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Review

A Meta-Analysis of the Impact of Using Angiotensin-Converting Enzyme Inhibitors (ACEIs) or Angiotensin II Receptor Blockers (ARBs) on Mortality, Severity, and Healthcare Resource Utilization in Patients with COVID-19

by
Ruijuan Li
,
Jie Zhang
and
Liang Ren
*
Department of Forensic Medicine, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430032, China
*
Author to whom correspondence should be addressed.
Adv. Respir. Med. 2025, 93(1), 4; https://doi.org/10.3390/arm93010004
Submission received: 3 January 2025 / Accepted: 26 January 2025 / Published: 18 February 2025
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Abstract

:

Highlights

This study addresses a critical question about the impact of ACEIs and ARBs on COVID-19, offering valuable insights through a robust meta-analysis. Its strength lies in professional statistical evaluation across diverse datasets, resolving prior inconsistencies.
What are the main findings?
  • The use of ACEIs and ARBs presents both advantages and disadvantages for patients with COVID-19.
  • The utilization of ACEIs and ARBs does not demonstrate a substantial correlation with mortality, severity, or healthcare resource utilization in patients with COVID-19.
What is the implication of the main finding?
  • The utilization of ACEIs and ARBs in patients diagnosed with COVID-19 is associated with benefits that outweigh the potential drawbacks.
  • The utilization of ACEIs and ARBs has been shown to be safe medical practice.

Abstract

Objective: The primary objective of this study is to explore the potential link between the utilization of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs) and its impact on mortality, disease severity, and healthcare resource utilization in individuals diagnosed with COVID-19. We aim to establish a solid theoretical foundation for safe and effective clinical medications. Methods: We conducted a comprehensive search of various databases, including CNKI, PubMed, Science, Cell, Springer, Nature, Web of Science, and Embase. We also traced the literature of the included studies to ensure a thorough analysis of the available evidence. After applying a set of inclusion and exclusion criteria, we ultimately included a total of 41 articles in our analysis. To determine the overall effect size for dichotomous variables, we used the Mantel–Haenszel odds ratio in random effect models. For continuous variables, we calculated the inverse variance SMD using random effect models. To assess the outcomes and heterogeneity, we considered p-values (p < 0.05) and I2 values for all outcomes. We performed multivariate and univariate meta-regression analyses using the maximum likelihood approach with the CMA 3.0 software. Results: The results of our analysis indicated that the use of ACEIs or ARBs did not significantly influence mortality (OR = 1.10, 95% CI 0.83–1.46, p = 0.43, I2 = 84%), severity (OR = 0.99, 95% CI 0.68–1.45, p = 0.98, I2 = 84%), or healthcare resource utilization (SMD = 0.03, 95% CI 0.06–0.12, p = 0.54, I2 = 37%) in patients with COVID-19 compared to those not taking ACEIs or ARBs. The multivariate meta-regression analysis model explained 63%, 31%, and 100% of the sources of heterogeneity for the three outcome indicators. Conclusions: The use of ACEIs and ARBs is not significantly correlated with mortality, severity, or healthcare resource utilization in patients with COVID-19, indicating safe clinical use of the medications.

1. Introduction

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the pathogen of COVID-19 [1]. It was declared a pandemic on 30 January 2020 [2,3]. The COVID-19 pandemic has overwhelmed the healthcare systems of most countries and led to substantial economic losses. As of 5:34 p.m. Central European Time (CET) on 1 December 2022, COVID-19 cases, including 6,615,258 deaths, have been reported to the World Health Organization (WHO), and as of 29 November 2022, a total of 13,042,112,489 doses of vaccines have been vaccinated [4]. SARS-CoV-2 is usually transmitted by respiratory tract. The main symptoms include fever, cough, weakness, sputum, hemoptysis, headache, diarrhea, lymphocytopenia, and shortness of breath [5].
The renin-angiotensin aldosterone system (RAAS) consists of two principal mutually antagonistic axes: the angiotensin-converting enzyme/angiotensin II/angiotensin II type 1 receptor (ACE/AngII/AT1R) axis and the angiotensin-converting enzyme 2/angiotensin-(1-7)/mas receptor (ACE2/Ang(1-7)/MasR) axis [6]. ACE degrades Ang I to Ang II. The harmful effects of Ang II-mediated by AT1R are well recognized, such as pro-inflammation, fibrosis, elevated blood pressure, and cardiovascular damage [7]. ACE2 mainly degrades Ang II to the Ang-(1-7), which binds to Mas receptors and antagonizes the ACE/Ang II/AT1R axis [8]. Coronaviruses have many stinging glycoproteins on their surface. ACE2 is widely present as a receptor for proteins in the lung, kidney, testis, adipose, brain tissue, and vascular smooth muscle cells. The harmful effects of Ang II-mediated by AT1R are well recognized [9,10]. Viral entry into the human body acts on ACE2, leading to downregulation of ACE2 and substantial accumulation of Ang II, resulting in a range of deleterious effects as described above. Therefore, ACE2 is considered an important target for the virulence and entry efficiency of the SARS-CoV-2.
In addition to general supportive therapy and mechanical treatment of the respiratory system, a number of drugs are widely used in clinical management, such as angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). ACEIs reduce Ang II production by inhibiting ACE, and ARBs block the AT1R [11]. These drugs counteract the cytokine storm of SARS-CoV-2 and lung injury, and are also useful in treating comorbidities in patients with COVID-19 [12,13]. However, the use of these medications elevates transmembrane ACE2, thus increasing ACE2 expression in the presence of SARS-CoV-2. This leads to an increase in viral entry and replication in the body [14,15,16,17]. A controversial question was proposed, asking whether we should increase ACE2 levels in tissues, focus on suppressing the inflammatory response and treating comorbidities, or promote a decrease in ACE2 levels in tissues to reduce viral entry and replication.
The essence of addressing the above question is to examine the use of ACEIs and ARBs that is beneficial or detrimental to the outcomes ultimately exhibited by patients with COVID-19. Therefore, this article discusses the effect of ACEIs or ARBs use on mortality, severity, and healthcare resource utilization in patients with COVID-19 by including many previous studies and using meta-analysis methods from the perspective of evidence-based medicine (EBM). To enhance the objectivity and credibility of the data for the latter two outcome indicators, we quantified “severity” as “the number of intensive care unit (ICU) admissions” and “healthcare resource utilization” as “the length of hospital stays”.

