医学
算法
置信区间
特应性皮炎
银屑病
尤登J统计
内科学
过敏性
胃肠病学
皮肤病科
免疫病理学
预测值
数学
作者
Svenja Müller,Thomas Welchowski,Matthias Schmid,Laura Maintz,Nadine Herrmann,Dagmar Wilsmann‐Theis,Thorben Royeck,Regina Havenith,Thomas Bieber
出处
期刊:Allergy
[Wiley]
日期:2023-10-21
卷期号:79 (1): 164-173
被引量:4
摘要
Abstract 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.
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