Machine learning analysis of pretreatment skin biopsies predicts nonresponse to dupilumab in patients with eczematous dermatitis
While dupilumab has revolutionized the treatment of atopic dermatitis (AD), a subset of patients may fail to respond or worsen after dupilumab initiation. Using a retrospective cohort of 53 dupilumab responders and 17 nonresponders, we developed a logistic regression classifier to predict nonrespons...
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Veröffentlicht in: | British journal of dermatology (1951) 2023-12, Vol.190 (1), p.132-134 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | While dupilumab has revolutionized the treatment of atopic dermatitis (AD), a subset of patients may fail to respond or worsen after dupilumab initiation. Using a retrospective cohort of 53 dupilumab responders and 17 nonresponders, we developed a logistic regression classifier to predict nonresponse using 7 cytokine staining and histological features derived from pretreatment biopsies. Our model demonstrated an accuracy of 95.7%, a sensitivity of 88.2%, a specificity of 98.1% and a PPV of 93.8% for predicting nonresponse using leave-one-out cross-validation, underscoring treatment-relevant immunological heterogeneity in eczema and demonstrating the potential of using machine learning and tissue biomarkers to predict dupilumab nonresponse. |
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ISSN: | 0007-0963 1365-2133 1365-2133 |
DOI: | 10.1093/bjd/ljad389 |