2. Materials and Methods

2.1. Search Strategy

In this study, the English search terms “SARS-CoV-2, ACEIs, ARBs” and the related Chinese search terms were used to search the databases of CNKI, PubMed, Science, Cell, Springer, Nature, Web of Science, and Embase. The search period was from the establishment of each database to October 2022. Some new articles were added by tracing the cited literature of the retrieved articles.

2.2. Inclusions and Exclusions Criteria

Inclusions: (1) domestic and foreign published randomized controlled trials, cohort studies, retrospective studies, and case studies; (2) all patients tested positive for SARS-CoV-2 infection; (3) patients in the control group discontinued, did not take ACEI/ARB drugs or took other anti-hypertensive drugs. The experimental group took or continued to take ACEI or ARB drugs; (4) outcome indicators included at least one of the following: ① number of deaths; ② ICU admissions; ③ length of hospital stays. Exclusions: ① articles without randomized controlled trials; ② animal experiments; ③ duplicate publications; ④ articles with no access to original data; ⑤ review and hypothesis articles; ⑥ articles with randomized controlled trial content that did not match the study content; ⑦ articles with incomplete or severely missing data; ⑧ articles without any of the above-mentioned outcome indicators.

2.3. Literature Quality Assessment

The risk of bias in the included literature was evaluated using the Cochrane Collaboration (https://training.Cochrane.org/handbook (accessed on 24 November 2024)) for the included literature, judged by ① whether correct use of randomization method; ② whether allocation concealment was used properly; ③ whether blinding was used correctly for patients; ④ whether the study personnel proper use of blinding; ⑤ whether the outcomes, as well as data, were complete; ⑥ whether the outcomes were selectively reported; ⑦ whether there was relevant bias.

2.4. Data Extraction

The following characteristics of the control and experimental groups from the included literature were extracted for this study: first author’s name, number of people in the group, median or mean of age in the group, number of deaths in the group, length of hospital stays, and ICU admissions. It is worth mentioning that the software used in this study uses the mean and standard deviation (rather than the median) for the statistical treatment of continuous variables, so we used the method recommended by Luo [18] and Wan’s [19] method to estimate the mean and standard deviation using the median, the first quartile, the third quartile, and the sample size (the quantification of the continuous variables in some of the studies used the median rather than the mean).

2.5. Statistical Analysis

We performed a meta-analysis of mortality, ICU admissions, and length of hospital stays in the two groups using Review Manager 5.3 software. It was assumed that the study outcomes reporting the two groups of patients were independent. Due to the expected heterogeneity, data were analyzed using the random effects model. The model showed differences between studies. Meta-analysis was performed using the Mantel–Haenszel odds ratio for dichotomous data (mortality and ICU admissions). For continuous variables (length of hospital stays). Meta-analysis was performed using inverse variance standardized mean difference (SMD). All results were judged by values for correlation (p) and heterogeneity (I2); if I2 ≥ 50%, it is high heterogeneity, and if I2 < 50%, it indicates low heterogeneity.

2.6. Publication Bias of Assessment

Because the three indicators analyzed were included in more than ten articles, we used funnel plots to show the included essays’ publication bias visually. To make the results more objective, we used Egger’s regression to assess the bias results with p-value (p < 0.05) and intercept (the closer to 0, the smaller the risk of bias).

2.7. Sensitivity Analysis

Sensitivity analysis was performed on the literature included in the three indicators to test the reliability of the meta-analysis, using CMA 3.0 software with the “leave-one-out” to test whether each piece of literature had a significant effect on the combined effect. The results were quantitatively assessed using p-values. indicating that the likelihood of publication bias is almost non-existent.

2.8. Statement

The study was registered in the PROSPERO (CRD42023389240).

3. Results

3.1. Articles Search Results

A total of 122 papers were researched according to the above search strategy. In total, 53 papers were added by tracing the references in some studies, and 43 duplicates were deleted by using EndNote X9 software. In addition, 61 essays were excluded by reading the abstracts and introduction, and another 31 papers were excluded by reading the full text. A total of 40 papers were finally included, of which 38 were used for mortality analysis [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57], 21 for analysis of the ICU admissions [21,22,23,24,26,27,29,30,33,35,36,37,38,39,40,41,44,45,54,58], and 19 for the analysis of the length of hospital stays [20,21,23,24,26,38,40,41,43,47,48,51,53,54,55,57,58,59,60] (Figure 1).

3.2. Basic Characteristics of the Included Studies

The included literature was in English, with 21,458 patients in the control group and 7300 patients in the experimental group for mortality, 587 patients in the control group and 484 patients in the experimental group for ICU admissions, and 2509 patients in the control group and 1681 patients in the experimental group for the length of hospital stays. Furthermore, the countries, the proportion of female patients in the sample, and some underlying diseases (hypertension, diabetes, cerebrovascular, and respiratory diseases) were extracted and analyzed (Table 1).

3.3. Quality Evaluation of the Included Literature

Among the included studies, eight articles described the method of random assignment sequence generation and concealment of the random assignment scheme, and two studies described the method of blinding subjects and experimenters, providing all information relevant to the validity of the blinding. Four papers blinded outcome assessors; data in thirty-nine articles were lost. Thirty-one articles had no selective reporting, and thirty-six papers had no other factors causing the risk of bias (Figure 2).

3.4. Meta-Analysis Outcomes

Meta-analysis indicates the heterogeneity ((I2 = 84%)) of mortality (OR = 1.10, 95% CI 0.83–1.46, p = 0.43) between studies was very high (Figure 3). For ICU admissions (OR = 0.99, 95% CI 0.68–1.45, p = 0.98). Heterogeneity was very high (I2 = 84%) (Figure 4). For the length of hospital stays (SMD = 0.03, 95% CI −0.06–0.12, p = 0.54). Heterogeneity was low (I2 = 37%) (Figure 5). In conclusion, the results of the meta-analysis showed that the use of ACEIs/ARBs was not associated with three outcome indicators of COVID-19 infections compared to not using ACEIs/ARBs. However, there is increasing heterogeneity in mortality and length of hospital stays between studies.

