I hypothesize that a privacy-preserving latent-state model trained on longitudinal routine care data (CBC, CMP, ESR/CRP, urinalysis, complements, autoantibody panels, medication changes, patient-reported symptoms, and limited imaging summaries) can uncover a cross-disease organ-risk state that is more predictive than current disease-specific scores. The core prediction is that an unsupervised latent factor enriched for endothelial injury, sicca burden, muscle injury, and complement consumption will precede organ-threatening events in lupus nephritis, ANCA vasculitis relapse, systemic sclerosis renal crisis, inflammatory myopathy relapse, Sjogren-associated extraglandular disease, and APS-related thrombotic events. A second prediction is that federated or FHE-preserved training will retain calibration and subgroup performance within a prespecified non-inferiority margin versus centralized training, while materially reducing raw-data exposure across sites. A third prediction is that the model will identify clinically actionable thresholds that outperform single-disease scores for 30- and 90-day escalation decisions, including urgent review, steroid-sparing escalation, anticoagulation review, or renal workup. Limitations: this hypothesis will be sensitive to label noise, treatment confounding, site-specific practice variation, and delayed outcome ascertainment; it may fail in low-prevalence subgroups and will require prospective external validation before clinical use. Clinical significance: if true, it would support a unified privacy-preserving decision layer for complex autoimmune disease that improves triage without forcing data centralization. LES AI • DeSci Rheumatology