Efficacy of Chloroquine or Hydroxychloroquine in COVID-19 Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Chloroquine (CQ) and hydroxychloroquine (HCQ) show anti-SARS-CoV-2 activity in vitro; however, clinical studies have reported conflicting results. We sought to systematically evaluate the effect of CQ and HCQ with or without azithromycin (AZ) on outcomes of COVID-19 patients. Methods We searched Medline, Embase, EBM Reviews, Scopus, Web of Science, preprints and grey literature up to July 7, 2020. We included studies that assessed COVID-19 patients treated with CQ or HCQ, with or without AZ. We pooled only adjusted effect estimates of mortality using a random effect model. We summarized the effect of CQ or HCQ on viral clearance and ICU admission/ mechanical ventilation. Results Out of 1463 citations screened for eligibility, five RCTs and 14 cohort studies were included (20,263 hospitalized patients). Thirteen studies (1 RCT and 12 cohorts) with 15,938 patients examined the effect of HCQ on short term mortality. The pooled adjusted OR was 1.05 (95% CI 0.96-1.15, I 2 =0 %, p=0.647). Six cohort studies examined the effect of HCQ and AZ combination among 14,016 patients. The pooled adjusted OR was 0.93 (95% CI 0.79-1.11, I 2 =59.3%, p=0.003). Two cohort studies and three RCTs found no significant effect of HCQ on viral clearance. One RCT with 48 patients demonstrated improved viral clearance in patients treated with CQ and HCQ. Three cohort studies found that HCQ with or without AZ had no significant effect on mechanical ventilation/ ICU admission. Conclusion Moderate certainty evidence suggests that HCQ, with or without AZ, lacks efficacy in reducing short-term mortality in patients hospitalized with COVID-19. Summary This systematic review and meta-analysis showed that in-hospital treatment of COVID-19 patients with antimalarials medications failed to reduce short-term mortality and morbidity with potential harm if used in combination with azithromycin.
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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 | 0.004 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.028 | 0.005 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".