Development and Validation of An Integrated Molecular Allergy Diagnostic Algorithm for Precision Evaluation of Children with Atopic Dermatitis

Abstract

Background

Atopic dermatitis (AD) is one of the most prevalent chronic inflammatory skin diseases in children and represents the initial manifestation of the atopic march. Conventional allergy diagnostic methods, including total serum immunoglobulin E (IgE) measurement and extract-based allergen-specific IgE testing, frequently fail to distinguish genuine allergen sensitization from immunological cross-reactivity, thereby limiting diagnostic precision and individualized patient management. Component-resolved molecular allergy diagnostics has emerged as an advanced strategy for precision allergology; however, evidence-based diagnostic algorithms integrating molecular diagnostics with routine clinical assessment remain limited.

Objective

To develop and clinically validate a comprehensive molecular diagnostic algorithm integrating clinical assessment, conventional laboratory biomarkers, and component-resolved molecular allergy diagnostics for improving the identification of clinically relevant allergen sensitization and optimizing personalized management in children with atopic dermatitis.

Methods

In this prospective observational study, 200 children with clinically confirmed atopic dermatitis were consecutively enrolled between January 2022 and December 2024 at the Republican Scientific Specialized Allergology Center (Tashkent, Uzbekistan). Disease severity was evaluated using the SCORAD index. Routine laboratory investigations included complete blood count, eosinophil count, and total serum IgE measurement. Component-resolved molecular allergy diagnostics was performed using the ImmunoCAP® and ALEX® platforms. Based on clinical, laboratory, and molecular findings, a novel six-step diagnostic algorithm was developed and its diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall diagnostic accuracy.

Results

Molecular sensitization was identified in 182 of 200 children (91.0%), whereas polysensitization was observed in 126 patients (63.0%). House dust mite molecular components (Der p 1, Der p 2, and Der p 23) represented the predominant sensitization profile. Implementation of the proposed algorithm enabled identification of clinically relevant allergen sensitization in 179 children (89.5%) and establishment of individualized molecular sensitization profiles in 148 patients (74.0%). Compared with conventional diagnostic methods, the proposed algorithm significantly improved identification of genuine sensitization (72.0% vs. 91.0%; p < 0.001) and recognition of immunological cross-reactivity (55.0% vs. 89.0%; p < 0.001). Overall diagnostic accuracy increased from 76.5% to 92.5%. The algorithm demonstrated excellent diagnostic performance, with a sensitivity of 91.3%, specificity of 87.5%, PPV of 93.2%, NPV of 84.0%, and an AUC of 0.93 (95% CI: 0.89–0.96).

Conclusions

The proposed six-step molecular diagnostic algorithm substantially improves diagnostic precision in pediatric atopic dermatitis by integrating clinical evaluation, conventional laboratory biomarkers, and component-resolved molecular allergy diagnostics. The algorithm accurately differentiates genuine allergen sensitization from immunological cross-reactivity, supports individualized molecular sensitization profiling, and provides a practical framework for implementing precision medicine in routine pediatric allergy practice. External multicenter validation is warranted to confirm its applicability across diverse populations.

Keywords:

Atopic dermatitis, component-resolved diagnostics, molecular allergy diagnostics

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