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Component-resolved diagnosis and beyond: Multivariable regression models to predict severity of hazelnut allergy. / Datema, Mareen R.; van Ree, Ronald; Asero, Riccardo; Barreales, Laura; Belohlavkova, Simona; de Blay, Frédéric; Clausen, Michael; Dubakiene, Ruta; Fernández-Perez, Cristina; Fritsche, Philipp; Gislason, David; Hoffmann-Sommergruber, Karin; Jedrzejczak-Czechowicz, Monika; Jongejan, Laurian; Knulst, André C.; Kowalski, Marek; Kralimarkova, Tanya Z.; Le, Thuy-My; Lidholm, Jonas; Papadopoulos, Nikolaos G.; Popov, Todor A.; del Prado, Nayade; Purohit, Ashok; Reig, Isabel; Seneviratne, Suranjith L.; Sinaniotis, Athanassios; Versteeg, Serge A.; Vieths, Stefan; Zwinderman, A. H.; Clare Mills, E. N.; Fernández-Rivas, Montserrat; Ballmer-Weber, Barbara.

In: Allergy, Vol. 73, No. 3, 2018, p. 549-559.

Research output: Contribution to journalArticleAcademicpeer-review

Harvard

Datema, MR, van Ree, R, Asero, R, Barreales, L, Belohlavkova, S, de Blay, F, Clausen, M, Dubakiene, R, Fernández-Perez, C, Fritsche, P, Gislason, D, Hoffmann-Sommergruber, K, Jedrzejczak-Czechowicz, M, Jongejan, L, Knulst, AC, Kowalski, M, Kralimarkova, TZ, Le, T-M, Lidholm, J, Papadopoulos, NG, Popov, TA, del Prado, N, Purohit, A, Reig, I, Seneviratne, SL, Sinaniotis, A, Versteeg, SA, Vieths, S, Zwinderman, AH, Clare Mills, EN, Fernández-Rivas, M & Ballmer-Weber, B 2018, 'Component-resolved diagnosis and beyond: Multivariable regression models to predict severity of hazelnut allergy' Allergy, vol. 73, no. 3, pp. 549-559. https://doi.org/10.1111/all.13328

APA

Datema, M. R., van Ree, R., Asero, R., Barreales, L., Belohlavkova, S., de Blay, F., ... Ballmer-Weber, B. (2018). Component-resolved diagnosis and beyond: Multivariable regression models to predict severity of hazelnut allergy. Allergy, 73(3), 549-559. https://doi.org/10.1111/all.13328

Vancouver

Author

Datema, Mareen R. ; van Ree, Ronald ; Asero, Riccardo ; Barreales, Laura ; Belohlavkova, Simona ; de Blay, Frédéric ; Clausen, Michael ; Dubakiene, Ruta ; Fernández-Perez, Cristina ; Fritsche, Philipp ; Gislason, David ; Hoffmann-Sommergruber, Karin ; Jedrzejczak-Czechowicz, Monika ; Jongejan, Laurian ; Knulst, André C. ; Kowalski, Marek ; Kralimarkova, Tanya Z. ; Le, Thuy-My ; Lidholm, Jonas ; Papadopoulos, Nikolaos G. ; Popov, Todor A. ; del Prado, Nayade ; Purohit, Ashok ; Reig, Isabel ; Seneviratne, Suranjith L. ; Sinaniotis, Athanassios ; Versteeg, Serge A. ; Vieths, Stefan ; Zwinderman, A. H. ; Clare Mills, E. N. ; Fernández-Rivas, Montserrat ; Ballmer-Weber, Barbara. / Component-resolved diagnosis and beyond: Multivariable regression models to predict severity of hazelnut allergy. In: Allergy. 2018 ; Vol. 73, No. 3. pp. 549-559.

BibTeX

@article{2d9780329ad548dca9d85635a5ceb538,
title = "Component-resolved diagnosis and beyond: Multivariable regression models to predict severity of hazelnut allergy",
abstract = "Component-resolved diagnosis (CRD) has revealed significant associations between IgE against individual allergens and severity of hazelnut allergy. Less attention has been given to combining them with clinical factors in predicting severity. To analyze associations between severity and sensitization patterns, patient characteristics and clinical history, and to develop models to improve predictive accuracy. Patients reporting hazelnut allergy (n = 423) from 12 European cities were tested for IgE against individual hazelnut allergens. Symptoms (reported and during Double-blind placebo-controlled food challenge [DBPCFC]) were categorized in mild, moderate, and severe. Multiple regression models to predict severity were generated from clinical factors and sensitization patterns (CRD- and extract-based). Odds ratios (ORs) and areas under receiver-operating characteristic (ROC) curves (AUCs) were used to evaluate their predictive value. Cor a 9 and 14 were positively (OR 10.5 and 10.1, respectively), and Cor a 1 negatively (OR 0.14) associated with severe symptoms during DBPCFC, with AUCs of 0.70-073. Combining Cor a 1 and 9 improved this to 0.76. A model using a combination of atopic dermatitis (risk), pollen allergy (protection), IgE against Cor a 14 (risk) and walnut (risk) increased the AUC to 0.91. At 92{\%} sensitivity, the specificity was 76.3{\%}, and the positive and negative predictive values 62.2{\%} and 95.7{\%}, respectively. For reported symptoms, associations and generated models proved to be almost identical but weaker. A model combining CRD with clinical background and extract-based serology is superior to CRD alone in assessing the risk of severe reactions to hazelnut, particular in ruling out severe reactions",
author = "Datema, {Mareen R.} and {van Ree}, Ronald and Riccardo Asero and Laura Barreales and Simona Belohlavkova and {de Blay}, Fr{\'e}d{\'e}ric and Michael Clausen and Ruta Dubakiene and Cristina Fern{\'a}ndez-Perez and Philipp Fritsche and David Gislason and Karin Hoffmann-Sommergruber and Monika Jedrzejczak-Czechowicz and Laurian Jongejan and Knulst, {Andr{\'e} C.} and Marek Kowalski and Kralimarkova, {Tanya Z.} and Thuy-My Le and Jonas Lidholm and Papadopoulos, {Nikolaos G.} and Popov, {Todor A.} and {del Prado}, Nayade and Ashok Purohit and Isabel Reig and Seneviratne, {Suranjith L.} and Athanassios Sinaniotis and Versteeg, {Serge A.} and Stefan Vieths and Zwinderman, {A. H.} and {Clare Mills}, {E. N.} and Montserrat Fern{\'a}ndez-Rivas and Barbara Ballmer-Weber",
year = "2018",
doi = "10.1111/all.13328",
language = "English",
volume = "73",
pages = "549--559",
journal = "Allergy",
issn = "0105-4538",
publisher = "Wiley-Blackwell",
number = "3",

