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

Davide Stefano Sardina,Francesco Papia,Giuseppe Valenti,Carina Gabriela Uasuf

doi:10.3390/diagnostics11112150

Abstract

Background: Omalizumab is the best treatment for patients with chronic spontaneous urticaria (CSU). Machine learning (ML) approaches can be used to predict response to therapy and the effectiveness of a treatment. No studies are available on the use of ML techniques to predict the response to Omalizumab in CSU. Methods: Data from 132 CSU outpatients were analyzed. Urticaria Activity Score over 7 days (UAS7) and treatment efficacy were assessed. Clinical and demographic characteristics were used for training and validating ML models to predict the response to treatment. Two methodologies were used to label the data based on the response to treatment (UAS7 ≥ 6): (A) at 1, 3 and 5 months; (B) classifying the patients as early responders (ER), late responders (LR) or non-responders (NR) (ER: UAS 7 ≥ 6 at first month, LR: UAS 7 ≥ 6 at third month, NR: if none of the previous conditions occurred). Results: ER were predominantly characterized by hypertension, while LR mainly suffered from asthma and hypothyroidism. A slight positive correlation (R2 = 0.21) was found between total IgE levels and UAS7 at 1 month. Variable Importance Analysis (VIA) reported D-dimer and C-reactive proteins as the key blood tests for the performance of learning techniques. Using methodology (A), SVM (specificity of 0.81) and k-NN (sensitivity of 0.8) are the best models to predict LR at the third month. Conclusion: k-NN plus the SVM model could be used to identify the response to treatment. D-dimer and C-reactive proteins have greater predictive power in training ML models.

Highlights

Chronic spontaneous urticaria (CSU) is defined by the spontaneous occurrence of wheals, angioedema or both that last longer than 6 weeks
Urticaria Activity Score over 7 days (UAS7) was considered equal or greater than 6 to classify chronic spontaneous urticaria (CSU) patients as “early responders” (ER; if they started to respond at 1 month and remained in this condition until the fifth month), and “late responders” (LR; if the response was achieved at the third month)
Techniques in allergies is still being explored [27]. This is the first time that a study explores the potential of Machine learning (ML) approaches in predicting the response to omalizumab in patients with chronic spontaneous urticaria (CSU)

Summary

Introduction

Chronic spontaneous urticaria (CSU) is defined by the spontaneous occurrence of wheals, angioedema or both that last longer than 6 weeks. It affects 1% of the general population, has an unpredictable course and duration, and 11–14% of the patients suffer for more than 5 years [1]. According to the EAACI/GA2 LEN/EDF/WAO guideline for urticaria, the first-line therapy is secondgeneration H1-antihistamines in standard dose, but, these are effective in less than 50% of CSU patients [4]. The third-line therapy, omalizumab, an anti-IgE monoclonal antibody, is more effective with a complete response rate that ranges from 26%

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