Highly accurate prediction of food challenge outcome using routinely available clinical data - 10/08/11
, Deirdre Daly, RN a, Claire Cullinane, RN a, Emily Stenke, MB a, Diane Keeton, RSCN b, Mich Erlewyn-Lajeunesse, MRCPCH DM b, Graham C. Roberts, MRCPCH DM b, c, Jane Lucas, MRCPCH PhD a, b, c, Jonathan O’B. Hourihane, MB, DM, MRCPI, FRCPCH a
Reprint requests: Audrey DunnGalvin, PhD, Department of Paediatrics and Child Health, Clinical Investigations Unit, Cork University Hospital, Wilton, Cork, Ireland.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.
Le texte complet de cet article est disponible en PDF.Key words : Food challenge, predictive model, validation, calculator, outcomes
Abbreviations used : AUC, DBPCFC, FA, OFC, ROC, sIgE, SPT
Plan
| Disclosure of potential conflict of interest: J. O’B. Hourihane receives speaker fees from King Pharmaceuticals, ALK-Abelló, Mead Johnson, and Phadia; receives research support from DANONE, the Children’s Research Center, and the Food Standards Agency; has provided legal consultation services or expert witness testimony in cases related to food allergy death; is honorary secretary of the Irish Association of Allergy and Immunology; is a medical advisor for the Anaphylaxis Campaign UK; and has a patent application relating to electronic use of the algorithm developed in this project. D. Daly receives research support from DANONE and Mead Johnson. The rest of the authors have declared that they have no conflict of interest. |
Vol 127 - N° 3
P. 633 - mars 2011 Retour au numéro
Article précédent
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