Predicting abatacept retention using machine learning.

ABSTRACT:

Background

The incorporation of machine learning is becoming more prevalent in the clinical setting. By predicting clinical outcomes, machine learning can provide clinicians with a valuable tool for refining precision medicine approaches and improving treatment outcomes.

Methods

This was a post hoc analysis of pooled patient-level data from the global, real-world ACTION and ASCORE trials in patients with rheumatoid arthritis (RA) initiating abatacept. Patient demographic and disease characteristics were input across 10 machine learning models used to predict 12-month treatment retention. Retention was defined as treatment for > 365 days or ≤ 365 days in patients who achieved remission or major clinical response (based on European Alliance of Associations for Rheumatology res

SUBMITTER: Alten R 

PROVIDER: S-EPMC11786492 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature