Developing a Prediction Model for Determination of Peanut Allergy Status in the Learning Early About Peanut Allergy (LEAP) Studies
Developing a Prediction Model for Determination of Peanut Allergy Status in the Learning Early About Peanut Allergy (LEAP) Studies
Background: The Learning Early About Peanut Allergy (LEAP) study team developed a protocol-specific algorithm using dietary history, peanut-specific IgE, and skin prick test (SPT) to determine peanut allergy status if the oral food challenge (OFC) could not be administered or did not provide a determinant result.
Objective: To investigate how well the algorithm determined allergy status in LEAP; to develop a new prediction model to determine peanut allergy status when OFC results are not available in LEAP Trio, a follow-up study of LEAP participants and their families; and to compare the new prediction model with the algorithm.
Methods: The algorithm was developed for the LEAP protocol before the analysis of the primary outcome. Subsequently, a prediction model was developed using logistic regression.
Results: Using the protocol-specified algorithm, 73% (453/617) of allergy determinations matched the OFC, 0.6% (4/617) were mismatched, and 26% (160/617) participants were nonevaluable. The prediction model included SPT, peanut-specific IgE, Ara h 1, Ara h 2, and Ara h 3. The model inaccurately predicted 1 of 266 participants as allergic who were not allergic by OFC and 8 of 57 participants as not allergic who were allergic by OFC. The overall error rate was 9 of 323 (2.8%) with an area under the curve of 0.99. The prediction model additionally performed well in an external validation cohort.
Conclusion: The prediction model performed with high sensitivity and accuracy, eliminated the problem of nonevaluable outcomes, and can be used to estimate peanut allergy status in the LEAP Trio study when OFC is not available.
Diagnostic algorithm, Food allergy, LEAP, Peanut allergy, Prevention
2217-2227.e9
Sever, Michelle L
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Calatroni, Agustin
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Roberts, Graham
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du Toit, George
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Bahnson, Henry T
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Radulovic, Suzana
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Larson, David
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Byron, Margie
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Santos, Alexandra F
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Huffaker, Michelle F
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Wheatley, Lisa M
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Lack, Gideon
cac030a2-c358-4880-a91d-d67d06e8e321
1 July 2023
Sever, Michelle L
77ce753e-b94e-4304-8e98-01719887348e
Calatroni, Agustin
160c92a6-78cc-4a1f-93ed-072205d0cbde
Roberts, Graham
ea00db4e-84e7-4b39-8273-9b71dbd7e2f3
du Toit, George
7930b820-e6f7-4c4c-866c-4334017d1106
Bahnson, Henry T
2ecc6945-97fd-46bc-8d46-42606d4ccfe0
Radulovic, Suzana
8e9bce98-67a2-4999-9898-ccae71e55aa3
Larson, David
032f71e3-99c1-4cd6-a1bc-52ed3f8f976e
Byron, Margie
9be3256c-89b5-4514-ba4e-d7556273e52c
Santos, Alexandra F
f5b69586-7f5c-4972-88dd-c463990bda94
Huffaker, Michelle F
5f730e5d-be1a-401b-a8c0-dcc2df39eca9
Wheatley, Lisa M
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Lack, Gideon
cac030a2-c358-4880-a91d-d67d06e8e321
Sever, Michelle L, Calatroni, Agustin and Roberts, Graham
,
et al.
(2023)
Developing a Prediction Model for Determination of Peanut Allergy Status in the Learning Early About Peanut Allergy (LEAP) Studies.
Journal of Allergy and Clinical Immunology: In Practice, 11 (7), .
(doi:10.1016/j.jaip.2023.04.032).
Abstract
Background: The Learning Early About Peanut Allergy (LEAP) study team developed a protocol-specific algorithm using dietary history, peanut-specific IgE, and skin prick test (SPT) to determine peanut allergy status if the oral food challenge (OFC) could not be administered or did not provide a determinant result.
Objective: To investigate how well the algorithm determined allergy status in LEAP; to develop a new prediction model to determine peanut allergy status when OFC results are not available in LEAP Trio, a follow-up study of LEAP participants and their families; and to compare the new prediction model with the algorithm.
