Hydroxychloroquine and maintenance immunosuppression use in kidney transplant recipients: Analysis of linked US registry and claims data
Bibliographic record
Abstract
Abstract Hydroxychloroquine (HCQ) is an antimalarial drug with immunomodulatory effects used to treat systemic lupus erythematosus (SLE) and scleroderma. The antiviral effects of HCQ have raised attention in the context of the COVID‐19 pandemic, although safety is controversial. We examined linkages of national transplant registry data with pharmaceutical claims and Medicare billing claims to study HCQ use among Medicare‐insured kidney transplant recipients with SLE or scleroderma (2008–2017; N = 1820). We compared three groups based on immunosuppression regimen 7 months‐to‐1 year post transplant: (a) tacrolimus (Tac) + mycophenolic acid (MPA) + prednisone (Pred) (referent group, 77.7%); (b) Tac + MPA + Pred + HCQ (16.5%); or (c) other immunosuppression + HCQ (5.7%). Compared to the referent group, recipients treated with other immunosuppression + HCQ had a 2‐fold increased risk of abnormal ECG or QT prolongation (18.9% vs. 10.7%; aHR, 1.12 1.96 3.42 , p = .02) and ventricular arrhythmias (15.2% vs. 11.4%; aHR, 1.00 1.81 3.29 , p = .05) in the >1‐to‐3 years post‐transplant. Tac + MPA + Pred + HCQ was associated with increased risk of ventricular arrhythmias (13.5% vs. 11.4%; aHR, 1.02 1.54 2.31 , p = .04) and pancytopenia (35.9% vs. 31.4%; aHR, 1.03 1.31 1.68 , p = .03) compared to triple immunosuppression without HCQ. However, HCQ‐containing regimens were not associated with an increased risk of death or graft failure. HCQ may be used safely in selected kidney transplant recipients in addition to their maintenance immunosuppression, although attention to arrhythmias is warranted.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".