3.5. Meta-Regression Outcomes

Multivariate meta-regression was performed to explain variations in the association between mortality and being on ACEIs/ARBs revealed. Age means, the proportion of female patients in the sample, and the proportion of some underlying diseases (hypertension, diabetes, cerebrovascular, and respiratory diseases) in included studies covariates to be significant together and explained R2 = 63% of the between-study heterogeneity in mortality (p = 0.0000). The above multivariate was significant as a source of heterogeneity. We performed further univariate regression with mean age (p = 0.0124), the proportion of female subjects (p = 0.0013), the proportion of hypertensive patients (p = 0.0083), the proportion of diabetic patients (p = 0.1734), the proportion of cardiovascular patients (p = 0.3317) and the ratio of patients with respiratory system diseases (p = 0.2143), and the countries (p = 0.9630). Therefore, the proportion of female and hypertensive patients, and mean age, was the primary source of heterogeneity as seen by further univariate regression analysis (Figure 6).
For ICU admissions, covariates are significant in included studies and explained R2 = 31% of the between-study heterogeneity (p = 0.0770). The above multivariate was not significant as a source of heterogeneity. We performed further univariate regression for mean age (p = 0.6623), the proportion of female subjects (p = 0.5185), the proportion of hypertensive patients (p = 0.0856), the proportion of diabetic patients (p = 0.3017), the proportion of cardiovascular patients (p = 0.8634) and the ratio of patients with respiratory system diseases (p = 0.7387), and the countries (p = 0.5508). Therefore, we did not find a specific source of heterogeneity. However, it was within the acceptable range due to its low heterogeneity (Figure 7).
Moreover, covariates for the length of hospital stays are significant and only explained R2 = 100% of the between-studies heterogeneity (p = 0.3118 > 0.05). Mean age (p = 0.0368), the proportion of females (p = 0.8902), the proportion of patients with hypertension (p = 0.6701), the proportion of patients with diabetes (p = 0.2100), the proportion of patients with cardiovascular disease (p = 0.0312), and the ratio of patients with respiratory system diseases (p = 0.0597), and the countries (p = 0.6585). By further univariate regression we found that mean age and patients with cardiovascular disease are specific sources of heterogeneity (Figure 8).

3.6. Publication Bias

For the funnel plot, although the effect values for the number of deaths in each study were concentrated at the top of the graph and distributed on both sides of the total effect, initially indicating that the publication bias was not significant. However, Egger’s regression intercept was −1.92 (95% CI −3.60–0.60, p = 0.01), indicating some publication bias (Figure 9). For the ICU admissions, the funnel plot showed that the effect values were evenly distributed on both sides of the total effect, and the intercept of Egger’s regression was −0.44 (95% CI −1.83–0.95, p = 0.51). Both results indicated that there was no significant publication bias (Figure 10). The effect values for the literature included in the study of the length of hospital stays were also evenly distributed on both sides of the total effect and clustered at the top of the graph, with an intercept of 0.35 (95% CI −3.14–3.84, p = 0.84); also confirming that there was no significant publication bias in the included studies (Figure 11).

3.7. Sensitivity Results

Sensitivity analysis is a method to test the stability of the meta-analysis results. CMA 3.0 software was used to perform sensitivity analysis on the included literature of the three indicators by the leave-one-out method. The results were combined after excluding the included literature one by one. The effect on the outcome indicators was not significant, indicating that the results of this study were stable (Figure 12, Figure 13 and Figure 14).

4. Discussion

ACEIs and ARBs are widely utilized in the management of COVID-19 patients with multiple comorbidities, such as hypertension and ischemic heart disease [61]. On the one hand, ACEI inhibits ACE and reduces Ang I production, while ARBs act by blocking the action of Ang II on its pro-oxidant/pro-inflammatory receptor AT1. Thus, both types of drugs downregulate the activity of the RASS pro-inflammatory axis [62]. ACEIs and ARBs have also been shown to reduce lung injury in mouse models constructed in several previous studies [63]. The two drugs also significantly modified pulmonary fibrosis while treating different patients with viral pneumonia [64,65]. The above results are undoubtedly beneficial for the treatment of patients with COVID-19. However, ACEI also has significant drawbacks, such as a reduced effect of Ang II on the anti-inflammatory receptor AT2, in particular, ACEI can also induce an increase in bradykinin, which can participate in blood pressure regulation and inflammation by increasing vascular permeability and vasodilatory effects. It has a robust pro-inflammatory effect [11]. The pro-inflammatory effects of bradykinin may counteract ACEI-induced downregulation of the Ang II/AT1 pro-inflammatory axis [66]. More importantly, using both drugs promotes the expression of ACE2, which increases viral replication and viral load in the body and leads to more severe disease in patients with COVID-19. Therefore, it is necessary to investigate whether ACEIs or ARBs reduce the body’s inflammatory response and counteracts the cytokine storm in patients with COVID-19 or whether they tend to increase the amount of virus entering the body, causing exacerbation of the disease in patients with COVID-19. Our meta-analysis illustrates that the use of the two drugs does not affect these three outcome indicators from the perspective of whether the use of the two drugs adds to the mortality, severity, and healthcare resource utilization of COVID-19.
The results of our meta-analysis showed that the use of ACEIs and ARBs had no significant effect on mortality, severity, or healthcare resource utilization for patients with COVID-19, and the multivariate meta-regression model for mortality showed that 63% of the heterogeneity between studies could be explained by mean age, the proportion of female, countries, proportion patients of hypertensive, diabetes, cardiovascular disease, and respiratory disease explained. The results of further univariate meta-regression revealed that the variable proportion of females was the primary source of heterogeneity. The exact cause needs further study. The results of the multivariate regression analysis of the ICU admissions and length of hospital stays showed that the above-mentioned moderating variables were able to explain 31% and 100% of the fraction. The heterogeneity itself was low for the former, for which further univariate regression did not find a source of heterogeneity within an acceptable range, and for the latter, which had a very high heterogeneity (I2 = 84%). However, our model did not find a specific source of heterogeneity.
This study has some advantages: it had a larger sample size, stricter inclusion and exclusion criteria of studies, more consistent data, and more credible results. We also cited univariate and multivariate meta-regression models and discussed the sources of heterogeneity between studies. This analysis also contains some limitations: ① the data in some studies were incomplete. ② Medical records, which may be less reliable, screened for use of the two drugs. ③ High heterogeneity in the included studies due to variations in clinical studies, and to deal with this difficulty, we used meta-regression to explore the sources of heterogeneity. We identified some of the sources of inter-study heterogeneity, however, the reasons for the impact of heterogeneity on the results are not explored in this paper. ④ Ethnic differences in the included study samples may have created high heterogeneity between studies, but because the ethnic classification of the samples is not mentioned in the literature, we could not discuss it.

