The decentralized OASIS study analyzed smartwatch biometric data and patient-reported outcomes from 532 SLE participants. A 94-feature regression model achieved significant correlation (R²=0.75) predicting self-reported flares, supporting proactive clinical screening.
Read MoreThis study combined natural language processing of medical records with patient biometric and self-reported data. Strong correlations identified 24 metrics predicting physician assessments, supporting AI-augmented remote lupus management strategies.
Read MoreThis prospective study evaluated aiSLE® MGMT engagement platform in SLE patients across five US practices. Participants showed improved quality-of-life metrics, reduced fatigue, and decreased physician-reported disease activity, demonstrating comprehensive care management benefits.
Read MoreThis study used a virtual/digital program to recruit at-risk individuals via online screening and sequential telehealth evaluations, classifying 18% with SLE in a mean of 371 days—shortening typical diagnosis time from 5-7 years—demonstrating potential for remote, accurate lupus assessment.
Read MoreThis decentralized study used machine learning to assess patient-reported outcomes, quality-of-life measures, and smartwatch biometric data from SLE patients. Models achieved significant predictive accuracy for disease flares, supporting proactive clinical screening.
Read MoreThis pilot study evaluates a comprehensive SLE management platform combining a Lupus Flare Risk Index biomarker, smartwatch-interfaced mobile app, and health coaching to improve patient self-efficacy and disease outcomes.
Read MoreThis study evaluated generative AI using ACR 1997 criteria to predict systemic lupus erythematosus classification from medical records. Results showed 76% predictive accuracy, with criterion-specific performance varying by clinical complexity.
Read MoreThis study assessed generative AI's ability to extract systemic lupus erythematosus classification criteria from medical records. Results showed structured criteria and digital profiling enhance diagnostic accuracy and clinical utility.
Read MoreThis study shows immune changes appear years before lupus is diagnosed: “type II interferon” and certain blood signals rise first, autoantibodies build next, and “type I interferon” spikes closest to diagnosis; combining these markers accurately flags high-risk individuals for earlier evaluation.
Read MoreThis study followed relatives of people with lupus and found those who later developed the disease already had higher “inflammation” signals in blood and lower “calming” signals; two markers, SCF (higher) and TGF-β (lower), best predicted who would transition, enabling earlier referral and prevention efforts.
Read MoreThis study developed a Lupus Disease Activity Immune Index integrating 32 soluble mediators weighted by autoantibody burden, successfully distinguishing active from low disease activity, correlating with SLEDAI scores, and identifying renal involvement to guide objective SLE management.
Read MoreThis study refined a Lupus Disease Activity Immune Index using 12 prioritized soluble mediators and autoantibody breadth, significantly distinguishing low versus active SLE, correlating with hSLEDAI, and identifying renal involvement, supporting objective, treat-to-target disease monitoring in practice
Read MoreThis study refined a blood-based Lupus Disease Activity Immune Index using nine key soluble mediators and autoantibody breadth, accurately distinguishing active from low/quiescent SLE and correlating with hSLEDAI, supporting objective, treat-to-target monitoring in clinical practice.
Read MoreThis study refined a blood-based Lupus Disease Activity Immune Index(L-DAI) using 33 soluble mediators and autoantibody breadth with machine learning, accurately distinguishing active from low SLE activity and offering clinically actionable, objective augmentation to traditional scoring systems
Read MoreThis study assessed the parallel use of L-FRI and L-DAI to assess simultaneous risk of future disease flare and concurrent disease activity to guide therapy. Results showed that combined assessment best identified imminent flares and concurent disease activity, supporting early intervention and clinical trial utility.
Read MoreThis study evaluated the association between both L-DAI and hSLEDAI and patient reported quality of life (HRQol). Higher biomarker-derived disease activity scores correlate with lower patient-reported quality of life, validating L-DAI and hSLEDAI as effective lupus monitoring tools.
Read MoreThis study evaluated L-FRI and L-DAI in parallel to determine the risk of future flare and concurrent disease activity. Simultaneous assessment improves identification of lupus patients with concurrent active disease and imminent flare risk.
Read MoreThis review highlights the need for predictive biomakers in SLE. A soluble mediator score showed high predictivity of impending flare in diverse population, offering a promising tool for early flare detection and intervention
Read MoreThis study assessed whether select Patient-reported outcomes enhance flare and disease activity indexes (L-DAI & L-FRI). Results showed PRO domains improves prediction and screening performance in lupus, supporting integration into SLE management
Read MoreThis study refined the flare risk index using immune mediators profiles in lupus patients. A subset of 11 mediators predicted risk of imminent flare with high sensitivity and specificity, enabling early intervention and improved disease management.
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