Medication-related hospitalisations in patients with SLE
Bibliographic record
Abstract
OBJECTIVES: Patients with SLE take multiple medications. Within a large prospective longitudinal SLE cohort, we characterised medication-related hospitalisations and their preventability. METHODS: We identified consecutive admissions to our tertiary hospitals between 2015 and 2020. Two independent adjudicators evaluated if medication-related events contributed to the hospitalisation, considering (1) adverse drug events (ADEs) and (2) events from medication non-adherence, using the Leape and Bates method. We classified ADEs as potentially preventable/ameliorable if we identified modifiable factors. Logistic regressions with generalised estimating equations evaluated associations between participant characteristics and medication-related hospitalisations, accounting for repeat hospitalisations within the same participant. RESULTS: We studied 68 hospitalisations among 45 participants (91% female). At first hospitalisation, the median age was 38 years (IQR 26.5-53.0) and median SLE duration was 12 years (IQR 5.5-19.5). One or more ADEs contributed to 20 (29%) hospitalisations (11/23 (48%) ADEs being preventable/ameliorable), and SLE flares associated with medication non-adherence contributed to 7 (10%) hospitalisations. Adjusting for age and sex, current prednisone use (adjusted OR (aOR) 3.7, 95% CI 1.1 to 13.0) or ≥1 current immunosuppressant (aOR 11.5, 95% CI 2.7 to 50.0), renal involvement at SLE diagnosis (aOR 6.5, 95% CI 2.7 to 15.7) and polypharmacy (≥5 medications; aOR 11.3, 95% CI 1.2 to 103.8) were associated with having an ADE-related (vs non-ADE) hospitalisation. Age at SLE diagnosis<18 years (OR 5.9, 95% CI 1.3 to 26.6) was associated with hospitalisation for a flare related to non-adherence. CONCLUSION: Forty per cent of SLE hospitalisations were medication-related, while half were potentially preventable/ameliorable. Renal involvement, polypharmacy, prednisone and immunosuppressant use were associated with hospitalisation related to an ADE, highlighting a vulnerable group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".