LO-024 Association between severe non-adherence to hydroxychloroquine and SLE flares, damage, and mortality in 660 patients from the SLICC inception cohort
Bibliographic record
Abstract
Background Hydroxychloroquine is one of the major treatment of SLE, but its effectiveness is impaired by non-adherence, reported to range from 3% to 85% in SLE patients. Our objective was to assess the associations of severe non-adherence to HCQ, objectively assessed by HCQ serum levels, and risks of SLE flares, damage, and mortality over 5 years of follow-up. Methods The SLICC Inception Cohort is a multicenter initiative (33 centers; 11 countries). Serum of patients taking HCQ for at least 3 months, sampled at enrolment or during the first-year follow-up visit, were analyzed. Severe non-adherence was defined by a serum HCQ level <106 ng/ml or <53 ng/ml, for daily HCQ doses of 400 or 200 mg/d, respectively. Association with the risk of a flare (defined as a SLEDAI-2K increase ≥4 points, initiation of prednisone or immunosuppressive drugs, or new renal involvement) was studied with logistic regression, and association with damage (first SLICC/ACR Damage Index (SDI) increase ≥1 point) and mortality were studied with separate Cox proportional hazard models. Results Of 1849 cohort subjects, 660 patients (88% women) were included. Median [interquartile range] serum HCQ was 388 ng/ml (244–566); 48 patients (7.3%) had severe HCQ non-adherence. No factors were clearly associated with severe non-adherence. Severe non-adherence was independently associated with flare (OR 3.38; 95% CI 1.80–6.42) and of an increase in the SDI within each of the first 3 years (HR 1.92 at 3 years; 95% CI 1.05–3.50). Eleven patients died within 5 years, including 3 with severe non-adherence (HR 5.41; 95% CI 1.43–20.39). Conclusions Severe non-adherence was independently associated with the risk of an SLE flare in the following year, with early damage and 5-year mortality. Our results suggest the benefits of testing of detecting severe non-adherence and dedicating more resources and more time to these patients, to improve their long-term prognosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".