Novel predictive model for adrenal insufficiency in dermatological patients with topical corticosteroids use: A cross-sectional study

Abstract

Purpose: This study aimed to identify predictive factors and to develop a predictive model for adrenal insufficiency (AI) related to topical corticosteroids use. Methods: The research was conducted using a cross-sectional design. Adult patients with dermatological conditions who had been prescribed topical steroids for at least 12 months by the dermatology outpatient departments of the Faculty of Medicine, Chiang Mai University from June through October 2020 were included. Data on potential predictors, including baseline characteristics and laboratory investigations, were collected. The diagnoses of AI were based on serum 8AM cortisol and low-dose ACTH stimulation tests. Multivariable logistic regression was used for the derivation of the diagnostic score. Results: Of the 42 patients, 17 (40.5%) had AI. The statistically significant predictive factors for AI were greater body surface area of corticosteroids use, age <60 years, and basal serum cortisol <7 µg/dL. In the final predictive model, duration of treatment was added as a factor based on its clinical significance for AI. The four predictive factors with their assigned scores were: body surface area involvement 10–30% (20), >30% (25); age <60 years old (15); basal serum cortisol of <7 µg/dL (30); and duration of treatment in years. Risk of AI was categorized into three groups, low, intermediate and high risk, with total scores of <25, 25–49 and ≥50, respectively. The predictive performance for the model was 0.92 based on area under the curve. Conclusion: The predictive model for AI in patients using topical corticosteroids provides guidance on the risk of AI to determine which patients should have dynamic ACTH stimulation tests (high risk) and which need only close follow-up (intermediate and low risk). Future validation of the model is warranted.