Predicting disease flares in axial spondyloarthritis using different machine learning models in the METEOR-SpA registry.

objectivesTo develop and externally validate machine learning models for predicting disease flares in axial spondyloarthritis (axSpA) using real-world registry data.

methodsPatients with axSpA receiving biological disease-modifying antirheumatic drugs in the METEOR-SpA registry were analysed at visit level. Flare was defined as an Axial Spondyloarthritis Disease Activity Score (ASDAS) increase ≥0.9 (or Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) increase ≥2) at the next 6-month assessment. Candidate models utilising routine clinical predictors and minimum-redundancy maximum-relevance variable selection were optimised for average precision (AP) to account for class imbalance, evaluated using patient-grouped cross-validation (CV) and externally validated in the Groningen Leeuwarden AxSpA cohort (GLAS) using discrimination, calibration and decision curve analysis (DCA).

resultsAfter eligibility criteria, 1167 patients and 3865 visits were included from the METEOR-SpA cohort; 618 (52.9%) patients had at least one visit with known flare status. Among them, 170 (27.5%) experienced at least one flare. A ridge logistic regression was selected as the final model with an internal CV receiver operating characteristic plot and area under the curve (ROC-AUC) of 0.72 (95% CI 0.70 to 0.75) and an AP of 0.291 (0.245 to 0.337), retaining eight predictors associated with flare. These were ASDAS (current and previous), female sex, flare history, enthesitis, infliximab exposure, physician global assessment and C reactive protein. In external validation in GLAS, ROC-AUC was 0.63 (95% CI 0.59 to 0.68) and AP 0.142 (0.116 to 0.171). DCA showed a modest positive net clinical benefit at low threshold probabilities.

conclusionsA reduced logistic-regression model showed moderate internal and modest external discrimination for short-term flare risk in axSpA. While not intended to guide individual pre-emptive therapy modification, the model may support low-risk interventions such as group-level risk stratification or tighter follow-up after further validation.