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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:British journal of dermatology (1951) 2023-12, Vol.190 (1), p.132-134
Hauptverfasser: Murphy, Michael J, Hwang, Erica, Singh, Katelyn, Lee, Trinity, Cohen, Jeffrey M, Damsky, William
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
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.
ISSN:0007-0963
1365-2133
1365-2133
DOI:10.1093/bjd/ljad389