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  3. Predicting Psoriatic Arthritis in Psoriasis Patients - A Swiss Registry Study.
 

Predicting Psoriatic Arthritis in Psoriasis Patients - A Swiss Registry Study.

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BORIS DOI
10.48620/76184
Date of Publication
April 2024
Publication Type
Article
Division/Institute

Clinic of Dermatology...

Contributor
Nielsen, Mia-Louise
Petersen, Troels C
Maul, Lara Valeska
Thyssen, Jacob P
Thomsen, Simon F
Wu, Jashin J
Navarini, Alexander A
Kündig, Thomas
Yawalkar, Nikhil
Clinic of Dermatology
Schlapbach, Christoph
Clinic of Dermatology
Boehncke, Wolf-Henning
Conrad, Curdin
Cozzio, Antonio
Micheroli, Raphael
Erik Kristensen, Lars
Egeberg, Alexander
Maul, Julia-Tatjana
Subject(s)

600 - Technology::610...

Series
Journal of Psoriasis and Psoriatic Arthritis
ISSN or ISBN (if monograph)
2475-5303
Publisher
SAGE Publications
Language
English
Publisher DOI
10.1177/24755303231217492
PubMed ID
39295895
Uncontrolled Keywords

classification

machine learning

predictive models

psoriasis

psoriatic arthritis

real word

registry

statistics

Description
Background
Psoriatic arthritis (PsA) is a prevalent comorbidity among patients with psoriasis, heavily contributing to their burden of disease, usually diagnosed several years after the diagnosis of psoriasis.Objectives
To investigate the predictability of psoriatic arthritis in patients with psoriasis and to identify important predictors.Methods
Data from the Swiss Dermatology Network on Targeted Therapies (SDNTT) involving patients treated for psoriasis were utilized. A combination of gradient-boosted decision trees and mixed models was used to classify patients based on their diagnosis of PsA or its absence. The variables with the highest predictive power were identified. Time to PsA diagnosis was visualized with the Kaplan-Meier method and the relationship between severity of psoriasis and PsA was explored through quantile regression.Results
A diagnosis of psoriatic arthritis was registered at baseline of 407 (29.5%) treatment series. 516 patients had no registration of PsA, 257 patients had PsA at inclusion, and 91 patients were diagnosed with PsA after inclusion. The model's AUROCs was up to 73.7%, and variables with the highest discriminatory power were age, PASI, physical well-being, and severity of nail psoriasis. Among patients who developed PsA after inclusion, significantly more first treatment series were classified in the PsA-group, compared to those with no PsA registration. PASI was significantly correlated with the median burden/severity of PsA ( = .01).Conclusions
Distinguishing between patients with and without PsA based on clinical characteristics is feasible and even predicting future diagnoses of PsA is possible. Patients at higher risk can be identified using important predictors of PsA.
Handle
https://boris-portal.unibe.ch/handle/20.500.12422/103400
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nielsen-et-al-2023-predicting-psoriatic-arthritis-in-psoriasis-patients-a-swiss-registry-study.pdftextAdobe PDF1.09 MBAttribution (CC BY 4.0)publishedOpen
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