Methods: The algorithm was developed for the LEAP protocol before the analysis of the primary outcome. Subsequently, a prediction model was developed using logistic regression.
Results: Using the protocol-specified algorithm, 73% (453/617) of allergy determinations matched the OFC, 0.6% (4/617) were mismatched, and 26% (160/617) participants were nonevaluable. The prediction model included SPT, peanut-specific IgE, Ara h 1, Ara h 2, and Ara h 3. The model inaccurately predicted 1 of 266 participants as allergic who were not allergic by OFC and 8 of 57 participants as not allergic who were allergic by OFC. The overall error rate was 9 of 323 (2.8%) with an area under the curve of 0.99. The prediction model additionally performed well in an external validation cohort.
Conclusion: The prediction model performed with high sensitivity and accuracy, eliminated the problem of nonevaluable outcomes, and can be used to estimate peanut allergy status in the LEAP Trio study when OFC is not available.
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Prediction_Model_for_Determination_of_Peanut_Allergy_Status
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Accepted/In Press date: 12 April 2023
e-pub ahead of print date: 3 May 2023
Published date: 1 July 2023
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Funding Information:
This research was performed as a project of the Immune Tolerance Network , an international clinical research consortium headquartered at the Benaroya Research Institute, and supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award no. UM1AI109565 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Funding Information:
Conflicts of interest: G. Roberts reports grants from the National Institute of Allergy and Infectious Diseases (NIAID, National Institutes of Health [NIH]). G. du Toit reports grants from NIAID (NIH), Food Allergy & Research Education (FARE), MRC & Asthma UK Centre, UK Department of Health through the National Institute for Health and Care Research (NIHR), and Action Medical Research and National Peanut Board; is a scientific advisory board member of Aimmune; is an investigator on pharma-sponsored allergy studies (Aimmune and DBV Technologies); and is a scientific advisor to Aimmune, DBV, and Novartis. H. T. Bahnson reports contract work paid to the institution, Benaroya Research Institute, from DBV Technologies, MYOR, King’s College London, and Stanford University; and additional salary support paid by King’s College London and Stanford University. S. Radulovic reports salary support from grants from NIAID (NIH). D. Larson reports employment compensation from Horizon Therapeutics. A. F. Santos reports grants from the Medical Research Council, the National Institute for Health Research, and NIAID; grants pending with Asthma UK, Medical Research Council, Biotechnology and Biological Sciences Research Council, and Rosetrees Trust; consultancy from Allergy Therpeutics, Stallergenes, and IgGenix; paid speaker services for Thermofisher, Buhlmann, Infomed, Nutricia, and Nestle; and provision of reagents through collaboration with King's College London and Thermofisher and Buhlmann. G. Lack reports grants from NIAID (NIH) and other from FARE, MRC & Asthma UK Centre, UK Department of Health through NIHR, National Peanut Board (NPB), and the Davis Foundation, during the conduct of the study; is a shareholder in DBV Technologies and Mighty Mission Me; and reports personal fees from Novartis, Sanofi-Genyzme, Regeneron, ALK-Abello, and Lurie Children's Hospital, outside the submitted work. The rest of the authors declare that they have no relevant conflicts of interest.
Publisher Copyright:
© 2023 The Authors
Keywords:
Diagnostic algorithm, Food allergy, LEAP, Peanut allergy, Prevention
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Local EPrints ID: 478867
URI: http://eprints.soton.ac.uk/id/eprint/478867
ISSN: 2213-2198
PURE UUID: 88968758-18ea-45c3-b6cf-0906c52e0eeb
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Date deposited: 11 Jul 2023 17:16
Last modified: 17 Aug 2024 01:40
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Author:
Michelle L Sever
Author:
Agustin Calatroni
Author:
George du Toit
Author:
Henry T Bahnson
Author:
Suzana Radulovic
Author:
David Larson
Author:
Margie Byron
Author:
Alexandra F Santos
Author:
Michelle F Huffaker
Author:
Lisa M Wheatley
Author:
Gideon Lack
Corporate Author: et al.
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