}

RIS

TY - JOUR

T1 - Component-resolved diagnosis and beyond: Multivariable regression models to predict severity of hazelnut allergy

AU - Datema, Mareen R.

AU - van Ree, Ronald

AU - Asero, Riccardo

AU - Barreales, Laura

AU - Belohlavkova, Simona

AU - de Blay, Frédéric

AU - Clausen, Michael

AU - Dubakiene, Ruta

AU - Fernández-Perez, Cristina

AU - Fritsche, Philipp

AU - Gislason, David

AU - Hoffmann-Sommergruber, Karin

AU - Jedrzejczak-Czechowicz, Monika

AU - Jongejan, Laurian

AU - Knulst, André C.

AU - Kowalski, Marek

AU - Kralimarkova, Tanya Z.

AU - Le, Thuy-My

AU - Lidholm, Jonas

AU - Papadopoulos, Nikolaos G.

AU - Popov, Todor A.

AU - del Prado, Nayade

AU - Purohit, Ashok

AU - Reig, Isabel

AU - Seneviratne, Suranjith L.

AU - Sinaniotis, Athanassios

AU - Versteeg, Serge A.

AU - Vieths, Stefan

AU - Zwinderman, A. H.

AU - Clare Mills, E. N.

AU - Fernández-Rivas, Montserrat

AU - Ballmer-Weber, Barbara

PY - 2018

Y1 - 2018

N2 - Component-resolved diagnosis (CRD) has revealed significant associations between IgE against individual allergens and severity of hazelnut allergy. Less attention has been given to combining them with clinical factors in predicting severity. To analyze associations between severity and sensitization patterns, patient characteristics and clinical history, and to develop models to improve predictive accuracy. Patients reporting hazelnut allergy (n = 423) from 12 European cities were tested for IgE against individual hazelnut allergens. Symptoms (reported and during Double-blind placebo-controlled food challenge [DBPCFC]) were categorized in mild, moderate, and severe. Multiple regression models to predict severity were generated from clinical factors and sensitization patterns (CRD- and extract-based). Odds ratios (ORs) and areas under receiver-operating characteristic (ROC) curves (AUCs) were used to evaluate their predictive value. Cor a 9 and 14 were positively (OR 10.5 and 10.1, respectively), and Cor a 1 negatively (OR 0.14) associated with severe symptoms during DBPCFC, with AUCs of 0.70-073. Combining Cor a 1 and 9 improved this to 0.76. A model using a combination of atopic dermatitis (risk), pollen allergy (protection), IgE against Cor a 14 (risk) and walnut (risk) increased the AUC to 0.91. At 92% sensitivity, the specificity was 76.3%, and the positive and negative predictive values 62.2% and 95.7%, respectively. For reported symptoms, associations and generated models proved to be almost identical but weaker. A model combining CRD with clinical background and extract-based serology is superior to CRD alone in assessing the risk of severe reactions to hazelnut, particular in ruling out severe reactions

AB - Component-resolved diagnosis (CRD) has revealed significant associations between IgE against individual allergens and severity of hazelnut allergy. Less attention has been given to combining them with clinical factors in predicting severity. To analyze associations between severity and sensitization patterns, patient characteristics and clinical history, and to develop models to improve predictive accuracy. Patients reporting hazelnut allergy (n = 423) from 12 European cities were tested for IgE against individual hazelnut allergens. Symptoms (reported and during Double-blind placebo-controlled food challenge [DBPCFC]) were categorized in mild, moderate, and severe. Multiple regression models to predict severity were generated from clinical factors and sensitization patterns (CRD- and extract-based). Odds ratios (ORs) and areas under receiver-operating characteristic (ROC) curves (AUCs) were used to evaluate their predictive value. Cor a 9 and 14 were positively (OR 10.5 and 10.1, respectively), and Cor a 1 negatively (OR 0.14) associated with severe symptoms during DBPCFC, with AUCs of 0.70-073. Combining Cor a 1 and 9 improved this to 0.76. A model using a combination of atopic dermatitis (risk), pollen allergy (protection), IgE against Cor a 14 (risk) and walnut (risk) increased the AUC to 0.91. At 92% sensitivity, the specificity was 76.3%, and the positive and negative predictive values 62.2% and 95.7%, respectively. For reported symptoms, associations and generated models proved to be almost identical but weaker. A model combining CRD with clinical background and extract-based serology is superior to CRD alone in assessing the risk of severe reactions to hazelnut, particular in ruling out severe reactions

U2 - 10.1111/all.13328

DO - 10.1111/all.13328

M3 - Article

VL - 73

SP - 549

EP - 559

JO - Allergy

JF - Allergy

SN - 0105-4538

IS - 3

ER -

ID: 4104393