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 referre...
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| Veröffentlicht in: | Allergy (Copenhagen) 2024-01, Vol.79 (1), p.164-173 |
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| creator | Müller, Svenja Welchowski, Thomas Schmid, Matthias Maintz, Laura Herrmann, Nadine Wilsmann‐Theis, Dagmar Royeck, Thorben Havenith, Regina Bieber, Thomas |
| description | 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 |
| doi_str_mv | 10.1111/all.15921 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2880101119</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2908006470</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3881-73d5ad444055ca5c34759f39a20501fea3da789a93cbfeacec34f9077646b3d23</originalsourceid><addsrcrecordid>eNp1kUFOGzEUhq2KqqS0Cy5QWWIDiwF7PM7YSwRNixSJDawtx_OGGHnGU9shyi4XQOoB4HI5SQ1Du6iEN9azPn3P-n-EDik5pfmcaedOKZcl_YAmlElRSCn5HpoQSnhRcSb20ecY7wkhdSnJJ7TPajGtmCQT9HgJD-D80EGfsG-xxsbZ3hrtsHZ3Pti07HDyeAjQWJPwsITep81gDY5rm8wSIl5AWgP0WCf_8t5A6HSyyUas-wYPMVt0zNNxWgLebZ9mzg677e9ZXrvbPo_KvN_3J1_Qx1a7CF_f7gN0O_t-c_GzmF__uLo4nxeGCUGLmjVcN1VVEc6N5oZVNZctk7oknNAWNGt0LaSWzCzyZCATrSR1Pa2mC9aU7AAdj94h-F8riEl1NhpwTvfgV1GVQuTscrQyo0f_ofd-Ffr8O5WzFIRMq5pk6mSkTPAxBmjVEGynw0ZRol46Urkj9dpRZr-9GVeLDpp_5N9SMnA2AmvrYPO-SZ3P56PyD8VOn7I</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2908006470</pqid></control><display><type>article</type><title>Development of a clinical algorithm to predict phenotypic switches between atopic dermatitis and psoriasis (the “Flip‐Flop” phenomenon)</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>Müller, Svenja ; Welchowski, Thomas ; Schmid, Matthias ; Maintz, Laura ; Herrmann, Nadine ; Wilsmann‐Theis, Dagmar ; Royeck, Thorben ; Havenith, Regina ; Bieber, Thomas</creator><creatorcontrib>Müller, Svenja ; Welchowski, Thomas ; Schmid, Matthias ; Maintz, Laura ; Herrmann, Nadine ; Wilsmann‐Theis, Dagmar ; Royeck, Thorben ; Havenith, Regina ; Bieber, Thomas</creatorcontrib><description>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.
Phenotypic switches from AD to PV or vice versa can occur spontaneouslyor during biologic therapy (“paradoxical reactions”) in predisposed patients (so‐called “Flip‐Flop” (FF) patients). We developed the first clinical algorithm to identify FF patients by the combination of medical history and examination criteria typical for AD and PV. The validation study with 300 Caucasian patients with AD, PV and FF showed a good prediction accuracy (89.7%). The model was improved using machine learning.Abbreviations: AD, atopic dermatitis; FF, Flip‐Flop; PV, psoriasis vulgaris</description><identifier>ISSN: 0105-4538</identifier><identifier>ISSN: 1398-9995</identifier><identifier>EISSN: 1398-9995</identifier><identifier>DOI: 10.1111/all.15921</identifier><identifier>PMID: 37864390</identifier><language>eng</language><publisher>Denmark: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Atopic dermatitis ; Dermatitis ; differential diagnosis ; Eczema ; Medical diagnosis ; Patients ; Precision medicine ; Psoriasis ; Psoriasis vulgaris ; Therapeutic targets ; validation study</subject><ispartof>Allergy (Copenhagen), 2024-01, Vol.79 (1), p.164-173</ispartof><rights>2023 The Authors. published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.</rights><rights>2023 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.</rights><rights>2023. