# Influencing factor analysis and prediction model construction of dupilumab treatment adherence: a prospective cohort study in moderate-to-severe atopic dermatitis

## TL;DR

This study identifies factors affecting adherence to dupilumab treatment for atopic dermatitis and builds a model to predict adherence for better patient management.

## Contribution

A multidimensional adherence prediction model for dupilumab treatment in atopic dermatitis using machine learning and clinical factors.

## Key findings

- Age, baseline EASI/NRS scores, and treatment response (EASI-75/SLS-75) independently predict adherence.
- Machine learning highlighted EASI/NRS and EASI-75/SLS-75 as key adherence predictors.
- A nomogram-based model provides personalized adherence risk visualization for precision management.

## Abstract

The efficacy of dupilumab in atopic dermatitis (AD) has been widely validated; however, systematic investigations into treatment adherence are lacking.

To analyze clinical factors influencing dupilumab adherence in patients with moderate-to-severe AD and develop a multidimensional adherence prediction model to support precision management of biologic therapies.

Using a single-center prospective cohort, a three-stage modeling approach was applied: (1) univariable Cox proportional hazards regression to identify potential predictors; (2) XGBoost modeling with SHAP method for feature importance ranking and dimensionality reduction; (3) multivariable Cox proportional hazards model for final prediction.

Univariable analysis indicated that treatment discontinuation was significantly associated with age, sex, combination therapy, baseline disease activity, and treatment response. Machine learning identified EASI/NRS and EASI-75/SLS-75 as key predictors of baseline disease activity and treatment response, respectively. The multivariable model confirmed independent predictive value for age, baseline EASI/NRS scores, and achievement of EASI-75/SLS-75.

This study identified key determinants of dupilumab adherence and developed a predictive adherence model that offers personalized risk visualization via nomograms, providing an evidence-based tool for the precision management of AD biologic therapies.