Predictive modeling for cow's milk allergy remission by low-dose oral immunotherapy in young children

Abstract

Background: The effectiveness of slow low-dose oral immunotherapy (SLOIT) for cow's milk (CM) allergy has been reported. Most OIT studies have discussed the target populations over 4 years old. Furthermore, no predicting modeling is reported for CM allergy remission by CM-SLOIT under 4 years of age. Objective: We sought to develop a predictive model for CM allergy remission by SLOIT after 3 years in young children who started CM-SLOIT under 4 years of age. Methods: We included young children with cow's milk allergy or cow's milk sensitization (development modeling set with 120 children and validation modeling set with 71 children). We did logistic regression analysis to develop the models. We calculated the area under the receiver operating curves (ROC-AUCs) to evaluate the predictive modeling performance. Results: The model (CM-sIgE before SLOIT + age at beginning SLOIT + serum TARC before starting SLOIT + CM-sIgE titer one year after OIT) showed good discrimination with the ROC-AUC of 0.83 (95% CI:0.76–0.91) on internal validation. Applying the model to the validation set gave good discrimination (ROC-AUC = 0.89, 95% CI:0.80–0.97) and a reasonable calibration (intraclass correlation coefficient = 0.88, 95% CI:0.62–0.97). Conclusion: We developed and validated predictive modeling for determining the remission rate of CM allergy at 3 years after SLOIT under 4 years of age in children with CM allergy. This predictive model is highly accurate and can support CM allergy management. (226 words)

Keywords

• Allergy
• Cow's milk
• Immunotherapy
• Predictive modeling
• Remission