5. Conclusions

Due to the lack of statistical significance of the findings of the meta-analysis, it concluded that the use of ACEIs or ARBs was not significantly correlating with mortality, severity, and healthcare resource utilization in patients with COVID-19, which provides a clinical basis for safe drug use. More extensive clinical trial studies are expected to provide further relevant proof.

Author Contributions

Conceptualization, R.L.; methodology, R.L.; software, R.L.; formal analysis, R.L.; investigation, R.L.; writing—original draft preparation, R.L. and L.R.; writing—review and editing, R.L. and J.Z.; visualization, L.R.; supervision, L.R.; project administration, L.R.; funding acquisition, L.R. All authors have read and agreed to the published version of the manuscript.

Funding

Study of Nephrin, Podocin, and CD2AP Expression in podocyte with COVID-19, Hubei Chongxin Judical Expertise Center (number 0231515045).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. de Oliveira, P.G.; Termini, L.; Durigon, E.L.; Lepique, A.P.; Sposito, A.C.; Boccardo, E. Diacerein: A potential multi-target therapeutic drug for COVID-19. Med. Hypotheses 2020, 144, 109920. [Google Scholar] [CrossRef] [PubMed]
  2. Degnah, A.A.; Al-Amri, S.S.; Hassan, A.M.; Almasoud, A.S.; Mousa, M.; Almahboub, S.A.; Alhabbab, R.Y.; Mirza, A.A.; Hindawi, S.I.; Alharbi, N.K.; et al. Seroprevalence of MERS-CoV in healthy adults in western Saudi Arabia, 2011–2016. J. Infect. Public Health 2020, 13, 697–703. [Google Scholar] [CrossRef] [PubMed]
  3. WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group; Sterne, J.A.C.; Murthy, S.; Diaz, J.V.; Slutsky, A.S.; Villar, J.; Angus, D.C.; Annane, D.; Azevedo, L.C.P.; Berwanger, O.; et al. Association Between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients with COVID-19: A Meta-analysis. JAMA 2020, 324, 1330–1341. [Google Scholar]
  4. Singh, A.; Gupta, V. SARS-CoV-2 therapeutics: How far do we stand from a remedy? Pharmacol. Rep. 2021, 73, 750–768. [Google Scholar] [CrossRef] [PubMed]
  5. WHO Coronavirus (COVID-19) Dashboard|WHO Coronavirus (COVID-19) Dashboard with Vaccination Data. Available online: https://covid19.who.int/ (accessed on 24 November 2024).
  6. Xu, Y.; Rong, J.; Zhang, Z. The emerging role of angiotensinogen in cardiovascular diseases. J. Cell. Physiol. 2021, 236, 68–78. [Google Scholar] [CrossRef]
  7. Pang, X.; Cui, Y.; Zhu, Y. Recombinant human ACE2: Potential therapeutics of SARS-CoV-2 infection and its complication. Acta Pharmacol. Sin. 2020, 41, 1255–1257. [Google Scholar] [CrossRef]
  8. Medina, D.; Arnold, A.C. Angiotensin-(1-7): Translational Avenues in Cardiovascular Control. Am. J. Hypertens. 2019, 32, 1133–1142. [Google Scholar] [CrossRef] [PubMed]
  9. Hikmet, F.; Méar, L.; Edvinsson, Å.; Micke, P.; Uhlén, M.; Lindskog, C. The protein expression profile of ACE2 in human tissues. Mol. Syst. Biol. 2020, 16, e9610. [Google Scholar] [CrossRef]
  10. Singh, M.; Bansal, V.; Feschotte, C. A single-cell RNA expression map of human coronavirus entry factors. Cell Rep. 2020, 32, 108175. [Google Scholar] [CrossRef] [PubMed]
  11. Labandeira-Garcia, J.L.; Labandeira, C.M.; Valenzuela, R.; Pedrosa, M.A.; Quijano, A.; Rodriguez-Perez, A.I. Drugs Modulating Renin-Angiotensin System in COVID-19 Treatment. Biomedicines 2022, 10, 502. [Google Scholar] [CrossRef]
  12. Guan, W.J.; Liang, W.H.; Zhao, Y.; Liang, H.R.; Chen, Z.S.; Li, Y.M.; Liu, X.Q.; Chen, R.C.; Tang, C.L.; Wang, T.; et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: A nationwide analysis. Eur. Respir. J. 2020, 55, 2000547. [Google Scholar] [CrossRef]
  13. Wang, D.; Hu, B.; Hu, C.; Zhu, F.; Liu, X.; Zhang, J.; Wang, B.; Xiang, H.; Cheng, Z.; Xiong, Y.; et al. Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA 2020, 323, 1061–1069. [Google Scholar] [CrossRef]
  14. Huang, Y.; Yang, C.; Xu, X.F.; Xu, W.; Liu, S.W. Structural and functional properties of SARS-CoV-2 spike protein: Potential antivirus drug development for COVID-19. Acta Pharmacol. Sin. 2020, 41, 1141–1149. [Google Scholar] [CrossRef] [PubMed]
  15. Tipnis, S.R.; Hooper, N.M.; Hyde, R.; Karran, E.; Christie, G.; Turner, A.J. A human homolog of angiotensin-converting enzyme. Cloning and functional expression as a captopril-insensitive carboxypeptidase. J. Biol. Chem. 2000, 275, 33238–33243. [Google Scholar] [CrossRef]
  16. Walls, A.C.; Park, Y.J.; Tortorici, M.A.; Wall, A.; McGuire, A.T.; Veesler, D. Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell 2020, 181, 281–292. [Google Scholar] [CrossRef]
  17. Yan, R.; Zhang, Y.; Li, Y.; Xia, L.; Guo, Y.; Zhou, Q. Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science 2020, 367, 1444–1448. [Google Scholar] [CrossRef]
