Title: RAAS Inhibition, Mortality, and Severity in COVID-19 Patients: A Systematic Review and Meta-Analysis
Abstract: Background: The effect of angiotensin-receptor blockers (ARBs) and angiotensinconverting enzyme inhibitors (ACEi) on outcome and severity in COVID-19 patients has been postulated Methods: We performed a systematic review in different databases to identify studies and research work that assessed the association of ACEi/ARBs on the severity of illness and mortality in COVID-19 subjects Inclusion criteria for our meta-analysis were all studies that included human subjects with COVID-19 infection, reported mortality and severity of the disease, and described ACEi/ARB treatment The data collected were the name of the first author, journal title, the country of the study, sample size, relative risk and confidence intervals for association of ACEi/ARB treatment and mortality and severity We used the random-effects model for the meta-analysis and the funnel plot analysis to assess potential publication bias Results: Out of 4,702 records reviewed in different databases, 11 papers were included in our meta-analysis Altogether, 8,643 patients were included in the final analysis Random effects model (REM) for the relationship between ACEi/ARB and survival showed that ACEi/ARB does not affect survival (relative risk [RR]=0 81, confidence interval ranges [CIR] from 0 53 to 1 23) There was no evidence of heterogeneity with I-squared =25 5% and p<0 235 By applying Egger's test, there was no evidence of small studies effect with P=0 64 REM for the relationship between ACEi/ARB and disease severity showed that ACEi/ARB are not related to disease severity (RR=0 90, CIR from 0 70 to 1 15) There was evidence of heterogeneity with I-squared =56 2% and p=0 01 By applying Egger's test, there was no evidence of small studies effect with P=0 93 Conclusions: Based on the results of this meta-analysis, ACEi/ARB are not associated with increased mortality or severity in COVID-19 subjects
Publication Year: 2020
Publication Date: 2020-10-01
Language: en
Type: review
Indexed In: ['crossref']
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot