Predicting SLE Flares with Patient Reports and Machine Learning
Official title: Prediction of SLE Flares Using Patient-Reported Outcomes and Machine Learning
Run by Sohag University · NCT07890662 · Data updated 2026-11-12
Aims to test a low-cost, patient-reported outcome–based machine learning tool to see if it can detect lupus flares early and support care, especially in settings with limited resources.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 60 Years |
| Sex | All |
| Enrollment | 500 (estimated) |
| Sponsor | Sohag University Academic / other |
| Locations | 1 site (Sohag, Nasr City) |
| Status | Recruiting |
| Started | June 30, 2026 |
| Main results expected | June 30, 2027 |
| Study ends | June 30, 2027 |
| Registry record updated | October 22, 2026 |
| Trial ID | NCT07890662 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults (18+) with SLE who meet the 2019 EULAR/ACR criteria and have regular follow-up at Sohag University.
✕Probably not a fit
Patients with co-existing inflammatory/autoimmune diseases, cognitive impairment, incomplete data, or no regular follow-up may not benefit from this approach.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational study will develop and validate a machine learning model that uses patient-reported outcomes to predict systemic lupus erythematosus flares. Participants are adults with SLE who meet the 2019 EULAR/ACR criteria and have regular follow-up at Sohag University. The project intends to reduce laboratory costs and enable telemedicine monitoring by leveraging PROs collected over time. The study does not prescribe an intervention beyond PRO data collection and analysis, and it will explore how the tool performs in real-world clinical workflows. Results could guide proactive flare management in resource-constrained environments.
Why it matters
Potential benefit: If successful, the PRO-based ML tool could help clinicians detect flares earlier and reduce unnecessary lab testing and in-person visits.
How similar studies have gone: Previous work has shown that PROs can contribute to flare prediction in autoimmune diseases, but this exact PRO-based ML approach in SLE is new and exploratory.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Age ≥ 18 years. • Both sexes. • Patients diagnosed with SLE who fulfilling the Systemic Lupus 2019 European League against Rheumatism/American College of rheumatology classification criteria for systemic lupus erythematosus (2019 EULAR/ACR SLE classification criteria) • Regular follow up. Exclusion Criteria: * • Co-existing chronic inflammatory or autoimmune diseases. • Age \<18 years • Cognitive impairment. • Incomplete data.
Where it's running
-
Sohag University
Sohag, Nasr City, Egypt Recruiting
Study contacts
- Study coordinator: Ahmed A. Abdelrahman
- Email: Ahmed.Alboghdady@med.sohag.edu.eg
- Phone: +20 11 14585489
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.