Development and evaluation of a multivariable prediction model for clinical improvement in an established cohort of Colombian rheumatoid arthritis patients.

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disease, and a predicting clinical improvement is essential.

OBJECTIVES

The aim of the present study was to identify predictor variables of clinical improvement in patients with RA using artificial intelligence (AI) models in a specialized RA center.

DESIGN

Retrospective cohort study in adult RA patients was conducted between January and June 2022. Follow-up data related to clinical improvement was taken from 6 to 12 months after the baseline. Predictive models were generated by machine learning (ML), by Python programming language. The Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis (TRIPOD) guidelines were followed to harmonize this study based on AI.

METHODS

The response variable was classified as improved and non-improved. Patients were considered improved if they persisted or achieved a Disease Activity Score 28-joints (DAS28) <3.2 at the end of the follow-up period or experienced a decrease ⩾0.6 compared to baseline, regardless of the initial DAS28 value. Explainability techniques in AI were applied to identify the most relevant clinical features.

RESULTS

In total, 3161 RA patients were included. The median age was 65 years (interquartile range (IQR) 57-72). 82.7% were female. Disease duration was 8.3 years (IQR 4.9-11.3). The median value of baseline DAS28 was 2.1 (IQR 2.1-2.8). 2668 (84.4%) were classified as improved, and 493 (15.6%) as non-improved. From ML models, the Extra tree model showed higher sensitivity (0.841). Regarding clinical improvement prediction with the Shapley Additive Explanations method, it was observed that low values of baseline DAS28 were positively associated with clinical improvement. The use of biologic disease-modifying antirheumatic drugs and the presence of anti-cyclic citrullinated peptide (CCP) were related to an increase in the probability of non-improved, which may be secondary to the level of severity of the disease.

CONCLUSION

AI models in RA can predict clinical improvement at initial consultations, enabling targeted approaches. Disease severity may be influenced by anti-CCP positivity and the use of biologic therapies when conventional treatments fail.