Prospective cohort study of psoriatic arthritis risk in patients with psoriasis in a real-world psoriasis registry

BackgroundThe characteristics that predict the onset of psoriatic arthritis (PsA) among patients with psoriasis (PsO) may inform diagnosis and treatment.ObjectiveTo develop a model to predict the 2-year risk of developing PsA among patients with PsO.MethodsThis was a prospective cohort study of patients in the CorEvitas Psoriasis Registry without PsA at enrollment and with 24-month follow-up. Unregularized and regularized logistic regression models were developed and tested using descriptive variables to predict dermatologist-identified PsA at 24 months. Model performance was compared using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.ResultsA total of 1489 patients were included. Nine unique predictive models were developed and tested. The optimal model, including Psoriasis Epidemiology Screening Tool (PEST), body mass index (BMI), modified Rheumatic Disease Comorbidity Index, work status, alcohol use, and patient-reported fatigue, predicted the onset of PsA within 24 months (AUC = 68.9%, sensitivity = 82.9%, specificity = 48.8%). A parsimonious model including PEST and BMI had similar performance (AUC = 68.8%; sensitivity = 92.7%, specificity = 36.5%).LimitationsPsA misclassification bias by dermatologists.ConclusionPEST and BMI were important factors in predicting the development of PsA in patients with PsO over 2 years and thereby foundational for future PsA risk model development. The characteristics that predict the onset of psoriatic arthritis (PsA) among patients with psoriasis (PsO) may inform diagnosis and treatment. To develop a model to predict the 2-year risk of developing PsA among patients with PsO. This was a prospective cohort study of patients in the CorEvitas Psoriasis Registry without PsA at enrollment and with 24-month follow-up. Unregularized and regularized logistic regression models were developed and tested using descriptive variables to predict dermatologist-identified PsA at 24 months. Model performance was compared using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. A total of 1489 patients were included. Nine unique predictive models were developed and tested. The optimal model, including Psoriasis Epidemiology Screening Tool (PEST), body mass index (BMI), modified Rheumatic Disease Comorbidity Index, work status, alcohol use, and patient-reported fatigue, predicted the onset of PsA within 24 months (AUC = 68.9%, sensitivity = 82.9%, specificity = 48.8%). A parsimonious model including PEST and BMI had similar performance (AUC = 68.8%; sensitivity = 92.7%, specificity = 36.5%). PsA misclassification bias by dermatologists. PEST and BMI were important factors in predicting the development of PsA in patients with PsO over 2 years and thereby foundational for future PsA risk model development.