Machine learning prediction and explanatory models of serious infections in patients with rheumatoid arthritis treated with tofacitinib.

ABSTRACT:

Background

Patients with rheumatoid arthritis (RA) have an increased risk of developing serious infections (SIs) vs. individuals without RA; efforts to predict SIs in this patient group are ongoing. We assessed the ability of different machine learning modeling approaches to predict SIs using baseline data from the tofacitinib RA clinical trials program.

Methods

This analysis included data from 19 clinical trials (phase 2, n = 10; phase 3, n = 6; phase 3b/4, n = 3). Patients with RA receiving tofacitinib 5 or 10 mg twice daily (BID) were included in the analysis; patients receiving tofacitinib 11 mg once daily were considered as tofacitinib 5 mg BID. All available patient-level baseline variables were extracted. Statistical and machine learning methods (logistic regression,

SUBMITTER: Hetland ML 

PROVIDER: S-EPMC11348567 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature