Antiandrogens for the treatment of COVID-19 patients: A meta-analysis of randomized controlled trials.
Huzaifa Ahmad CheemaAqeeb Ur RehmanAsmaa Ahmed ElrashedyAleenah MohsinAbia ShahidMuhammad EhsanMuhammad AyyanHeba IsmailTalal AlmasPublished in: Journal of medical virology (2023)
Antiandrogens may carry a potential benefit as a therapeutic agent against COVID-19. However, studies have been yielding mixed results, thus hindering any objective recommendations. This necessitates a quantitative synthesis of data to quantify the benefits of antiandrogens. We systematically searched PubMed/MEDLINE, Cochrane Library, clinical trial registers, and reference lists of included studies to identify relevant randomized controlled trials (RCTs). Results from the trials were pooled using a random-effects model and outcomes were reported as risk ratios (RR) and mean differences (MDs) with 95% confidence intervals (CIs). Fourteen RCTs with a total sample size of 2593 patients were included. Antiandrogens yielded a significant mortality benefit (RR 0.37; 95% CI; 0.25-0.55). However, on subgroup analysis, only proxalutamide/enzalutamide and sabizabulin were found to significantly reduce mortality (RR 0.22, 95% CI: 0.16-0.30 and RR 0.42, 95% CI: 0.26-0.68, respectively), while aldosterone receptor antagonists and antigonadotropins did not show any benefit. No significant between-group difference was found in the early or late initiation of therapy. Antiandrogens also reduced hospitalizations and the duration of hospital stay, and improved recovery rates. Proxalutamide and sabizabulin may be effective against COVID-19, however, further large-scale trials are needed to confirm these findings.
Keyphrases
- sars cov
- coronavirus disease
- clinical trial
- end stage renal disease
- prostate cancer
- cardiovascular events
- randomized controlled trial
- ejection fraction
- systematic review
- newly diagnosed
- healthcare
- chronic kidney disease
- peritoneal dialysis
- case control
- phase iii
- angiotensin ii
- electronic health record
- respiratory syndrome coronavirus
- prognostic factors
- stem cells
- high resolution
- type diabetes
- emergency department
- machine learning
- combination therapy
- artificial intelligence
- mass spectrometry
- climate change
- cell therapy
- coronary artery disease
- drug induced