Predictive Value of Active Sacroiliitis in MRI for Flare Among Chinese Patients with Axial Spondyloarthritis in Remission

Introduction: In recent axSpAx patients with remission lasting at least 3 months and later followed-up monthly for a median of 8 months, we compared the predictive value of baseline MRI of sacroiliac joints and constructed a nomogram model for predicting flare. Methods: This study included 251 patients with axial spondyloarthritis, according to the ASAS axSpA classification criteria, who achieved Low Disease Activity (ASDAS) and underwent MRI examination. A total of 144 patients from the First Affiliated Hospital of Xiamen University were used as the nomogram training set; 107 from the First Affiliated Hospital of Fujian Medical University were for external validation. Results: The median time of relapse was 8.705 months (95% CI 8.215-9.195) and 7.781 months (95% CI 7.075-8.486) for MRI-positive patients and 9.8 months (95% CI 9.273-10.474) for MRI negative patients, respectively. Both active sacroiliitis on MRI (HR 1.792, 95% CI 1.230-2.611) and anti-TNF-alpha treatments (HR 0.507, 95% CI 0.349-0.736) were significantly associated with disease flares. Gender, disease duration, HLA-B27, MRI, and anti-TNF-alpha treatment were selected as predictors of the nomogram. The areas under the ROC curve (AUROCs) of the 1-year remission probability in the training and validation groups were 0.71 and 0.729, respectively. Nomogram prediction models present better AUROCs, C-indices, and decision curve analysis cure than the clinical experience model. Conclusions: Active sacroiliitis in MRI requires weighting in order to estimate remission and disease flares, when axSpA patients achieve low disease activity. The simple nomogram might be able to discriminate and calibrate in clinical practice. Trial registration: ClinicalTrials, NCT03425 812, Registered 8 February 2018, https://clinicaltrials.gov

Automated summary

This study examined the predictive model for flare in axSpA patients, finding significant associations between active sacroiliitis on MRI, anti-TNF-alpha treatment, and disease flares. The nomogram prediction model showed better performance in discriminating and calibrating in clinical practice.