Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals-The OASIS Study.
Eldon R JupeGerald H LushingtonMohan PurushothamanFabricio PautassoGeorg ArmstrongArif SorathiaJessica CrawleyVijay R NadipelliBernard RubinRyan NewhardtMelissa E MunroeBrett AdelmanPublished in: Biotech (Basel (Switzerland)) (2023)
(1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for predicting possible disease flares. (2) Methods: Participants were recruited from the LupusCorner online community. Adults self-reporting an SLE diagnosis were consented and given a mobile application to record patient profile (PP), PRO, and QOL metrics, and enlisted participants received smartwatches for digital biometric monitoring. The resulting data were profiled using feature selection and classification algorithms. (3) Results: 550 participants completed digital surveys, 144 (26%) agreed to wear smartwatches, and medical records (MRs) were obtained for 68. Mining of PP, PRO, QOL, and biometric data yielded a 26-feature model for classifying participants according to MR-identified disease flare risk. ROC curves significantly distinguished true from false positives (ten-fold cross-validation: p < 0.00023; five-fold: p < 0.00022). A 25-feature Bayesian model enabled time-variant prediction of participant-reported possible flares (P(true) > 0.85, p < 0.001; P(nonflare) > 0.83, p < 0.0001). (4) Conclusions: Regular profiling of patient well-being and biometric activity may support proactive screening for circumstances warranting clinical assessment.
Keyphrases
- systemic lupus erythematosus
- disease activity
- machine learning
- rheumatoid arthritis
- patient reported outcomes
- deep learning
- rheumatoid arthritis patients
- big data
- ankylosing spondylitis
- electronic health record
- healthcare
- patient reported
- artificial intelligence
- end stage renal disease
- newly diagnosed
- juvenile idiopathic arthritis
- chronic kidney disease
- emergency department
- case report
- magnetic resonance
- magnetic resonance imaging
- cross sectional
- prognostic factors
- ejection fraction
- quantum dots
- sensitive detection
- label free