Development and Validation of an Artificial Intelligence-Driven Model for Accurate Classification of Erythrodermic Psoriasis Severity: Erythrodermic Psoriasis Integrated Classification System (EPICS).

Abstract

Background: Erythrodermic psoriasis is a rare subtype of psoriasis with widespread skin lesions, with some patients experiencing severe systemic symptoms.

Objective: We aimed to develop and validate an artificial intelligence-driven model for accurate classification of erythrodermic psoriasis severity by integrating clinical and laboratory indicators.

Methods: A retrospective cohort study was conducted at Peking Union Medical College Hospital (2005-22). Patients were divided into mild and moderate-to-severe groups using k-means clustering. After imputing missing values, we trained seven candidate algorithms-K-Nearest Neighbors, Artificial Neural Network, Random Forest, Extreme Gradient Boosting, Support Vector Machine, Bayesian classifier, and logistic regression-using repeated, stratified ten-fold cross-validation with three repeats (10 × 3 CV); performance was summarized by the mean area under the receiver operating characteristic curve across folds. Feature importance was assessed using SHAP (Shapley Additive exPlanations), a game-theoretic approach that quantifies each features contribution to individual model predictions, ten indicators were incorporated into a diagnostic scoring system. The optimal cut-off for mild/moderate-to-severe cases classification was selected with the Youden index on the cross-validated receiver operating characteristic curve.

Results: Of 260 screened records, 242 erythrodermic patients met the study criteria. Histology confirmed psoriasis in 108 cases, while the remaining patients were diagnosed based on clinical presentation and medical history. K-means clustering assigned 94 patients to the moderate-to-severe group and 148 to the mild group. Moderate-to-severe erythrodermic psoriasis was characterized by a higher inflammatory burden (median neutrophil-to-lymphocyte ratio 4.11 vs 2.70, p < 0.001), more frequent fever (88% vs 41%, p < 0.001), greater edema severity (16% vs 1.4%, p < 0.001), lower albumin and higher calcium levels (both p < 0.001), and longer hospitalization (median 26 vs 20 days, p = 0.005). After adjustment for age and sex, moderate-to-severe cases required systemic therapy roughly twice as often as mild cases (odds ratio 2.21, p < 0.05). Of seven machine-learning algorithms, the Artificial Neural Network yielded the highest mean validation area under the curve. The SHAP analysis highlighted the ten most influential predictors adopted from the Artificial Neural Network-edema, edematous erythema (defined as the combination of both redness and swelling of the skin), fever, albumin, neutrophil-to-lymphocyte ratio, serum calcium, white blood cell count, acute-phase reactants (C-reactive protein or erythrocyte sedimentation rate), pruritus, and superficial lymphadenopathy-and these were converted to integer points to form the bedside score. The receiver operating characteristic analysis identified 33.5 points as the optimal threshold for distinguishing between mild and moderate-to-severe cases. The model, named 'EPICS' (Erythrodermic Psoriasis Integrated Classification System), effectively stratified patients, as evidenced by internal validation. This model is currently available online ( https://pumch-dermatology.shinyapps.io/classification/ ).

Conclusions: The EPICS model is a robust tool for assessing erythrodermic psoriasis severity, offering precise classification based on easily accessible clinical and laboratory indicators. However, its effectiveness in clinical practice requires further validation through additional research.