The Cardiac Toxicity of Chloroquine or Hydroxychloroquine in COVID-19 Patients: A Systematic Review and Meta-regression Analysis
Bibliographic record
Abstract
Abstract Importance The antimalarial agents chloroquine (CQ) and hydroxychloroquine (HCQ) have been proposed as a potential treatment for COVID-19 due their effect on several cellular processes that impact viral replication. Although more than 100 ongoing trials are testing their efficacy, CQ and HCQ are being used widely in clinical practice, exposing COVID-19 patients to potentially significant cardiac adverse effects. Objective To systematically review the literature and estimate the risk of cardiac toxicity in patients receiving CQ or HCQ for COVID-19. Data Sources A systematic search was conducted on May 27, 2020 of Ovid EBM Reviews, Ovid Embase (1974+), Ovid Medline (1946+ including epub ahead of print, in-process & other non-indexed citations), Scopus (1970+) and Web of Science (1975+) and preprint servers (Medrvix and ResearchSquare) and manual search of references lists. Study Selection Studies that included COVID-19 patients treated with CQ or HCQ, with or without azithromycin, were included as follows: (1) COVID-19 patient population, (2) the study included more than 10 patients receiving either one of the medications, (3) reported electrocardiographic changes and/or cardiac arrhythmias. Data Extraction and Synthesis Study characteristics and endpoints incidence were extracted. Due to the very low incidence of torsades de pointes (TdP) and other endpoints (rare events), the arcsine transformation was used to obtain a pooled estimate of the different incidences using a random-effects meta-analysis. Meta-regression analyses were used to assess whether the incidence of different endpoints significantly varied by multiple study-level variables specified a priori. Main Outcomes and Measures Pooled Incidence of: (1) change in QTc value from baseline ≥ 60 ms, (2) QTc ≥ 500 ms, (3) the composite of endpoint 1 and 2, (4) TdP arrhythmia or ventricular tachycardia (VT) or cardiac arrest, (5) discontinuation of treatment due to drug-induced QT prolongation or arrhythmias. Results A total of 19 studies with a total of 5652 patients were included. All included studies were of high methodological quality in terms of exposure ascertainment or outcome assessment. Among 2719 patients treated with CQ or HCQ, only two episodes of TdP were reported; the pooled incidence of TdP arrhythmia or VT or cardiac arrest was 3 per 1000, 95% CI (0-21), I 2 =96%, 18 studies with 3725 patients. Among 13 studies of 4334 patients, the pooled incidence of discontinuation of CQ or HCQ due to prolonged QTc or arrhythmias was 5%, 95% CI (1-11), I 2 =98%. The pooled incidence of change in QTc from baseline of ≥ 60 ms was 7%, 95% CI (3-14), I 2 =94% (12 studies of 2008 patients). The pooled incidence of QTc ≥ 500 ms was 6%, 95% CI (2-12), I 2 =95% (16 studies of 2317 patients). Among 11 studies of 3127 patients, the pooled incidence of change in QTc from baseline of ≥ 60 ms or QTc ≥ 500 ms was 9%, 95% CI (3-17), I 2 =97%. Mean/median age, coronary artery disease, hypertension, diabetes, concomitant QT prolonging medications, ICU care, and severity of illness in the study populations explained between-studies heterogeneity. Conclusions and Relevance Treatment of COVID-19 patients with CQ or HCQ is associated with a significant risk of drug-induced QT prolongation, which is a harbinger for drug-induced TdP/VT or cardiac arrest. CQ/HCQ use resulted in a relatively higher incidence of TdP as compared to drugs withdrawn from the market for this particular adverse effect. Therefore, these agents should be used only in the context of randomized clinical trials, in patients at low risk for drug-induced QT prolongation, with adequate safety monitoring. Key Points Question What are the risks of different cardiac toxicities in patients receiving chloroquine (CQ) or hydroxychloroquine (HCQ) for COVID-19. Findings In this systematic review, treatment of COVID-19 patients with CQ or HCQ is associated with a clinically significant risk of drug-induced QT prolongation, and torsades de pointes (TdP) arrhythmia/ventricular tachycardia/cardiac arrest in a relatively higher incidence compared to drugs withdrawn from the market for such adverse effects. Meaning These agents should be used only in the context of clinical trials, in patients at low risk for drug-induced QT prolongation, with adequate safety monitoring.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".