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

Background: 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. Methods: 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. Results: 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. Conclusion: The FF algorithm represents the first validated tool to identify FF patients. [ABSTRACT FROM AUTHOR]