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Psoriasis to Psoriatic Arthritis: Interim Results of the PRESTO Study

Nov 06, 2020 7:14 am

Early diagnosis of arthritis improves outcomes and quality of life in patients with psoriasis. A longitudinal cohort study shows that the development of PsA within clinically meaningful time frames can be predicted with reasonable accuracy for psoriasis patients (Abstract #0310, Poster Session A).

In this interim report of the Prediction of Psoriatic Arthritis Tool (PRESTO) study, Prof. Lihi Eder and colleagues analysed data from psoriasis patients without PsA at the time of enrollment in the International Psoriasis and Arthritis Team (IPART) study cohort. Patients were prospectively followed and their PsA status assessed by a rheumatologist annually. A prediction model was developed using data on demographics, psoriasis characteristics, co-morbidities and musculoskeletal symptoms. It was set to estimate the risk of developing PsA over 1 and 5 years respectively. 

Of the 635 psoriasis patients, 75 patients developed PsA. The following candidate risk factors for PsA were identified: severity of musculoskeletal symptoms, psoriatic nail lesions, iritis, psoriasis type, location and severity and the use of non-biologic systemic medications or phototherapy. Younger age, patient global health and pain severity predicted the risk of developing PsA within a year (AUC 76.8, 95% confidence interval (CI) 68.5, 85.1). On the other hand, psoriatic nail lesion, health assessment questionnaire disability index (HAQ-DI), Functional Assessment of Chronic Illness Therapy (FACIT) fatigue scale and use of systemic non-biologic medication or phototherapy (AUC 71.9, 95% CI 65.2, 78.6) were the predictors for developing PsA within 5 years. 

Development of a simple prediction tool can identify psoriasis patients at high risk for developing PsA and ultimately lead to timely intervention. This interim report adds to our knowledge of how a proportion of patients with psoriasis eventually develop PsA. It has set the groundwork for further validation of these prediction models and we await further results as we continue to improve the quality of life of these patients. 


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