Development of a prediction model of treatment response in patients with cutaneous arteritis: Insights from a cohort of 33 patients. Issue 7 (25th March 2021)

• Record Type: Journal Article
• Authors: Matsuda, Kazuki Mitsuru; Yoshizaki, Ayumi; Kotani, Hirohito; Kuzumi, Ai; Fukayama, Maiko; Ebata, Satoshi; Fukasawa, Takemichi; Yoshizaki‐Ogawa, Asako; Sato, Shinichi
• Is Part Of: Journal of dermatology. Volume 48:Issue 7(2021)
• Page Start: 1021
• Page End: 1026
• Publication Date: 2021-03-25
• Subjects: clinical decision rule -- clinical prediction rule -- necrotizing arteritis -- polyarteritis nodosa -- vasculitis; Dermatology -- Periodicals; Dermatology -- Japan -- Periodicals; Skin -- Diseases -- Periodicals; 616.5005
• DOI: 10.1111/1346-8138.15868
• Languages: English
• ISSNs: 0385-2407

Abstract

Abstract: Cutaneous arteritis (CA) is necrotizing vasculitis invading the small‐ to medium‐sized arteries of the skin. The majority of patients can be favorably managed by low‐ to medium‐dose systemic corticosteroids (prednisolone, <0.5 mg/kg/day) or other oral medications such as non‐steroidal anti‐inflammatory drugs, dapsone, and azathioprine. Meanwhile, some patients require more intensive therapy including high‐dose systemic corticosteroids (prednisolone, ≥0.5 mg/kg/day), i.v. immunoglobulin, and i.v. cyclophosphamide therapy. Although predicting such treatment response among CA patients is critical in clinical decision‐making, prediction rules have not yet been established. Herein, we retrospectively reviewed 33 patients regularly visiting our clinic to reveal predictive factors of their treatment response. Clinical data were collected from electronic medical records. Association between each factor and treatment response was examined by logistic regression analysis. Progression‐free time was calculated by Kaplan–Meier's method and analyzed by log–rank test and Cox progression hazard model. Potential predictive factors were selected, given 1 point for each, and integrated into a classification model. Discrimination of the model was examined by the receiver operating characteristic (ROC) curve analysis. In total, 33 CA patients were enrolled in our study. Of these, 11 patients required intensive therapy, classified as treatment non‐responders. Logistic analyses revealed that treatment response was significantly associated with male sex, presence of skin ulcers, and elevated serum levels of C‐reactive protein at the initial work‐up. Kaplan–Meier analyses also demonstrated that those factors are predictive of progression‐free time. The area under the ROC curve of our classification model was 0.92 (95% confidence interval, 0.83–1.00), which classified non‐responders from the others with a sensitivity of 90.9% and specificity of 81.8% at the cut‐off point of 2 or more. Collectively, treatment response of CA could be predictable by a combination of sex, presence of skin ulcers, and serum levels of C‐reactive protein.