ArticleThe journal of allergy and clinical immunology. In practice2023
Developing a Prediction Model for Determination of Peanut Allergy Status in the Learning Early About Peanut Allergy (LEAP) Studies.
Article in The journal of allergy and clinical immunology. In practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03546413 (Follow up of Learning Early About Peanut Allergy), which is not on this map. Cited by 7 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Follow up of Learning Early About Peanut Allergy (LEAP) Participants and Their Families: LEAP Trio (ITN070AD)
Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Follow-up to Adolescence after Early Peanut Introduction for Allergy Prevention.NEJM evidence · 2024Trial
- Differentiating Peanut Allergy from Sensitization in Polish Children: A Real-Life Diagnostic Model.Life (Basel, Switzerland) · 2026Article
- Innovative diagnostic techniques and their clinical implications in food allergy: current clinical practice and future perspectives.Frontiers in allergy · 2026Review
- Objective sensitization algorithm validates parental report of food allergy in children.The journal of allergy and clinical immunology. Global · 2025Article
- Skin testing improves predictive value of midrange peanut-specific IgE and Ara h 2 levels in the LEAP study.The journal of allergy and clinical immunology. In practice · 2025Article
- Peanut-specific IgG subclasses as biomarkers of peanut allergy in LEAP study participants.The World Allergy Organization journal · 2024Article
- Risk subgroups and intervention effects among infants at high risk for peanut allergy: A model for clinical decision making.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2024Article
Corrections and comments
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Authors and funding
12 authors at 7 institutions in 2 countries.
Funding
Abstract
backgroundThe 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.
objectiveTo 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.
methodsThe algorithm was developed for the LEAP protocol before the analysis of the primary outcome. Subsequently, a prediction model was developed using logistic regression.
resultsUsing 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.
conclusionThe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.