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Predicting Psoriatic Arthritis in Psoriasis Patients – A Swiss Registry Study

Authors :
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
Schlapbach, Christoph
Boehncke, Wolf-Henning
Conrad, Curdin
Cozzio, Antonio
Micheroli, Raphael
Erik Kristensen, Lars
Egeberg, Alexander
Maul, Julia-Tatjana
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
Schlapbach, Christoph
Boehncke, Wolf-Henning
Conrad, Curdin
Cozzio, Antonio
Micheroli, Raphael
Erik Kristensen, Lars
Egeberg, Alexander
Maul, Julia-Tatjana
Source :
Nielsen , M-L , Petersen , T C , Maul , L V , Thyssen , J P , Thomsen , S F , Wu , J J , Navarini , A A , Kündig , T , Yawalkar , N , Schlapbach , C , Boehncke , W-H , Conrad , C , Cozzio , A , Micheroli , R , Erik Kristensen , L , Egeberg , A & Maul , J-T 2024 , ' Predicting Psoriatic Arthritis in Psoriasis Patients – A Swiss Registry Study ' , Journal of Psoriasis and Psoriatic Arthritis , vol. 9 , no. 2 , pp. 41-50 .
Publication Year :
2024

Abstract

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 (P = .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.<br />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 (P =.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.

Details

Database :
OAIster
Journal :
Nielsen , M-L , Petersen , T C , Maul , L V , Thyssen , J P , Thomsen , S F , Wu , J J , Navarini , A A , Kündig , T , Yawalkar , N , Schlapbach , C , Boehncke , W-H , Conrad , C , Cozzio , A , Micheroli , R , Erik Kristensen , L , Egeberg , A & Maul , J-T 2024 , ' Predicting Psoriatic Arthritis in Psoriasis Patients – A Swiss Registry Study ' , Journal of Psoriasis and Psoriatic Arthritis , vol. 9 , no. 2 , pp. 41-50 .
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1439556049
Document Type :
Electronic Resource