P002 Predicting psoriasis biologic drug discontinuation: an explainable machine learning approach applying British Association of Dermatologists Biologics and Immunomodulators Register data

Abstract Evidence-based precision medicine strategies do not currently exist to guide the choice of biologics in the treatment of psoriasis. As a result, a costly and arduous trial-and-error approach is often adopted. Artificial intelligence has the potential to improve personalization through the prediction of treatment outcomes using real-world data, such as that within the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR). We aimed to develop an explainable machine learning (ML) model to predict biologic drug discontinuation in a biologic-naive psoriasis cohort using BADBIR data. BADBIR data (2007–2024) were engineered to enable readability. Adult biologic-naive patients across all biologic cohorts with > 6 months of follow-up data were included. Recruitment centres representing 10% of the overall cohort were randomly separated for external validation (model testing). The residual cohort was then randomly split for model training (80%) and internal validation (20%, for hyperparameter tuning). Random forest modelling was applied for imputation of missing data. Only clinical data at baseline prior to biologic initiation were used for model training to enhance future clinical utilization. The performance of several ML (XG-Boost, AdaBoost, random forest) and deep learning (simple and recurrent neural networks) algorithms was evaluated. External validation was performed with a cross-validation leave-group-out approach of individual recruitment centres. SHAP (SHapley Additive exPlanations) and permutation feature importance values were generated to understand model predictions. In total, 10 806 patients were included, in the cohorts for training (n = 7722), internal validation (n = 1930) and external validation (for final model testing: nine centres, n = 1154). Most patients (n = 7290, 67%) discontinued initial biologic therapy within their follow-up duration (median 6.6 years). Within the discontinuation cohort, adalimumab (originator and biosimilars, 57%) was most prescribed. Higher proportions of female patients (43% vs. 37%) and patients with psoriatic arthritis (21% vs. 17%) and scalp psoriasis (59% vs. 51%) were noted in the discontinuation vs. the continuation cohort, respectively. AdaBoost, an ensemble ML model, outperformed other evaluated models with regards to area under the receiver operating characteristic curve (AUROC). Model testing predicted discontinuation of biologic therapy with (mean, 95% confidence interval) precision 0.85 (0.83–0.88), recall 0.80 (0.78–0.83), F1 score 0.82, AUROC 0.76 (0.71–0.78) and area under the precision recall curve (AUPRC) 0.83 (0.81–0.86). Performance metrics following testing with cross-validation [mean (SD)] were precision 0.79 (0.09), recall 0.69 (0.2), F1 score 0.74 (0.16), AUROC 0.71 (0.06) and AUPRC 0.75 (0.11). The features contributing most significantly to model performance were initial biologic drug, baseline Psoriasis Area and Severity Index, patient age, recruitment centre and baseline white cell count. In conclusion, AdaBoost represents an explainable, ML model with potential clinical utility to predict treatment outcomes of patients with psoriasis using real-world registry data. Future work will investigate discontinuation risk across a range of individual biologic therapies.