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Clinical Trial Summary

Systemic lupus erythematosus is a chronic autoimmune disease, which can involve multiple systems and largely impair patients' health. Kidney is the one of the most commonly affected organs. It was reported that more than about 70% SLE patients developed lupus nephritis, which was highly associated with the long-term prognosis1,2. It will be a great advantage if the high-risk groups could be predicated and prevented with pre-treatment, the renal prognosis and survival would be promisingly improved. The incidence of lupus nephritis within past 10 years in new-onset SLE patients was recorded in our retrospective study, which was highest in their first-year, about 17%, and about 5% per year in the following years3. The raising of risk prediction models and the recognition of high-risk patients are quite important. The prediction model depends on the collection of patient phenotypes, which are scattered in various forms and very cumbersome. In our previous study, a total of 14,439 SLE patients were collected from the rheumatology and immunology departments of 13 Chinese tertiary hospitals in this study, including 13 062 females (90.46%), with an average age of 33.4 years, and the time span of EMR (Electronic Medical Records) was from October 28, 2001 to March 31, 2017. It includes basic information about patients, physical examination, inspection and diagnostic information, etc. We designed a hybrid NLP system combined NLP technical and expert knowledge at the same time, which was named as Deep Phenotyping System (DPS), to extract all the phenotypic information recorded in EMR. The DPS efficiently processed EMR data, and its accuracy, precision, and recall were each greater than 93%. It extracted 73 794 entities from 14,439 SLE cases, each with time attributes, and produced 18,785,000,640 entities. Thus, a LN prediction model was raised, which the likelihood of lupus patients without nephritis will develop lupus nephritis within half and one year can be predicted.) More than 35 000 phenotypes were used in this model and it was verified with independent samples. The best accuracy (ACC) and area under the curve (AUC) predicting the 1-year and 2-year risk of developing lupus nephritis can be achieved 0.88 and 0.86 respectively. The comprehensive SLE phenotype database constructed by NLP greatly improves the research efficiency of lupus clinical phenotype. We first proposed a predictive model of lupus nephritis, which is high applicability and efficiency. The experimental results of good close and open testing fully demonstrate the authenticity and practicality of this database. The research process and method based on real world data are also applicable to predict other important complications of lupus3. Till now, there were no studies investigating secondary prevention tools of lupus nephritis. However, as we all known, disease flare is a high-risk factor of cruel organ damage, and our previous data showed that lupus nephritis was one of the important flare patterns4. Two phase III, randomized, placebo-controlled studies, BLISS-52 and BLISS-76 showed that belimumab, the only FDA-approved biologic in SLE, targeting B Lymphocyte Stimulator, can reduce disease flares compared to standard-of-care (SOC) therapy5,6. A propensity-score matching study further proved that belimumab add on reduces organ damage progression as measured by SDI7. A pooled post-hoc analysis of the BLISS trials took a deeper look at renal outcome, and suggest that belimumab may offer renal benefit in patients with SLE, indicated by less renal flares in belimumab group, that is 1.1% versus 3.0 in the placebo8. We hypothesized and tried to analyze that whether belimumab could act as a secondary prevention tool for SLE patients at high-risk.


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NCT number NCT05585671
Study type Observational
Source RenJi Hospital
Contact
Status Not yet recruiting
Phase
Start date June 1, 2023
Completion date June 1, 2026