Hydroxychloroquine-Chloroquine, QT-Prolongation, and Major Adverse Cardiac Events: A Meta-analysis and Scoping Review
Bibliographic record
Abstract
Objectives: We aimed to evaluate the high-quality literature on the frequency and nature of major adverse cardiac events (MACE) associated with either hydroxychloroquine (HCQ) or chloroquine (CQ). Data sources: We searched Medline, Embase, International Pharmaceutical Abstracts, and Cochrane Central from 1996 onward using search strategies created in collaboration with medical science librarians. Study selection and data extraction: Randomized controlled trials (RCTs) published in English language from January 1996 to September 2022, involving adult patients at least 18 years of age, were selected. Outcomes of interest were death, arrhythmias, syncope, and seizures. Random-effects meta-analyses were performed with a Treatment Arm Continuity Correction for single and double zero event studies. Data synthesis: By study drug, there were 31 HCQ RCTs (n = 6677), 9 CQ RCTs (n = 622), and 1 combined HCQ-CQ trial (n = 105). Mortality was the most commonly reported MACE at 220 of 255 events (86.3%), with no reports of torsades de pointes or sudden cardiac death. There was no increased risk of MACE with exposure to HCQ-CQ compared with control (risk ratio [RR] = 0.90, 95% CI = 0.69-1.17, I 2 = 0%). Relevance to patient care and clinical practice: These findings have important implications with respect to patient reassurance and updated guidance for prescribing practices of these medications. Conclusions: Despite listing as QT-prolonging meds, HCQ-CQ did not increase the risk of MACE.
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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 | 0.034 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".