AB0380 MACHINE LEARNING CAN PREDICT GIANT CELL ARTERITIS RELAPSE AFTER GLUCOCORTICOID TAPERING
Authors: V. Venerito, G. Emmi, L. Cantarini, N. Lascaro, M. Fornaro, O. Angelini, L. Coladonato, F. Cacciapaglia, P. Leccese, G. Lopalco, F. Iannone
Journal: Annals of the Rheumatic Diseases (2021)

Background: To date reliable biomarkers and risk factors for relapsing giant cell arteris (GCA) after glucocorticoid (GC) tapering are still lacking. In an increasing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for the implementation of complex multi-parametric decision algorithms. A ML approach allows to handle complex non-linear relationships between patient attributes that are hard to model with traditional statistical methods, merging them to output a forecast or a probability for a given outcome. Objectives: To assess whether ML algorithms can predict GCA relapse after glucocorticoid tapering. Methods: GC-naïve GCA patients who presented to 4 tertiary care centers between January 2015 and January 2019, who underwent GC therapy and regular follow up visits for at least 12 months were retrospectively analyzed and used for training and validation (through 10-fold cross-validation) of n.2 ML algorithms, namely Decision Trees (DT) and Random Forest (RF). Test of the algorithms was carried out GCA patients referred to the same centers from March 2019 to September 2020 whose data was longitudinally recorded during the 12 months after presentation. Demographic, clinical an laboratory characteristics (Erythrocyte Sedimentation Rate (ESR) and C Reactive Protein (CRP) levels) were gathered. The outcome of interest was the GCA relapse within 12 months after induction of remission, during GC tapering. The accuracy of the algorithms in both validation and test phases was assessed. Results: The training and validation dataset consisted of n.85 GCA patients (59 female, 69.4%) with mean age 73.8 (±8.7) years at presentation. They were treated with 27.1 (±17.4) mg prednisone (PDN) equivalent at first visit. During GC tapering 34 of them (40%) experienced a disease relapse within 12 months. The test dataset consisted of n.22 patients (14 female, 63.4%) with mean age 75.5 (±8.7) years at presentation, who underwent GC induction therapy with a mean dose of 30.3 (±17.3) mg PDN equivalent. Nine of them (40.9%) had a GCA flare during GC tapering, within 12 months. Accuracy of DT and RF in predicting the outcome of interest on the training dataset was 68.3% and 73.4% respectively. On testing datasets DT and RF accuracy was 57.1 and 72.4%, respectively. As shown in Figure 1, the most important patient attributes for RF forecast were found to be CRP and ESR baseline levels as well as age and symptom duration (months) at first visit. Conclusion: RF algorithm can predict GCA relapse after glucocorticoid tapering with fairly good accuracy. To date this is one of the most accurate predictive modeling for such outcome. This ML method represents a reproducible tool executable on computers as well as mobile devices and capable of supporting clinicians in GCA patient management. References: [1]Hellmich B., Agueda A., Monti S. , et al. 2018 Update of the EULAR recommendations for the management of large vessel vasculitis Annals of the Rheumatic Diseases 2020;79:19-30. [2]Venerito, V., Angelini, O., Cazzato, G. et al. A convolutional neural network with transfer learning for automatic discrimination between low and high-grade synovitis: a pilot study. Internal and Emergency Medicine 2021. Disclosure of Interests: None declared