  18. Luo, D.; Wan, X.; Liu, J.; Tong, T. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Stat. Methods Med. Res. 2018, 27, 1785–1805. [Google Scholar] [CrossRef] [PubMed]
  19. Wan, X.; Wang, W.; Liu, J.; Tong, T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med. Res. Methodol. 2014, 14, 135. [Google Scholar] [CrossRef] [PubMed]
  20. Bauer, A.; Schreinlechner, M.; Sappler, N.; Dolejsi, T.; Tilg, H.; Aulinger, B.A.; Weiss, G.; Bellmann-Weiler, R.; Adolf, C.; Wolf, D.; et al. Discontinuation versus continuation of renin-angiotensin-system inhibitors in COVID-19 (ACEI-COVID): A prospective, parallel group, randomised, controlled, open-label trial. Lancet Respir. Med. 2021, 9, 863–872. [Google Scholar] [CrossRef] [PubMed]
  21. Bae, D.J.; Tehrani, D.M.; Rabadia, S.V.; Frost, M.; Parikh, R.V.; Calfon-Press, M.; Aksoy, O.; Umar, S.; Ardehali, R.; Rabbani, A.; et al. Angiotensin Converting Enzyme Inhibitor and Angiotensin II Receptor Blocker Use Among Outpatients Diagnosed with COVID-19. Am. J. Cardiol. 2020, 132, 150–157. [Google Scholar] [CrossRef] [PubMed]
  22. Bean, D.M.; Kraljevic, Z.; Searle, T.; Bendayan, R.; Kevin, O.; Pickles, A.; Folarin, A.; Roguski, L.; Noor, K.; Shek, A.; et al. Angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers are not associated with severe COVID-19 infection in a multi-site UK acute hospital trust. Eur. J. Heart Fail. 2020, 22, 967–974. [Google Scholar] [CrossRef]
  23. Cetinkal, G.; Kocas, B.B.; Ser, O.S.; Kilci, H.; Yildiz, S.S.; Celebi, S.N.; Verdi, Y.; Altinay, M.; Kilickesmez, K. The Association Between Chronic Use of Renin–Angiotensin-Aldosterone System Blockers and in-Hospital Adverse Events among COVID-19 Patients with Hypertension. Med. Bull. Sisli Etfal Hosp. 2020, 54, 399–404. [Google Scholar] [CrossRef] [PubMed]
  24. Chaudhri, I.; Koraishy, F.M.; Bolotova, O.; Yoo, J.; Marcos, L.A.; Taub, E.; Sahib, H.; Bloom, M.; Ahmad, S.; Skopicki, H.; et al. Outcomes Associated with the Use of Renin-Angiotensin-Aldosterone System Blockade in Hospitalized Patients with SARS-CoV-2 Infection. Kidney360 2020, 1, 801–809. [Google Scholar] [CrossRef]
  25. Choi, H.K.; Koo, H.; Seok, H.; Jeon, J.H.; Choi, W.S.; Kim, D.J.; Park, D.W.; Han, E. ARB/ACEI use and severe COVID-19: A nationwide case-control study. medRxiv 2020. [Google Scholar] [CrossRef]
  26. Cohen, J.B.; Hanff, T.C.; William, P.; Sweitzer, N.; Rosado-Santander, N.R.; Medina, C.; Rodriguez-Mori, J.E.; Renna, N.; Chang, T.I.; Corrales-Medina, V.; et al. Continuation versus discontinuation of renin-angiotensin system inhibitors in patients admitted to hospital with COVID-19: A prospective, randomised, open-label trial. Lancet Respir. Med. 2021, 9, 275–284. [Google Scholar] [CrossRef]
  27. Covino, M.; De Matteis, G.; Burzo, M.L.; Santoro, M.; Fuorlo, M.; Sabia, L.; Sandroni, C.; Gasbarrini, A.; Franceschi, F.; Gambassi, G.; et al. Angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers and prognosis of hypertensive patients hospitalised with COVID-19. Intern. Med. J. 2020, 50, 1483–1491. [Google Scholar] [CrossRef]
  28. Cugno, M.; Gualtierotti, R.; Casazza, G.; Tafuri, F.; Ghigliazza, G.; Torri, A.; Costantino, G.; Montano, N.; Peyvandi, F. Mortality in Patients with COVID-19 on Renin Angiotensin System Inhibitor Long-Term Treatment: An Observational Study Showing that Things Are Not Always as They Seem. Adv. Ther. 2021, 38, 2709–2716. [Google Scholar] [CrossRef] [PubMed]
  29. Duarte, M.; Pelorosso, F.; Nicolosi, L.N.; Salgado, M.V.; Vetulli, H.; Aquieri, A.; Azzato, F.; Castro, M.; Coyle, J.; Davolos, I.; et al. Telmisartan for treatment of Covid-19 patients: An open multicenter randomized clinical trial. eClinicalMedicine 2021, 37, 100962. [Google Scholar] [CrossRef]
  30. Felice, C.; Nardin, C.; Di Tanna, G.L.; Grossi, U.; Bernardi, E.; Scaldaferri, L.; Romagnoli, M.; Tonon, L.; Cavasin, P.; Novello, S.; et al. Use of RAAS Inhibitors and Risk of Clinical Deterioration in COVID-19: Results From an Italian Cohort of 133 Hypertensives. Am. J. Hypertens. 2020, 33, 944–948. [Google Scholar] [CrossRef]
  31. Fosbøl, E.L.; Butt, J.H.; Østergaard, L.; Andersson, C.; Selmer, C.; Kragholm, K.; Schou, M.; Phelps, M.; Gislason, G.H.; Gerds, T.A.; et al. Association of Angiotensin-Converting Enzyme Inhibitor or Angiotensin Receptor Blocker Use with COVID-19 Diagnosis and Mortality. JAMA 2020, 324, 168–177. [Google Scholar] [CrossRef]
  32. Genet, B.; Vidal, J.-S.; Cohen, A.; Boully, C.; Beunardeau, M.; Harlé, L.M.; Gonçalves, A.; Boudali, Y.; Hernandorena, I.; Bailly, H.; et al. COVID-19 In-Hospital Mortality and Use of Renin-Angiotensin System Blockers in Geriatrics Patients. J. Am. Med. Dir. Assoc. 2020, 21, 1539–1545. [Google Scholar] [CrossRef]
