Establishment and preliminary validation of a noninvasive diagnostic model for food allergy in infants

Infants are at a high risk of food allergy (FA); however, owing to the lack of commonly recognized simple and noninvasive diagnostic tools, definitive diagnosis of infantile FA is challenging. In this study, we aimed to establish a diagnostic model comprising highly suggestive indicators to facilitate the early identification of infantile FA.

In this case-control study, we enrolled two groups of infants with suspected FA. FA diagnoses were confirmed through oral food challenges (OFCs). The training set, which included infants enrolled between 2022 and 2023, was used to develop a logistic regression diagnostic model and perform internal cross-validation. The testing set comprising previous cases between 2016 and 2021 was used to perform external validation. We assessed the discrimination and calibration of the diagnostic model using the area under the curve (AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis (DCA).

We identified variables for the diagnostic model, including eczema, stool form, hematochezia, failure to thrive (FTT), respiratory symptoms, and family history of allergic diseases. The AUCs for the training set, internal cross-validation, and external validation were 0.86 (0.83-0.90), 0.86 (0.76-0.95), and 0.87 (0.83-0.92), respectively, showing good diagnostic performance of the model. The Hosmer-Lemeshow goodness-of-fit test results and calibration curve showed that the model had good calibration. DCA results showed a high net benefit value in clinical decision-making.

The diagnostic model, constructed with the six aforementioned variables, can serve as a simple and noninvasive diagnostic tool for clinicians to effectively distinguish FA from similar diseases.