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Highly accurate prediction of food challenge outcome using routinely available clinical data

Highly accurate prediction of food challenge outcome using routinely available clinical data
Highly accurate prediction of food challenge outcome using routinely available clinical data
Background: Serum specific IgE or skin prick tests are less useful at levels below accepted decision points.

Objectives: We sought to develop and validate a model to predict food challenge outcome by using routinely collected data in a diverse sample of children considered suitable for food challenge.

Methods: The proto-algorithm was generated by using a limited data set from 1 service (phase 1). We retrospectively applied, evaluated, and modified the initial model by using an extended data set in another center (phase 2). Finally, we prospectively validated the model in a blind study in a further group of children undergoing food challenge for peanut, milk, or egg in the second center (phase 3). Allergen-specific models were developed for peanut, egg, and milk.

Results: Phase 1 (N = 429) identified 5 clinical factors associated with diagnosis of food allergy by food challenge. In phase 2 (N = 289), we examined the predictive ability of 6 clinical factors: skin prick test, serum specific IgE, total IgE minus serum specific IgE, symptoms, sex, and age. In phase 3 (N = 70), 97% of cases were accurately predicted as positive and 94% as negative. Our model showed an advantage in clinical prediction compared with serum specific IgE only, skin prick test only, and serum specific IgE and skin prick test (92% accuracy vs 57%, and 81%, respectively).

Conclusion: Our findings have implications for the improved delivery of food allergy–related health care, enhanced food allergy–related quality of life, and economized use of health service resources by decreasing the number of food challenges performed.
food challenge, predictive model, validations calculator, outcomes
0091-6749
633-639.e3
DunnGalvin, Audrey
cb3a7df5-feb4-414a-b528-459c52dd2a80
Daly, Deirdre
63c6153e-40e4-4679-b073-3b0162f794b9
Cullinane, Claire
d7843ac6-f0a7-41fe-8021-1121a2a3abb9
Stenke, Emily
97671d10-ad43-4ea9-9fef-8e7229c93a8b
Keeton, Diane
db40a7aa-86e8-490c-a69d-d9f5ea3c3279
Erlewyn-Lajeunesse, Michel
cce68767-8d78-45ad-bb00-3da4f83d4ea6
Roberts, Graham C
ea00db4e-84e7-4b39-8273-9b71dbd7e2f3
Lucas, Jane
5cb3546c-87b2-4e59-af48-402076e25313
Hourihane, Jonathan O'.B.
25de726c-3e91-4fc2-9414-e3974bb22daf
DunnGalvin, Audrey
cb3a7df5-feb4-414a-b528-459c52dd2a80
Daly, Deirdre
63c6153e-40e4-4679-b073-3b0162f794b9
Cullinane, Claire
d7843ac6-f0a7-41fe-8021-1121a2a3abb9
Stenke, Emily
97671d10-ad43-4ea9-9fef-8e7229c93a8b
Keeton, Diane
db40a7aa-86e8-490c-a69d-d9f5ea3c3279
Erlewyn-Lajeunesse, Michel
cce68767-8d78-45ad-bb00-3da4f83d4ea6
Roberts, Graham C
ea00db4e-84e7-4b39-8273-9b71dbd7e2f3
Lucas, Jane
5cb3546c-87b2-4e59-af48-402076e25313
Hourihane, Jonathan O'.B.
25de726c-3e91-4fc2-9414-e3974bb22daf

DunnGalvin, Audrey, Daly, Deirdre, Cullinane, Claire, Stenke, Emily, Keeton, Diane, Erlewyn-Lajeunesse, Michel, Roberts, Graham C, Lucas, Jane and Hourihane, Jonathan O'.B. (2011) Highly accurate prediction of food challenge outcome using routinely available clinical data. Journal of Allergy and Clinical Immunology, 127 (3), 633-639.e3. (doi:10.1016/j.jaci.2010.12.004). (PMID:21377032)

Record type: Article

Abstract

Background: Serum specific IgE or skin prick tests are less useful at levels below accepted decision points.

Objectives: We sought to develop and validate a model to predict food challenge outcome by using routinely collected data in a diverse sample of children considered suitable for food challenge.

Methods: The proto-algorithm was generated by using a limited data set from 1 service (phase 1). We retrospectively applied, evaluated, and modified the initial model by using an extended data set in another center (phase 2). Finally, we prospectively validated the model in a blind study in a further group of children undergoing food challenge for peanut, milk, or egg in the second center (phase 3). Allergen-specific models were developed for peanut, egg, and milk.

Results: Phase 1 (N = 429) identified 5 clinical factors associated with diagnosis of food allergy by food challenge. In phase 2 (N = 289), we examined the predictive ability of 6 clinical factors: skin prick test, serum specific IgE, total IgE minus serum specific IgE, symptoms, sex, and age. In phase 3 (N = 70), 97% of cases were accurately predicted as positive and 94% as negative. Our model showed an advantage in clinical prediction compared with serum specific IgE only, skin prick test only, and serum specific IgE and skin prick test (92% accuracy vs 57%, and 81%, respectively).

Conclusion: Our findings have implications for the improved delivery of food allergy–related health care, enhanced food allergy–related quality of life, and economized use of health service resources by decreasing the number of food challenges performed.

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More information

Published date: March 2011
Keywords: food challenge, predictive model, validations calculator, outcomes
Organisations: Infection Inflammation & Immunity, Human Development & Health

Identifiers

Local EPrints ID: 178055
URI: http://eprints.soton.ac.uk/id/eprint/178055
ISSN: 0091-6749
PURE UUID: bdc5776e-0bb0-4d67-8216-f21fa9f32cac
ORCID for Graham C Roberts: ORCID iD orcid.org/0000-0003-2252-1248
ORCID for Jane Lucas: ORCID iD orcid.org/0000-0001-8701-9975

Catalogue record

Date deposited: 22 Mar 2011 16:02
Last modified: 14 Mar 2024 02:50

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Contributors

Author: Audrey DunnGalvin
Author: Deirdre Daly
Author: Claire Cullinane
Author: Emily Stenke
Author: Diane Keeton
Author: Michel Erlewyn-Lajeunesse
Author: Jane Lucas ORCID iD
Author: Jonathan O'.B. Hourihane

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