  33. Hakeam, H.A.; Alsemari, M.; Al Duhailib, Z.; Ghonem, L.; Alharbi, S.A.; Almutairy, E.; Bin Sheraim, N.M.; Alsalhi, M.; Alhijji, A.; AlQahtani, S.; et al. Association of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Blockers with Severity of COVID-19: A Multicenter, Prospective Study. J. Cardiovasc. Pharmacol. Ther. 2021, 26, 244–252. [Google Scholar] [CrossRef] [PubMed]
  34. Huang, Z.; Cao, J.; Yao, Y.; Jin, X.; Luo, Z.; Xue, Y.; Zhu, C.; Song, Y.; Wang, Y.; Zou, Y.; et al. The effect of RAS blockers on the clinical characteristics of COVID-19 patients with hypertension. Ann. Transl. Med. 2020, 8, 430. [Google Scholar] [CrossRef] [PubMed]
  35. Kim, J.H.; Baek, Y.-H.; Lee, H.; Choe, Y.J.; Shin, H.J.; Shin, J.-Y. Clinical outcomes of COVID-19 following the use of angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers among patients with hypertension in Korea: A nationwide study. Epidemiol. Health 2021, 43, e2021004. [Google Scholar] [CrossRef]
  36. Kuzeytemiz, M.; Tenekecioglu, E. Effect of renin-angiotensin system blocker on COVID-19 in young patients with hypertension. J. Investig. Med. 2022, 70, 786–791. [Google Scholar] [CrossRef]
  37. Lafaurie, M.; Martin-Blondel, G.; Delobel, P.; Charpentier, S.; Sommet, A.; Moulis, G. Outcome of patients hospitalized for COVID-19 and exposure to angiotensin-converting enzyme inhibitors and angiotensin-receptor blockers in France: Results of the ACE-CoV study. Fundam. Clin. Pharmacol. 2021, 35, 194–203. [Google Scholar] [CrossRef]
  38. Lam, K.W.; Chow, K.W.; Vo, J.; Hou, W.; Li, H.; Richman, P.S.; Mallipattu, S.K.; Skopicki, H.A.; Singer, A.J.; Duong, T.Q. Continued In-Hospital Angiotensin-Converting Enzyme Inhibitor and Angiotensin II Receptor Blocker Use in Hypertensive COVID-19 Patients Is Associated with Positive Clinical Outcome. J. Infect. Dis. 2020, 222, 1256–1264. [Google Scholar] [CrossRef]
  39. Mehta, N.; Kalra, A.; Nowacki, A.S.; Anjewierden, S.; Han, Z.; Bhat, P.; Carmona-Rubio, A.E.; Jacob, M.; Procop, G.W.; Harrington, S.; et al. Association of Angiotensin II Receptor Blockers and Angiotensin-Converting Enzyme Inhibitors on COVID-19-Related Outcome. JAMA Cardiol. 2020, 5, 1020–1026. [Google Scholar] [CrossRef] [PubMed]
  40. Li, J.; Wang, X.; Chen, J.; Zhang, H.; Deng, A. Association of Renin-Angiotensin System Inhibitors with Severity or Risk of Death in Patients with Hypertension Hospitalized for Coronavirus Disease 2019 (COVID-19) Infection in Wuhan, China. JAMA Cardiol. 2020, 5, 825–830. [Google Scholar] [CrossRef]
  41. Lim, J.-H.; Cho, J.-H.; Jeon, Y.; Kim, J.H.; Lee, G.Y.; Jeon, S.; Noh, H.W.; Lee, Y.-H.; Lee, J.; Chang, H.-H.; et al. Adverse impact of renin–angiotensin system blockade on the clinical course in hospitalized patients with severe COVID-19: A retrospective cohort study. Sci. Rep. 2020, 10, 20250. [Google Scholar] [CrossRef] [PubMed]
  42. Liu, X.; Liu, Y.; Chen, K.; Yan, S.; Bai, X.; Li, J.; Liu, D. Efficacy of ACEIs/ARBs vs CCBs on the progression of COVID-19 patients with hypertension in Wuhan: A hospital-based retrospective cohort study. J. Med. Virol. 2021, 93, 854–862. [Google Scholar] [CrossRef]
  43. Lopes, R.D.; Macedo, A.V.S.; de Barros E Silva, P.G.M.; Moll-Bernardes, R.J.; Dos Santos, T.M.; Mazza, L.; Feldman, A.; D’Andréa Saba Arruda, G.; de Albuquerque, D.C.; Camiletti, A.S.; et al. Effect of Discontinuing vs Continuing Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers on Days Alive and Out of the Hospital in Patients Admitted with COVID-19: A Randomized Clinical Trial. JAMA 2021, 325, 254–264. [Google Scholar] [CrossRef] [PubMed]
  44. López-Otero, D.; López-Pais, J.; Cacho-Antonio, C.E.; Antúnez-Muiños, P.J.; González-Ferrero, T.; Pérez-Poza, M.; Otero-García, Ó.; Díaz-Fernández, B.; Bastos-Fernández, M.; Bouzas-Cruz, N.; et al. Impact of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers on COVID-19 in a western population. CARDIOVID registry. Rev. Esp. Cardiol. 2021, 74, 175–182. [Google Scholar] [CrossRef] [PubMed]
  45. Meng, J.; Xiao, G.; Zhang, J.; He, X.; Ou, M.; Bi, J.; Yang, R.; Di, W.; Wang, Z.; Li, Z.; et al. Renin-angiotensin system inhibitors improve the clinical outcomes of COVID-19 patients with hypertension. Emerg. Microbes Infect. 2020, 9, 757–760. [Google Scholar] [CrossRef]
  46. Najmeddin, F.; Solhjoo, M.; Ashraf, H.; Salehi, M.; Rasooli, F.; Ghoghaei, M.; Soleimani, A.; Bahreini, M. Effects of Renin–Angiotensin–Aldosterone Inhibitors on Early Outcomes of Hypertensive COVID-19 Patients: A Randomized Triple-Blind Clinical Trial. Am. J. Hypertens. 2021, 34, 1217–1226. [Google Scholar] [CrossRef]
