Predictors of Unsuccessful Hydroxychloroquine Tapering and Discontinuation: Can We Personalize Decision‐Making in Systemic Lupus Erythematosus Treatment?
Bibliographic record
Abstract
OBJECTIVE: Hydroxychloroquine (HCQ) is a key systemic lupus erythematosus (SLE) drug, making concerns of drug shortages grave. Our objective was to evaluate factors associated with poor outcomes after HCQ taper or discontinuation in SLE. METHODS: We studied 5 Canadian SLE cohorts between 1999 and 2019, following patients from the date of HCQ tapering (cohort 1) or discontinuation (cohort 2). A composite outcome was defined as any of the following: a need for therapy augmentation, an increase (of at least 4 points) in the Systemic Lupus Erythematosus Disease Activity Index 2000 score, or hospitalization for SLE. In each cohort, multivariable Cox regression was used to identify demographic and clinical factors associated with time to the earliest of these events. A third cohort continuing to receive HCQ was also studied, to assess whether the same factors influenced the outcome even when the HCQ dose was unchanged. RESULTS: The poor outcome rate, per 100 person-years, was 35.7 (95% confidence interval [95% CI] 31.6-40.3) in the HCQ taper cohort (n = 398), 29.0 (95% CI 25.5-33.0) in the discontinuation cohort (n = 395), and 16.1 (95% CI 13.2-19.6) in the maintenance cohort (n = 395). In patients tapering HCQ, baseline prednisone use was independently associated with greater risk of poor outcomes. In the discontinuation cohort, the risk of poor outcomes was greater for Black patients and those diagnosed with SLE at age ≤25 years. Among those maintaining HCQ, baseline immunosuppressive use and First Nations ethnicity were associated with poor outcomes. CONCLUSION: We identified demographic and clinical factors associated with poor outcomes after HCQ taper/discontinuation. This information is critical in the current setting of potential shortages, but over the long term, such information could inform personalized therapies.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".