Hydroxychloroquine and Chloroquine-Induced Cardiac Arrhythmias and Sudden Cardiac Death in Patients With Systemic Autoimmune Rheumatic Diseases: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
ABSTRACT: Hydroxychloroquine (HCQ) and chloroquine (CQ) are foundational treatments for several systemic autoimmune rheumatic diseases, including systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Concerns regarding the risk of cardiac arrhythmia and death have been raised, yet the burden of HCQ and CQ-related cardiac toxicities remains unclear. A systematic literature search was conducted in the MEDLINE and Embase databases for articles published between the earliest date and April 2023 reporting cardiac conduction abnormalities in patients with systemic autoimmune rheumatic diseases taking HCQ or CQ. Meta-analysis was performed to calculate the difference in mean corrected QT (QTc) interval and odds ratio of prolonged QTc interval in those taking HCQ or CQ versus not. Of 2673 unique records, 34 met the inclusion criteria, including 70,609 subjects. Thirty-three studies reported outcomes in HCQ and 9 in CQ. Five studies reported outcomes in RA, 11 in SLE, and 18 in populations with mixed rheumatic diseases. Eleven studies reported mean QTc and OR for prolonged QTc for meta-analysis, all reporting outcomes in HCQ. There was a significant increase in mean QTc (10.29 ms, P = 0.458) among HCQ users compared to non-HCQ users in patients with RA. There was no difference in mean QTc between HCQ and non-HCQ users in other systemic autoimmune rheumatic diseases. When rheumatic diseases were pooled, HCQ users were more likely to have prolonged QTc compared to non-HCQ users (odds ratio 1.57, 95% CI, 1.19, 2.08). The results of this study suggest that clinicians should be aware of potential adverse cardiac events of HCQ and consider QTc monitoring for patients on HCQ for the treatment of systemic autoimmune rheumatic diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.034 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".