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

Abstract

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.

Keywords: artificial intelligence; clinical improvement; machine learning; probability learning; rheumatoid arthritis.

Figure 1.

Strategy to define clinically improved patients and non-improved patients.

Figure 2.

SHAP summary plot. The graph shows the SHAP values organized according to the importance of the variables that contribute to the Clinical improvement. These graphs use a range of colors, from blue (low) to red (high), to represent the characteristic values of each variable. For example, for the initial DAS28, the blue color reflects lower scores, while the red color indicates a higher score on the initial DAS28. In the case of binary variables such as the use of biologicals, the blue color represents the absence of the characteristic, and the red color represents its presence. Therefore, linear changes in feature values are visualized through color variation, while SHAP values (shown at the bottom of the graph) indicate whether they are closer to Clinical improvement (positive SHAP values) or non-improved (negative SHAP values). DAS28, Disease Activity Score 28—joints; SHAP, Shapley Additive Explanations.