Prediction of disease severity using serum biomarkers in patients with mild-moderate Atopic Dermatitis: A pilot study

In-Seon Lee, Mijung Yeom, Kyuseok Kim, Dae-Hyun Hahm, SeHyun Kang and Hi-Joon Park

PLOS ONE, 2023, vol. 18, issue 11, 1-12

Abstract: Atopic dermatitis (AD) is an inflammatory skin condition that relies largely on subjective evaluation of clinical signs and symptoms for diagnosis and severity assessment. Using multivariate data, we attempted to construct prediction models that can diagnose the disease and assess its severity. We combined data from 28 mild-moderate AD patients and 20 healthy controls (HC) to create random forest models for classification (AD vs. HC) and regression analysis to predict symptom severities. The classification model outperformed the random permutation model significantly (area under the curve: 0.85 ± 0.10 vs. 0.50 ± 0.15; balanced accuracy: 0.81 ± 0.15 vs. 0.50 ± 0.15). Correlation analysis revealed a significant positive correlation between measured and predicted total SCORing Atopic Dermatitis score (SCORAD; r = 0.43), objective SCORAD (r = 0.53), eczema area and severity index scores (r = 0.58, each p

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0293332

DOI: 10.1371/journal.pone.0293332

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