Assessment of egg and milk allergies among Indians by revalidating a food allergy predictive model.

Background: The recent upsurge in food allergy indicates the need for accurate medical diagnostics. The application of predictive diagnostic models can envisage the outcome of oral food challenge (OFC), reducing cost and time. A logistic regression model was developed by DunnGalvin for children predicting OFC outcome using six predictors viz: sex, age, history, specific IgE, total IgE minus specific IgE, and skin prick test. This model was later updated by Klemans, reducing the number of predictors enhancing the calibration and discrimination of outcome.

Objective: Our aim was to revalidate both the models for assessment of egg and milk allergies among Indians in the age group 0-19 years and to determine regression coefficients for our study population.

Methods: Revalidation was done at the allergy clinic using OFC outcomes of egg and milk allergic patients. Precise values of the predictors were set up for which calibration (predicted against observed outcome) and discrimination (area under curve [AUC] of receiver operator characteristic curve [ROC]) would be better.

Results: The Klemans model with reduced number of predictors showed better accuracy, calibration and discrimination than the DunnGalvin. Best calibration for egg allergy was achieved in the Klemans model with correlation coefficient (r) of 0.90 and accuracy of 97%. The AUC of ROC was 0.90. For milk allergy, the coefficient was 0.94 with accuracy of 98%. The AUC was 0.91.

Conclusion: The present study showed that mathematical models are non-invasive and can be successfully used as appropriate alternative to OFC in Indian population after proper validation.