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3881-73d5ad444055ca5c34759f39a20501fea3da789a93cbfeacec34f9077646b3d23</citedby><cites>FETCH-LOGICAL-c3881-73d5ad444055ca5c34759f39a20501fea3da789a93cbfeacec34f9077646b3d23</cites><orcidid>0000-0003-4924-2281 ; 0000-0003-2940-647X ; 0000-0002-8800-3817 ; 0000-0001-6053-1530 ; 0000-0002-2118-959X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fall.15921$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fall.15921$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37864390$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Müller, Svenja</creatorcontrib><creatorcontrib>Welchowski, Thomas</creatorcontrib><creatorcontrib>Schmid, Matthias</creatorcontrib><creatorcontrib>Maintz, Laura</creatorcontrib><creatorcontrib>Herrmann, Nadine</creatorcontrib><creatorcontrib>Wilsmann‐Theis, Dagmar</creatorcontrib><creatorcontrib>Royeck, Thorben</creatorcontrib><creatorcontrib>Havenith, Regina</creatorcontrib><creatorcontrib>Bieber, Thomas</creatorcontrib><title>Development of a clinical algorithm to predict phenotypic switches between atopic dermatitis and psoriasis (the “Flip‐Flop” phenomenon)</title><title>Allergy (Copenhagen)</title><addtitle>Allergy</addtitle><description>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.
Phenotypic switches from AD to PV or vice versa can occur spontaneouslyor during biologic therapy (“paradoxical reactions”) in predisposed patients (so‐called “Flip‐Flop” (FF) patients). We developed the first clinical algorithm to identify FF patients by the combination of medical history and examination criteria typical for AD and PV. The validation study with 300 Caucasian patients with AD, PV and FF showed a good prediction accuracy (89.7%). The model was improved using machine learning.Abbreviations: AD, atopic dermatitis; FF, Flip‐Flop; PV, psoriasis vulgaris</description><subject>Algorithms</subject><subject>Atopic dermatitis</subject><subject>Dermatitis</subject><subject>differential diagnosis</subject><subject>Eczema</subject><subject>Medical diagnosis</subject><subject>Patients</subject><subject>Precision medicine</subject><subject>Psoriasis</subject><subject>Psoriasis vulgaris</subject><subject>Therapeutic targets</subject><subject>validation study</subject><issn>0105-4538</issn><issn>1398-9995</issn><issn>1398-9995</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNp1kUFOGzEUhq2KqqS0Cy5QWWIDiwF7PM7YSwRNixSJDawtx_OGGHnGU9shyi4XQOoB4HI5SQ1Du6iEN9azPn3P-n-EDik5pfmcaedOKZcl_YAmlElRSCn5HpoQSnhRcSb20ecY7wkhdSnJJ7TPajGtmCQT9HgJD-D80EGfsG-xxsbZ3hrtsHZ3Pti07HDyeAjQWJPwsITep81gDY5rm8wSIl5AWgP0WCf_8t5A6HSyyUas-wYPMVt0zNNxWgLebZ9mzg677e9ZXrvbPo_KvN_3J1_Qx1a7CF_f7gN0O_t-c_GzmF__uLo4nxeGCUGLmjVcN1VVEc6N5oZVNZctk7oknNAWNGt0LaSWzCzyZCATrSR1Pa2mC9aU7AAdj94h-F8riEl1NhpwTvfgV1GVQuTscrQyo0f_ofd-Ffr8O5WzFIRMq5pk6mSkTPAxBmjVEGynw0ZRol46Urkj9dpRZr-9GVeLDpp_5N9SMnA2AmvrYPO-SZ3P56PyD8VOn7I</recordid><startdate>202401</startdate><enddate>202401</enddate><creator>Müller, Svenja</creator><creator>Welchowski, Thomas</creator><creator>Schmid, Matthias</creator><creator>Maintz, Laura</creator><creator>Herrmann, Nadine</creator><creator>Wilsmann‐Theis, Dagmar</creator><creator>Royeck, Thorben</creator><creator>Havenith, Regina</creator><creator>Bieber, Thomas</creator><general>Blackwell Publishing Ltd</general><scope>24P</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T5</scope><scope>H94</scope><scope>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4924-2281</orcidid><orcidid>https://orcid.org/0000-0003-2940-647X</orcidid><orcidid>https://orcid.org/0000-0002-8800-3817</orcidid><orcidid>https://orcid.org/0000-0001-6053-1530</orcidid><orcidid>https://orcid.org/0000-0002-2118-959X</orcidid></search><sort><creationdate>202401</creationdate><title>Development