In Dermatology and therapy
INTRODUCTION : Atopic dermatitis (AD) is a chronic, inflammatory skin disorder that impairs patients' quality of life (QoL). Physician assessment of AD disease severity is determined by clinical scales and assessment of affected body surface area (BSA), which might not mirror patients' perceived disease burden.
METHODS : Using data from an international cross-sectional web-based survey of patients with AD and a machine learning approach, we sought to identify disease attributes with the highest impact on QoL for patients with AD. Adults with dermatologist-confirmed AD participated in the survey between July-September 2019. Eight machine learning models were applied to the data with dichotomised Dermatology Life Quality Index (DLQI) as the response variable to identify factors most predictive of AD-related QoL burden. Variables tested were demographics, affected BSA and affected body areas, flare characteristics, activity impairment, hospitalisation and AD therapies. Three machine learning models, logistic regression model, random forest and neural network, were selected on the basis of predictive performance. Each variable's contribution was computed via importance values from 0 to 100. For relevant predictive factors, further descriptive analyses were conducted to characterise those findings.
RESULTS : In total, 2314 patients completed the survey with mean age 39.2 years (standard deviation 12.6) and average disease duration of 19 years. Measured by affected BSA, 13.3% of patients had moderate-to-severe disease. However, 44% of patients reported a DLQI > 10, indicative of a very large to extremely large impact on QoL. Activity impairment was the most important factor predicting high QoL burden (DLQI > 10) across models. Hospitalisation during the past year and flare type were also highly ranked. Current BSA involvement was not a strong predictor of AD-related QoL impairment.
CONCLUSIONS : Activity impairment was the single most important factor for AD-related QoL impairment while current extent of AD did not predict higher disease burden. These results support the importance of considering patients' perspectives when determining the severity of AD.
Paul Carle, Griffiths Christopher E M, Costanzo Antonio, Herranz Pedro, Grond Susanne, Mert Can, Tietz Nicole, Riedl Elisabeth, Augustin Matthias
2023-Mar-02
Atopic dermatitis, Machine learning methods, Quality of life