  47. Nouri-Vaskeh, M.; Kalami, N.; Zand, R.; Soroureddin, Z.; Varshochi, M.; Ansarin, K.; Rezaee, H.; Taghizadieh, A.; Sadeghi, A.; Maleki, M.A.; et al. Comparison of losartan and amlodipine effects on the outcomes of patient with COVID-19 and primary hypertension: A randomised clinical trial. Int. J. Clin. Pract. 2021, 75, e14124. [Google Scholar] [CrossRef]
  48. Oussalah, A.; Gleye, S.; Urmes, I.C.; Laugel, E.; Callet, J.; Barbé, F.; Orlowski, S.; Malaplate, C.; Aimone-Gastin, I.; Caillierez, B.M.; et al. Long-term ACE Inhibitor/ARB Use Is Associated with Severe Renal Dysfunction and Acute Kidney Injury in Patients with Severe COVID-19: Results From a Referral Center Cohort in the Northeast of France. Clin. Infect. Dis. 2020, 71, 2447–2456. [Google Scholar] [CrossRef]
  49. Soleimani, A.; Kazemian, S.; Saleh, S.K.; Aminorroaya, A.; Shajari, Z.; Hadadi, A.; Talebpour, M.; Sadeghian, H.; Payandemehr, P.; Sotoodehnia, M.; et al. Effects of Angiotensin Receptor Blockers (ARBs) on In-Hospital Outcomes of Patients with Hypertension and Confirmed or Clinically Suspected COVID-19. Am. J. Hypertens. 2020, 33, 1102–1111. [Google Scholar] [CrossRef]
  50. Tan, N.D.; Qiu, Y.; Xing, X.B.; Ghosh, S.; Chen, M.H.; Mao, R. Associations Between Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blocker Use, Gastrointestinal Symptoms, and Mortality Among Patients with COVID-19. Gastroenterology 2020, 159, 1170–1172. [Google Scholar] [CrossRef] [PubMed]
  51. Wang, H.-Y.; Peng, S.; Ye, Z.; Li, P.; Li, Q.; Shi, X.; Zeng, R.; Yao, Y.; He, F.; Li, J.; et al. Renin-angiotensin system inhibitor is associated with the reduced risk of all-cause mortality in COVID-19 among patients with/without hypertension. Front. Med. 2022, 16, 102–110. [Google Scholar] [CrossRef]
  52. Wang, Z.; Zhang, D.; Wang, S.; Jin, Y.; Huan, J.; Wu, Y.; Xia, C.; Li, Z.; Qi, X.; Zhang, D.; et al. A Retrospective Study from 2 Centers in China on the Effects of Continued Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers in Patients with Hypertension and COVID-19. Med. Sci. Monit. 2020, 26, e926651. [Google Scholar] [CrossRef]
  53. Xu, J.; Huang, C.; Fan, G.; Liu, Z.; Shang, L.; Zhou, F.; Wang, Y.; Yu, J.; Yang, L.; Xie, K.; et al. Use of angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers in context of COVID-19 outbreak: A retrospective analysis. Front. Med. 2020, 14, 601–612. [Google Scholar] [CrossRef] [PubMed]
  54. Yang, G.; Tan, Z.; Zhou, L.; Yang, M.; Peng, L.; Liu, J.; Cai, J.; Yang, R.; Han, J.; Huang, Y.; et al. Effects of Angiotensin II Receptor Blockers and ACE (Angiotensin-Converting Enzyme) Inhibitors on Virus Infection, Inflammatory Status, and Clinical Outcomes in Patients with COVID-19 and Hypertension: A Single-Center Retrospective Study. Hypertension 2020, 76, 51–58. [Google Scholar] [CrossRef] [PubMed]
  55. Lee, H.-Y.; Ahn, J.; Park, J.; Kang, C.K.; Won, S.-H.; Kim, D.W.; Park, J.-H.; Chung, K.-H.; Joh, J.-S.; Bang, J.H.; et al. Different therapeutic associations of renin-angiotensin system inhibitors with coronavirus disease 2019 compared with usual pneumonia. Korean J. Intern. Med. 2021, 36, 617–628. [Google Scholar] [CrossRef] [PubMed]
  56. Zhong, Y.; Zhao, L.; Wu, G.; Hu, C.; Wu, C.; Xu, M.; Dong, H.; Zhang, Q.; Wang, G.; Yu, B.; et al. Impact of renin–angiotensin system inhibitors use on mortality in severe COVID-19 patients with hypertension: A retrospective observational study. J. Int. Med. Res. 2020, 48, 300060520979151. [Google Scholar] [CrossRef] [PubMed]
  57. Zhou, X.; Zhu, J.; Xu, T. Clinical characteristics of coronavirus disease 2019 (COVID-19) patients with hypertension on renin–angiotensin system inhibitors. Clin. Exp. Hypertens. 2020, 42, 656–660. [Google Scholar] [CrossRef] [PubMed]
  58. Feng, B.; Zhang, D.; Wang, Q.; Yu, F.; Zou, Q.; Xie, G.; Wang, R.; Yang, X.; Chen, W.; Lou, B.; et al. Effects of angiotensin II receptor blocker usage on viral load, antibody dynamics, and transcriptional characteristics among COVID-19 patients with hypertension. J. Zhejiang Univ. Sci. B 2021, 22, 330–340. [Google Scholar] [CrossRef] [PubMed]
  59. Matsuzawa, Y.; Ogawa, H.; Kimura, K.; Konishi, M.; Kirigaya, J.; Fukui, K.; Tsukahara, K.; Shimizu, H.; Iwabuchi, K.; Yamada, Y.; et al. Renin-angiotensin system inhibitors and the severity of coronavirus disease 2019 in Kanagawa, Japan: A retrospective cohort study. Hypertens. Res. 2020, 43, 1257–1266. [Google Scholar] [CrossRef] [PubMed]
