Association of angiotensin-converting enzyme inhibitors and angiotensin-receptor blockers with risk of mortality, severity or SARS-CoV-2 test positivity in COVID-19 patients: meta-analysis.
Mohitosh BiswasMost Sumaiya Khatun KaliPublished in: Scientific reports (2021)
The effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) in the treatment of COVID-19 are highly debated. This study was aimed to assess aggregated risk by investigating the association of ACEIs/ARBs users against non-users of ACEIs/ARBs with the risk of mortality or severe clinical manifestations or magnitude of SARS-CoV-2 test positivity in COVID-19 patients. Systematic literature search was carried out in different databases for eligible studies. The pooled relative risks (RRs) were measured using RevMan software where P<0.05 was set as statistical significance. In total, 10 studies were included in this analysis. After pooled estimation, it was demonstrated that SARS-CoV-2 positive patients taking ACEIs/ARBs were not associated with an increased risk of mortality compared to those not taking ACEIs/ARBs (RR 0.89; 95% CI 0.64-1.23; P=0.48). Furthermore, the risk of composite severe clinical manifestations was not significantly different between the positive patients with or without ACEIs/ARBs users (RR 1.29; 95% CI 0.81-2.04; P=0.28). There was no risk difference for SARS-CoV-2 test positivity in patients with or without ACEIs/ARBs users (RR 1.00; 95% CI 0.95-1.05; P=0.91). These findings may augment current professional society guidelines for not discontinuing ACEIs/ARBs in treating COVID-19 patients where it is clinically indicated.
Keyphrases
- sars cov
- angiotensin converting enzyme
- angiotensin ii
- respiratory syndrome coronavirus
- systematic review
- vascular smooth muscle cells
- cardiovascular events
- end stage renal disease
- case control
- randomized controlled trial
- risk factors
- newly diagnosed
- early onset
- ejection fraction
- cardiovascular disease
- clinical trial
- coronavirus disease
- machine learning
- risk assessment
- clinical practice
- patient reported outcomes
- open label
- binding protein
- big data
- human health
- drug induced
- combination therapy