Background/Purpose: ANA-associated RMDs (ANA-RMDs) include SLE, Sjogren’s, Scleroderma, Myositis, and mixed/undifferentiated CTD. Despite overlapping clinical and immunophenotypic features, there is significant disparity in access to targeted therapies across ANA-RMDs. A robust data-driven reclassification using clinical and biomarker data with clinical impact could define more homogeneous cohorts for therapies and clinical trials.

Methods: We trained a variational autoencoder with the European PRECISESADS cohort of 876 ANA-RMD patients using R, keras, and tensorflow. 25 covariates were prioritised by ANA-RMD specialists and patient focus-groups . Data was compressed to an 8-neuron latent space and analyzed with multiple clustering techniques.

For validation, Kmeans centroids from PRECISESADS were applied to the DEFINITION dataset (219 patients). Cluster durability was assessed using entropy, elbow plots, and cluster stability index. Gene expression data was analyzed with heatmaps and summary statistics.

Clinical impact in DEFINITION was analyzed cross-sectionally and longitudinally using descriptive statistics, PROs (e.g., SF36), physician assessments (e.g., BILAG-2004, PGA), and gene expression scores. 5-year follow-up outcomes included hospitalization rates. Kaplan-Meier and Sankey plots were generated with survival and flipPlots R packages

Results: Deep learning revealed five distinct ANA-RMD classes. Each class encompassed patients from various legacy diagnoses, with no single legacy diagnosis mapping to a new class. These classes were: (i) Sicca, mostly patients with a legacy diagnosis of pSS, SLE, or UCTD with low disease activity but high IFN-I expression; (ii) Quiescent, characterized by low gene expression and physician-assessed disease activity but high patient-reported pain scores; (iii) Active MSK disease, with high MSK disease activity and high inflammatory gene expression; (iv) Myeloinflammatory, with high levels of therapeutic change, PRO impact, and high myeloid/interferon/inflammatory gene expression, containing substantial numbers of previously undifferentiated patients; (v) High clinical and interferon activity, with high healthcare utilization, physician-assessed disease activity, and emergency department attendance. Five-year healthcare data revealed significant differences in hospital admission rates (p< 0.01) and emergency department attendance (p< 0.01) for the new classes but not for legacy diagnoses.

Conclusion: Using advanced deep learning, we developed and validated a new classification for ANA-RMDs. Our findings showed that (i) more of the ANA-RMD spectrum could be classified than with legacy diagnoses; (ii) immunophenotypic and clinical features within these classes were more homogeneous than with legacy diagnoses, suggesting suitability for the same therapies and outcomes; (iii) these classes better predicted long-term outcomes and healthcare utilization. Clinical trials in these populations may yield larger effect sizes and provide evidence applicable to more patients, thereby reducing healthcare inequality.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/discovery-and-validation-of-a-new-classification-of-ana-rmds-that-better-predict-long-term-outcomes-compared-to-legacy-diagnoses/