Predicting Anti-TNF Treatment Response in Rheumatoid Arthritis: An Artificial Intelligence-driven Model Using Cytokine Profile and Routine Clinical Practice Parameters

Overview

Journal Heliyon

Specialty Social Sciences

Date 2024 Jan 1

PMID 38163219

Authors

Juan Luis Valdivieso Shephard

Enrique Josue Alvarez Robles

Carmen Camara Hijon

Borja Hernandez Breijo

Marta Novella-Navarro

Patricia Bogas Schay

Ricardo Cuesta de la Camara

Alejandro Balsa Criado

Eduardo Lopez Granados

Chamaida Plasencia Rodriguez

Affiliations

Soon will be listed here.

Abstract

Introduction: Rheumatoid arthritis (RA) is a heterogeneous disease in which therapeutic strategies used have evolved dramatically. Despite significant progress in treatment strategies such as the development of anti-TNF drugs, it is still not possible to differentiate those patients who will respond from who will not. This can lead to effective-treatment delays and unnecessary costs. The aim of this study was to utilize a profile of the patient's characteristics, clinical parameters, immune status (cytokine profile) and artificial intelligence to assess the feasibility of developing a tool that could allow us to predict which patients will respond to treatment with anti-TNF drugs.

Methods: This study included 38 patients with RA from the RA-Paz cohort. Clinical activity was measured at baseline and after 6 months of treatment. The cytokines measured before the start of anti-TNF treatment were IL-1, IL-12, IL-10, IL-2, IL-4, IFNg, TNFa, and IL-6. Statistical analyses were performed using the Wilcoxon-Rank-Sum Test and the Benjamini-Hochberg method. The predictive model viability was explored using the 5-fold cross-validation scheme in order to train the logistic regression models.

Results: Statistically significant differences were found in parameters such as IL-6, IL-2, CRP and DAS-ESR. The predictive model performed to an acceptable level in correctly classifying patients (ROC-AUC 0.804167 to 0.891667), suggesting that it would be possible to develop a clinical classification tool.

Conclusions: Using a combination of parameters such as IL-6, IL-2, CRP and DAS-ESR, it was possible to develop a predictive model that can acceptably discriminate between remitters and non-remitters. However, this model needs to be replicated in a larger cohort to confirm these findings.

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