Machine learning identifies a profile of inadequate responder to methotrexate in rheumatoid arthritis.

Objectives: Around 30% of patients with RA have an inadequate response to MTX. We aimed to use routine clinical and biological data to build machine learning models predicting EULAR inadequate response to MTX and to identify simple predictive biomarkers.

Methods: Models were trained on RA patients fulfilling the 2010 ACR/EULAR criteria from the ESPOIR and Leiden EAC cohorts to predict the EULAR response at 9 months (± 6 months).

Machine learning predicts response to TNF inhibitors in rheumatoid arthritis: results on the ESPOIR and ABIRISK cohorts.

Objectives: Around 30% of patients with rheumatoid arthritis (RA) do not respond to tumour necrosis factor inhibitors (TNFi). We aimed to predict patient response to TNFi using machine learning on simple clinical and biological data.

Methods: We used data from the RA ESPOIR cohort to train our models.

Allergy Evaluation of Hypersensitivity to Platinum Salts and Taxanes: A Six-Year Experience.

Background: Hypersensitivity reactions (HSRs) to platinum salts (PS) and taxanes (TX) are a challenge to cancer management. Allergy evaluation based on skin tests (ST) and graded challenges can provide a diagnosis of an allergy to a suspected drug and indicate possible treatment with alternative same-class drugs.

Objective: This study aimed to estimate the negative predictive value of ST in the diagnosis of HSRs to TX and PS.