Publications

This 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

This 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.

This study validated the utility of the flare risk index. 11 immune mediators shows strong predictive power for lupus flares, including differentiation of mild-moderate and severe flare risk.

This study evaluated whether immune mediators predicts lupus flares. Using plasma samples and machine learning algorithm, 31 mediators ditinguished pre-flare from stable state. A refined lupus flare prediction index achieved high sensitivity and specificity supporting its use as a predictive tool for early flare detection and lupus management.

This study assessed baseline and follow-up plasma levels of 52 soluble mediators in African American SLE patients. Results showed 31 elevated Immune mediator preceding disease flare, aiding in personalized flare prediction and intervention strategies

This study evaluated the changes in plasma concentrations of soluble mediators that precede clinically defined disease flares. Findings showed that shift in inflammatory and regualatory cytokines precedes lupus flares, suggesting that soluble mediators can serve as predictive biomakers, enabling earlier intervention and more precise monitoring of disease activity.

This 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.

This 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.

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.

This 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.

This 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.

This 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.

This 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.

This 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.

This 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.

This 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.