Development of a clinical algorithm to predict phenotypic switches between atopic dermatitis and psoriasis (the "Flip-Flop" phenomenon).

Abstract

Atopic dermatitis (AD) and psoriasis vulgaris (PV) are almost mutually exclusive diseases with different immune polarizations, mechanisms and therapeutic targets. Switches to the other disease ("Flip-Flop" [FF] phenomenon) can occur with or without systemic treatment and are often referred to as paradoxical reactions under biological therapy. The objective was to develop a diagnostic algorithm by combining clinical criteria of AD and PV to identify FF patients. The algorithm was prospectively validated in patients enrolled in the CK-CARE registry in Bonn, Germany. Afterward, algorithm refinements were implemented based on machine learning. Three hundred adult Caucasian patients were included in the validation study (n = 238 with AD, n = 49 with PV, n = 13 with FF; mean age 41.2 years; n = 161 [53.7%] female). The total FF scores of the PV and AD groups differed significantly from the FF group in the validation data (p < .001). The predictive mean generalized Youden-Index of the initial model was 78.9% [95% confidence interval 72.0%-85.6%] and the accuracy was 89.7%. Disease group-specific sensitivity was 100% (FF), 95.0% (AD), and 61.2% (PV). The specificity was 89.2% (FF), 100% (AD), and 100% (PV), respectively. The FF algorithm represents the first validated tool to identify FF patients.