Predicting early discontinuation of adalimumab in patients with rheumatoid arthritis using machine learning: A specialty pharmacy-based approach.

Abstract

Patients with rheumatoid arthritis (RA) prescribed adalimumab often discontinue treatment within 6 months because of a perceived lack of benefit. Specialty pharmacies are well-positioned to intervene early, but identifying patients at high risk for early discontinuation is difficult to predict with existing tools. To develop a predictive model using machine learning (ML) to identify patients with RA at high risk of discontinuing adalimumab within 6 months, enabling targeted pharmacist interventions. We used the retrospective data of patients with RA who initiated adalimumab at a specialty pharmacy between 2020 and 2023. Eligible patients completed patient-reported assessment at first dispense and maintained medication adherence (proportion of days covered ≥80%) before discontinuation. A total of 38 features were collected at pharmacy service initiation from an integrated dispensing and clinical management platform. Predictors were selected based on low missingness (≤20%) and low interfeature correlation (|Pearson coefficient|≤0.4). Several ML classification models were trained and evaluated using metrics including area under the receiver operating characteristic curve (AUC-ROC) and F1 score. Of 300 eligible patients with RA, 37.7% were classified as high risk for discontinuing adalimumab within 6 months owing to loss of efficacy. A total of 19 predictors were selected, including sex, age, treatment initiation status (new vs transfer), pain score, joint swelling, morning stiffness, RA duration, body mass index, bone health, infection, history of joint injury, and comorbidities. Elastic Net achieved the highest performance (AUC-ROC = 0.886; F1 score = 0.741) followed closely by linear discriminant analysis and support vector machines, which also performed well in identifying high-risk patients. Predictive modeling using routinely collected specialty pharmacy data can identify patients with RA at risk of early adalimumab discontinuation. In particular, the Elastic Net regularized logistic regression model offered high discriminative performance and may support pharmacist-led follow-up and timely interventions to reduce medication waste and improve patient outcomes.