STUDY OF ANTI-MALARIALS IN INCOMPLETE LUPUS – THE SMILE TRIAL
Bibliographic record
Abstract
O056 / #121 Topic:AS23 - SLE-Diagnosis, Manifestations, & Outcomes ABSTRACT CONCURRENT SESSION 09: SLE THERAPY – REVISITING OLD DRUGS AND UNLOCKING HIDDEN POTENTIAL OF NEW MEDICATIONS 24-05-2025 10:40 AM - 11:40 AM Background/Purpose Patients with features of systemic lupus erythematosus (SLE) who do not have sufficient criteria to be classified can be designated as having incomplete lupus (ILE). This is a common condition seen in clinical practice and it has further significance as a group that has high risk of progression to SLE. Identification and treatment of those at risk has the potential to reduce the severity and incidence of SLE. Methods Based on previous studies, hydroxychloroquine (HCQ) was chosen as an intervention for a randomized, double-blind, placebo-controlled trial to determine whether the rate of accumulation of clinical and immunologic features of SLE as defined by the 2012 SLICC criteria could be reduced. ILE was defined as ANA positivity with one or 2 additional criteria from the SLICC 2012 list. Males and females 15 to 49 years of age were eligible for enrollment. After baseline evaluation including ophthalmologic exam, participants were randomized 1:1 to HCQ or placebo. Evaluations at 3-month intervals included clinical and laboratory measures as well as patient-reported outcomes (PROs). Treatment was continued for 24 months, but if SLICC criteria were satisfied sooner, patients exited the study. Ophthalmologic exams were carried out at conclusion of treatment. Results A total of 187 ILE patients were randomized at 7 sites in the USA. After excluding 7 patients found to have SLE criteria at baseline when pending laboratory data were completed, 180 patients were available for analysis; 92 were randomized to HCQ and 88 received placebo. The mean age was 33 years, 91.1% were female and 74.4% were White individuals. At randomization, 65.6% had 2 SLICC criteria; the remainder had 3 SLICC criteria. The most common manifestations involved skin and joints. SLE per criteria developed in 24 participants (13.3%) during the trial who were terminated early and 40 (22%) developed additional SLICC criteria. The primary outcome was the rate of acquisition of SLICC criteria analyzed via a generalized linear mixed-effects model, with an embedded ordinal logistic regression, comparing the changes over time for the 2 arms. This showed similar slopes in the 2 groups (P=0.72). The odds of progressing to a higher SLICC score relative to the previous score was 14% smaller for every 3-month increase in time for the HCQ group and 18% smaller for the placebo group, a difference which was not statistically significant (P=0.69). A key secondary outcome was time to progression to SLE. Using a Cox proportional hazards regression model, the hazard of progressing to SLE was 10% higher for the HCQ group than for placebo, which was not a statistically significant difference (P=0.81). Adverse events were similar in the 2 groups and no serious adverse events related to use of HCQ were recorded. Five individuals were excluded from entry due to abnormal ophthalmologic findings; none developed during the trial. Conclusions The SMILE results do not endorse the use of HCQ to prevent accumulation of SLICC SLE criteria. However, the definition of ILE used in SMILE does include individuals who are at risk for progressive disease and may be useful in future studies of preventive therapies. Other ongoing analyses will determine whether autoantibodies, inflammatory mediators or PROs were related to progressive illness or use of HCQ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".