TY - JOUR

T1 - Predicting Psoriatic Arthritis in Psoriasis Patients – A Swiss Registry Study

AU - Nielsen, Mia-Louise

AU - Petersen, Troels C.

AU - Maul, Lara Valeska

AU - Thyssen, Jacob P.

AU - Thomsen, Simon F.

AU - Wu, Jashin J.

AU - Navarini, Alexander A.

AU - Kündig, Thomas

AU - Yawalkar, Nikhil

AU - Schlapbach, Christoph

AU - Boehncke, Wolf-Henning

AU - Conrad, Curdin

AU - Cozzio, Antonio

AU - Micheroli, Raphael

AU - Erik Kristensen, Lars

AU - Egeberg, Alexander

AU - Maul, Julia-Tatjana

N1 - Publisher Copyright: © The Author(s) 2023.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - classification

KW - machine learning

KW - predictive models

KW - psoriasis

KW - psoriatic arthritis

KW - real word

KW - registry

KW - statistics

U2 - 10.1177/24755303231217492

DO - 10.1177/24755303231217492

M3 - Journal article

AN - SCOPUS:85177453220

VL - 9

SP - 41

EP - 50

JO - Journal of Psoriasis and Psoriatic Arthritis

JF - Journal of Psoriasis and Psoriatic Arthritis

SN - 2475-5303

IS - 2

ER -