Alexis Ogdie1, Ryan W Harrison2, Robert R McLean2, Tin-Chi Lin2, Mark Lebwohl3, Bruce E Strober4, Joe Zhuo5, Vardhaman Patel5, Philip J Mease6. 1. Departments of Medicine/Rheumatology and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: OgdieA@pennmedicine.upenn.edu. 2. CorEvitas, LLC (formerly known as Corrona, LLC), Waltham, Massachusetts. 3. Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New York. 4. Department of Dermatology, Yale University, New Haven, Connecticut. 5. Bristol Myers Squibb, Princeton, New Jersey. 6. Rheumatology, Swedish Medical Center/Providence St. Joseph Health and University of Washington, Seattle, Washington.
Abstract
BACKGROUND: The characteristics that predict the onset of psoriatic arthritis (PsA) among patients with psoriasis (PsO) may inform diagnosis and treatment. OBJECTIVE: To develop a model to predict the 2-year risk of developing PsA among patients with PsO. METHODS: 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. RESULTS: 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%). LIMITATIONS: PsA misclassification bias by dermatologists. CONCLUSION: 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.
BACKGROUND: The characteristics that predict the onset of psoriatic arthritis (PsA) among patients with psoriasis (PsO) may inform diagnosis and treatment. OBJECTIVE: To develop a model to predict the 2-year risk of developing PsA among patients with PsO. METHODS: 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. RESULTS: 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%). LIMITATIONS: PsA misclassification bias by dermatologists. CONCLUSION: 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.