adeb8c50
Arshad, S. Hasan
917e246d-2e60-472f-8d30-94b01ef28958
Kurukulaaratchy, Ramesh
9c7b8105-2892-49f2-8775-54d4961e3e74
Khurana Hershey, Gurjit K.
53061fb3-f236-4e53-84c6-e2e5f1c89d2e

Biagini Myers, Jocelyn M., Schauberger, Eric, He, Hua, Martin, Lisa J., Kroner, John, Hill, Gregory M., Ryan, Patrick H., LeMasters, Grace K., Bernstein, David I., Lockey, James E., Arshad, S. Hasan, Kurukulaaratchy, Ramesh and Khurana Hershey, Gurjit K. (2019) A Pediatric Asthma Risk Score to better predict asthma development in young children. Journal of Allergy and Clinical Immunology, 143 (5), 1803-1810.e2. (doi:10.1016/j.jaci.2018.09.037).

Record type: Article

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.

Text
Figure 1 - Accepted Manuscript
Download (457kB)
Text
Table 1 - Accepted Manuscript
Download (16kB)
Text
Supplemental Figure 1 - Accepted Manuscript
Download (209kB)
Text
Supplemental Tables - Accepted Manuscript
Download (16kB)
Text
Figure 2 red and blue with shading - Accepted Manuscript
Download (6kB)
Other
Manuscript file CLEAN SUBMITTED - Accepted Manuscript
Download (123kB)

Show all 6 downloads.

More information

Accepted/In Press date: 18 September 2018
e-pub ahead of print date: 13 December 2018
Published date: May 2019

Identifiers

Local EPrints ID: 431192
URI: http://eprints.soton.ac.uk/id/eprint/431192
ISSN: 0091-6749
PURE UUID: b114ed51-0b5b-46a9-b241-de0582496832
ORCID for Ramesh Kurukulaaratchy: ORCID iD orcid.org/0000-0002-1588-2400

Catalogue record

Date deposited: 24 May 2019 16:30
Last modified: 26 Nov 2021 05:52

Export record

Altmetrics

Contributors

Author: Jocelyn M. Biagini Myers
Author: Eric Schauberger
Author: Hua He
Author: Lisa J. Martin
Author: John Kroner
Author: Gregory M. Hill
Author: Patrick H. Ryan
Author: Grace K. LeMasters
Author: David I. Bernstein
Author: James E. Lockey
Author: S. Hasan Arshad
Author: Gurjit K. Khurana Hershey

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×