Developing a Prediction Model for Determination of Peanut Allergy Status in the Learning Early About Peanut Allergy (LEAP) Studies

Michelle L. Sever; Agustin Calatroni; Graham Roberts; George du Toit; Henry T. Bahnson; Suzana Radulovic; David Larson; Margie Byron; Alexandra F. Santos; Michelle F. Huffaker; Lisa M. Wheatley; Gideon Lack

doi:10.1016/j.jaip.2023.04.032

Developing a Prediction Model for Determination of Peanut Allergy Status in the Learning Early About Peanut Allergy (LEAP) Studies

Michelle L. Sever, Agustin Calatroni, Graham Roberts, George du Toit, Henry T. Bahnson, Suzana Radulovic, David Larson, Margie Byron, Alexandra F. Santos, Michelle F. Huffaker^*, Lisa M. Wheatley, Gideon Lack

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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.

Original language	English
Pages (from-to)	2217-2227.e9
Journal	Journal of Allergy and Clinical Immunology: In Practice
Volume	11
Issue number	7
DOIs	https://doi.org/10.1016/j.jaip.2023.04.032
Publication status	Published - Jul 2023

Keywords

Diagnostic algorithm
Food allergy
LEAP
Peanut allergy
Prevention

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10.1016/j.jaip.2023.04.032

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Sever, M. L., Calatroni, A., Roberts, G., du Toit, G., Bahnson, H. T., Radulovic, S., Larson, D., Byron, M., Santos, A. F., Huffaker, M. F., Wheatley, L. M., & Lack, G. (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), 2217-2227.e9. https://doi.org/10.1016/j.jaip.2023.04.032

@article{86bb7eb8046449b6b685ccb0490a10ff,

title = "Developing a Prediction Model for Determination of Peanut Allergy Status in the Learning Early About Peanut Allergy (LEAP) Studies",

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.",

keywords = "Diagnostic algorithm, Food allergy, LEAP, Peanut allergy, Prevention",

author = "Sever, {Michelle L.} and Agustin Calatroni and Graham Roberts and {du Toit}, George and Bahnson, {Henry T.} and Suzana Radulovic and David Larson and Margie Byron and Santos, {Alexandra F.} and Huffaker, {Michelle F.} and Wheatley, {Lisa M.} and Gideon Lack",

note = "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{\textquoteright}s College London, and Stanford University; and additional salary support paid by King{\textquoteright}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: {\textcopyright} 2023 The Authors",

year = "2023",

month = jul,

doi = "10.1016/j.jaip.2023.04.032",

language = "English",

volume = "11",

pages = "2217--2227.e9",

journal = "Journal of Allergy and Clinical Immunology: In Practice",

issn = "2213-2198",

publisher = "Elsevier",

number = "7",

}

Sever, ML, Calatroni, A, Roberts, G, du Toit, G, Bahnson, HT, Radulovic, S, Larson, D, Byron, M, Santos, AF, Huffaker, MF, Wheatley, LM & Lack, G 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, vol. 11, no. 7, pp. 2217-2227.e9. https://doi.org/10.1016/j.jaip.2023.04.032

TY - JOUR

T1 - Developing a Prediction Model for Determination of Peanut Allergy Status in the Learning Early About Peanut Allergy (LEAP) Studies

AU - Sever, Michelle L.

AU - Calatroni, Agustin

AU - Roberts, Graham

AU - du Toit, George

AU - Bahnson, Henry T.

AU - Radulovic, Suzana

AU - Larson, David

AU - Byron, Margie

AU - Santos, Alexandra F.

AU - Huffaker, Michelle F.

AU - Wheatley, Lisa M.

AU - Lack, Gideon

N1 - 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

PY - 2023/7

Y1 - 2023/7

N2 - 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.

AB - 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.

KW - Diagnostic algorithm

KW - Food allergy

KW - LEAP

KW - Peanut allergy

KW - Prevention

UR - http://www.scopus.com/inward/record.url?scp=85161268994&partnerID=8YFLogxK

U2 - 10.1016/j.jaip.2023.04.032

DO - 10.1016/j.jaip.2023.04.032

M3 - Article

C2 - 37146884

AN - SCOPUS:85161268994

SN - 2213-2198

VL - 11

SP - 2217-2227.e9

JO - Journal of Allergy and Clinical Immunology: In Practice

JF - Journal of Allergy and Clinical Immunology: In Practice

IS - 7

ER -

Developing a Prediction Model for Determination of Peanut Allergy Status in the Learning Early About Peanut Allergy (LEAP) Studies

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