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.
1803-1810.e2
Biagini Myers, Jocelyn M.
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Schauberger, Eric
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He, Hua
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Martin, Lisa J.
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Kroner, John
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Hill, Gregory M.
0668d9e9-7eea-40a0-901e-b5c8ccfe4649
Ryan, Patrick H.
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LeMasters, Grace K.
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Bernstein, David I.
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Lockey, James E.
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Arshad, S. Hasan
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Kurukulaaratchy, Ramesh
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Khurana Hershey, Gurjit K.
53061fb3-f236-4e53-84c6-e2e5f1c89d2e
May 2019
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), .
(doi:10.1016/j.jaci.2018.09.037).
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.
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Accepted/In Press date: 18 September 2018
e-pub ahead of print date: 13 December 2018
Published date: May 2019
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Local EPrints ID: 431192
URI: http://eprints.soton.ac.uk/id/eprint/431192
ISSN: 0091-6749
PURE UUID: b114ed51-0b5b-46a9-b241-de0582496832
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Date deposited: 24 May 2019 16:30
Last modified: 16 Mar 2024 07:52
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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:
Gurjit K. Khurana Hershey
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