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Prediction models for childhood asthma: a systematic review

Prediction models for childhood asthma: a systematic review
Prediction models for childhood asthma: a systematic review

Background: The inability to objectively diagnose childhood asthma before age five often results in both under-treatment and over-treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school-age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school-age asthma. Methods: Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school-age children (6-13 years). Validation studies were evaluated as a secondary objective. Results: Twenty-four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression-based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression-based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62-0.83). Conclusion: Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school-age asthma prediction.

asthma, childhood, prediction model, risk scores, wheeze
0905-6157
616-627
Kothalawala, Dilini Mahesha
c22b9e92-e60a-44b6-a34b-2eb37a3a1212
Perunthadambil Kadalayil, Latha
e620b801-844a-45d9-acaf-e0a58acd7cf2
Weiss, Veronique B.N.
d862ff0c-d721-4df0-bd6d-43ef50809c4a
Kyyaly, Mohammed Aref
7bd69b33-fec8-405c-9f40-b7157f0242f0
Arshad, Syed
917e246d-2e60-472f-8d30-94b01ef28958
Holloway, John
4bbd77e6-c095-445d-a36b-a50a72f6fe1a
Rezwan, Faisal I
203f8f38-1f5d-485b-ab11-c546b4276338
Kothalawala, Dilini Mahesha
c22b9e92-e60a-44b6-a34b-2eb37a3a1212
Perunthadambil Kadalayil, Latha
e620b801-844a-45d9-acaf-e0a58acd7cf2
Weiss, Veronique B.N.
d862ff0c-d721-4df0-bd6d-43ef50809c4a
Kyyaly, Mohammed Aref
7bd69b33-fec8-405c-9f40-b7157f0242f0
Arshad, Syed
917e246d-2e60-472f-8d30-94b01ef28958
Holloway, John
4bbd77e6-c095-445d-a36b-a50a72f6fe1a
Rezwan, Faisal I
203f8f38-1f5d-485b-ab11-c546b4276338

Kothalawala, Dilini Mahesha, Perunthadambil Kadalayil, Latha, Weiss, Veronique B.N., Kyyaly, Mohammed Aref, Arshad, Syed, Holloway, John and Rezwan, Faisal I (2020) Prediction models for childhood asthma: a systematic review. Pediatric Allergy and Immunology, 31 (6), 616-627. (doi:10.1111/pai.13247).

Record type: Article

Abstract

Background: The inability to objectively diagnose childhood asthma before age five often results in both under-treatment and over-treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school-age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school-age asthma. Methods: Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school-age children (6-13 years). Validation studies were evaluated as a secondary objective. Results: Twenty-four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression-based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression-based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62-0.83). Conclusion: Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school-age asthma prediction.

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Kothalawala et al PAI full final revised manuscript - Accepted Manuscript
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More information

Accepted/In Press date: 28 February 2020
e-pub ahead of print date: 17 March 2020
Published date: August 2020
Keywords: asthma, childhood, prediction model, risk scores, wheeze

Identifiers

Local EPrints ID: 438442
URI: http://eprints.soton.ac.uk/id/eprint/438442
ISSN: 0905-6157
PURE UUID: 1ddb51d4-b1e6-4254-bb7c-cfbc951e780d
ORCID for Mohammed Aref Kyyaly: ORCID iD orcid.org/0000-0002-1684-9207
ORCID for John Holloway: ORCID iD orcid.org/0000-0001-9998-0464
ORCID for Faisal I Rezwan: ORCID iD orcid.org/0000-0001-9921-222X

Catalogue record

Date deposited: 10 Mar 2020 17:31
Last modified: 03 Dec 2021 05:01

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Contributors

Author: Dilini Mahesha Kothalawala
Author: Veronique B.N. Weiss
Author: Syed Arshad
Author: John Holloway ORCID iD
Author: Faisal I Rezwan ORCID iD

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