Chloroquine and hydroxychloroquine are associated with reduced cardiovascular risk: a systematic review and meta-analysis
Bibliographic record
Abstract
Background and aims: Chloroquine (CQ) and hydroxychloroquine (HCQ) are widely used in patients with rheumatic diseases, but their effects on the cardiovascular system remain unclear. We aimed to assess whether CQ/HCQ could reduce the risk of cardiovascular disease (CVD). Materials and methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Embase, and the ClinicalTrials.gov for studies investigating the association between CQ/HCQ and the risk of CVD from inception to 20 December 2017. We carried out the quality assessment using the Newcastle-Ottawa Quality Assessment Scale (NOS). Random-effects model was used to pool the risk estimates relative ratio (RR), hazard ratio (HR) or odds ratio (OR) with 95% confidence interval (CI) for the outcomes. Results: A total of 19 studies (7 case-control studies, 12 cohort studies, and no clinical trials) involving 19,679 participants were included in the meta-analysis. Pooled results for HRs or RRs showed that CQ/HCQ was associated with a significantly reduced risk of CVD (pooled RR 0.72, 95% CI 0.56–0.94, p =0.013). Results based on ORs showed a similar tendency towards a reduced risk of CVD with CQ/HCQ (pooled OR 0.41, 95% CI 0.25–0.69, p =0.001). Conclusion: Our results suggested that CQ/HCQ was associated with a reduced risk of CVD in patients with rheumatic diseases. Randomized trials are needed to confirm the potential of CQ/HCQ in cardiovascular prevention in patients with and without rheumatic diseases. Keywords: chloroquine, hydroxychloroquine, antimalarials, cardiovascular disease, atherosclerosis, drug repurpose and rheumatic diseases, systematic review
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".