Exploring Machine Learning Techniques to Predict the Response to Omalizumab in Chronic Spontaneous Urticaria

Elevated baseline soluble FcεRI may be linked to early response to omalizumab treatment in chronic spontaneous urticaria

BACKGROUND

Omalizumab, an anti-IgE monoclonal antibody, is an effective treatment in chronic spontaneous urticaria (CSU). Predictors of fast and good response for omalizumab treatment have not yet been identified and characterized.

OBJECTIVE

To evaluate whether soluble FcεRI (sFcεRI), a marker of IgE-mediated mast cell activation, predicts the time of response to omalizumab in CSU.

METHODS

Sera of 67 CSU patients were obtained before omalizumab treatment and analysed for sFcεRI levels by ELISA (2 ng/mL was used as cut-off for elevated sFcɛRI). Treatment response during the first 4 weeks was assessed with the urticaria activity score (UAS7), urticaria control test (UCT) and the rolling UAS7 (rUAS7).

RESULTS

Elevated pre-treatment sFcɛRI levels were detected in more than 70% of patients with completely controlled disease (UCT = 16) and well-controlled disease (UCT = 12-15) and were significantly associated with disease control (χ2  = 4.94, p < 0.05). More than half of the patients (14/25) with low levels had poor disease control (UCT < 12). Of the patients who achieved complete and marked UAS7 response, respectively, 75% and 63% had elevated baseline sFcɛRI levels. Post-treatment UAS7 scores were lower in patients with elevated sFcɛRI levels reaching statistical significance at Week 3 (p < 0.05). Patients with elevated baseline sFcɛRI levels achieved rUAS7 ≤ 6 and = 0 earlier than those with lower levels (Days 9 vs. 13 and Days 12 vs. 14, respectively).

CONCLUSION

Elevated sFcεRI serum levels predict early and good response to treatment with omalizumab, which may help to better design treatment options for CSU patients.

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

PURPOSE OF REVIEW

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.

RECENT FINDINGS

In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.