of a clinical algorithm to predict phenotypic switches between atopic dermatitis and psoriasis (the “Flip‐Flop” phenomenon)</title><author>Müller, Svenja ; Welchowski, Thomas ; Schmid, Matthias ; Maintz, Laura ; Herrmann, Nadine ; Wilsmann‐Theis, Dagmar ; Royeck, Thorben ; Havenith, Regina ; Bieber, Thomas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3881-73d5ad444055ca5c34759f39a20501fea3da789a93cbfeacec34f9077646b3d23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Atopic dermatitis</topic><topic>Dermatitis</topic><topic>differential diagnosis</topic><topic>Eczema</topic><topic>Medical diagnosis</topic><topic>Patients</topic><topic>Precision medicine</topic><topic>Psoriasis</topic><topic>Psoriasis vulgaris</topic><topic>Therapeutic targets</topic><topic>validation study</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Müller, Svenja</creatorcontrib><creatorcontrib>Welchowski, Thomas</creatorcontrib><creatorcontrib>Schmid, Matthias</creatorcontrib><creatorcontrib>Maintz, Laura</creatorcontrib><creatorcontrib>Herrmann, Nadine</creatorcontrib><creatorcontrib>Wilsmann‐Theis, Dagmar</creatorcontrib><creatorcontrib>Royeck, Thorben</creatorcontrib><creatorcontrib>Havenith, Regina</creatorcontrib><creatorcontrib>Bieber, Thomas</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Allergy (Copenhagen)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Müller, Svenja</au><au>Welchowski, Thomas</au><au>Schmid, Matthias</au><au>Maintz, Laura</au><au>Herrmann, Nadine</au><au>Wilsmann‐Theis, Dagmar</au><au>Royeck, Thorben</au><au>Havenith, Regina</au><au>Bieber, Thomas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of a clinical algorithm to predict phenotypic switches between atopic dermatitis and psoriasis (the “Flip‐Flop” phenomenon)</atitle><jtitle>Allergy (Copenhagen)</jtitle><addtitle>Allergy</addtitle><date>2024-01</date><risdate>2024</risdate><volume>79</volume><issue>1</issue><spage>164</spage><epage>173</epage><pages>164-173</pages><issn>0105-4538</issn><issn>1398-9995</issn><eissn>1398-9995</eissn><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.
Phenotypic switches from AD to PV or vice versa can occur spontaneouslyor during biologic therapy (“paradoxical reactions”) in predisposed patients (so‐called “Flip‐Flop” (FF) patients). We developed the first clinical algorithm to identify FF patients by the combination of medical history and examination criteria typical for AD and PV. The validation study with 300 Caucasian patients with AD, PV and FF showed a good prediction accuracy (89.7%). The model was improved using machine learning.Abbreviations: AD, atopic dermatitis; FF, Flip‐Flop; PV, psoriasis vulgaris</abstract><cop>Denmark</cop><pub>Blackwell Publishing Ltd</pub><pmid>37864390</pmid><doi>10.1111/all.15921</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-4924-2281</orcidid><orcidid>https://orcid.org/0000-0003-2940-647X</orcidid><orcidid>https://orcid.org/0000-0002-8800-3817</orcidid><orcidid>https://orcid.org/0000-0001-6053-1530</orcidid><orcidid>https://orcid.org/0000-0002-2118-959X</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms Atopic dermatitis Dermatitis differential diagnosis Eczema Medical diagnosis Patients Precision medicine Psoriasis Psoriasis vulgaris Therapeutic targets validation study |
| title | Development of a clinical algorithm to predict phenotypic switches between atopic dermatitis and psoriasis (the “Flip‐Flop” phenomenon) |
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