  60. Tian, C.; Li, N.; Bai, Y.; Xiao, H.; Li, S.; Ge, Q.-G.; Shen, N.; Ma, Q.-B. Angiotensin converting enzymes inhibitors or angiotensin receptor blockers should be continued in COVID-19 patients with hypertension. World J. Clin. Cases 2021, 9, 47–60. [Google Scholar] [CrossRef]
  61. Agirbasli, M. The effects of antihypertensive medications on severity and outcomes of COVID19. J. Hum. Hypertens. 2022, 36, 875–879. [Google Scholar] [CrossRef] [PubMed]
  62. Wösten-van Asperen, R.M.; Lutter, R.; Specht, P.A.; Moll, G.N.; van Woensel, J.B.; van der Loos, C.M.; van Goor, H.; Kamilic, J.; Florquin, S.; Bos, A.P. Acute respiratory distress syndrome leads to reduced ratio of ACE/ACE2 activities and is prevented by angiotensin-(1-7) or an angiotensin II receptor antagonist. J. Pathol. 2011, 225, 618–627. [Google Scholar] [CrossRef] [PubMed]
  63. Ye, R.; Liu, Z. ACE2 exhibits protective effects against LPS-induced acute lung injury in mice by inhibiting the LPS-TLR4 pathway. Exp. Mol. Pathol. 2020, 113, 104350. [Google Scholar] [CrossRef]
  64. Henry, C.; Zaizafoun, M.; Stock, E.; Ghamande, S.; Arroliga, A.C.; White, H.D. Impact of angiotensin-converting enzyme inhibitors and statins on viral pneumonia. Bayl. Univ. Med. Cent. Proc. 2018, 31, 419–423. [Google Scholar] [CrossRef] [PubMed]
  65. Imai, Y.; Kuba, K.; Rao, S.; Huan, Y.; Guo, F.; Guan, B.; Yang, P.; Sarao, R.; Wada, T.; Leong-Poi, H.; et al. Angiotensin-converting enzyme 2 protects from severe acute lung failure. Nature 2005, 436, 112–116. [Google Scholar] [CrossRef]
  66. Mansour, E.; Palma, A.C.; Ulaf, R.G.; Ribeiro, L.C.; Bernardes, A.F.; Nunes, T.A.; Agrela, M.V.; Bombassaro, B.; Monfort-Pires, M.; Camargo, R.L.; et al. Safety and Outcomes Associated with the Pharmacological Inhibition of the Kinin–Kallikrein System in Severe COVID-19. Viruses 2021, 13, 309. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Selection process of meta-analyses (PRISMA) flow diagram.
Figure 1. Selection process of meta-analyses (PRISMA) flow diagram.
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Figure 2. Bias risk assessed by the Cochrane assessment tool of included studies.
Figure 2. Bias risk assessed by the Cochrane assessment tool of included studies.
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Figure 3. Forest plot for mortality (OR = 1.10, 95% CI 0.83–1.46, p = 0.43). The heterogeneity between studies was very high (I2 = 84%) [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57].
Figure 3. Forest plot for mortality (OR = 1.10, 95% CI 0.83–1.46, p = 0.43). The heterogeneity between studies was very high (I2 = 84%) [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57].
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Figure 4. Forest plot for ICU admissions (OR = 0.99, 95% CI 0.68–1.45, p = 0.98). Heterogeneity was very high (I2 = 84%) [21,22,23,24,26,27,29,30,33,35,36,37,38,39,40,41,44,45,54,58].
Figure 4. Forest plot for ICU admissions (OR = 0.99, 95% CI 0.68–1.45, p = 0.98). Heterogeneity was very high (I2 = 84%) [21,22,23,24,26,27,29,30,33,35,36,37,38,39,40,41,44,45,54,58].
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Figure 5. Forest plot for length of hospital stays (SMD = 0.03, 95% CI −0.06–0.12, p = 0.54). Heterogeneity was low (I2 = 37%) [20,21,23,24,26,38,40,41,43,47,48,51,53,54,55,57,58,59,60].
Figure 5. Forest plot for length of hospital stays (SMD = 0.03, 95% CI −0.06–0.12, p = 0.54). Heterogeneity was low (I2 = 37%) [20,21,23,24,26,38,40,41,43,47,48,51,53,54,55,57,58,59,60].
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Figure 6. Univariate regression analysis for mortality.
Figure 6. Univariate regression analysis for mortality.
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Figure 7. Univariate regression analysis for ICU admissions.
Figure 7. Univariate regression analysis for ICU admissions.
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Figure 8. Univariate regression analysis for the length of hospital stays.
Figure 8. Univariate regression analysis for the length of hospital stays.
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Figure 9. Funnel plots of mortality.
Figure 9. Funnel plots of mortality.
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Figure 10. Funnel plots of ICU admissions.
Figure 10. Funnel plots of ICU admissions.
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Figure 11. Funnel plots of length of hospital stays.
Figure 11. Funnel plots of length of hospital stays.
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Figure 12. Sensitivity analysis for mortality [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57].
Figure 12. Sensitivity analysis for mortality [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57].
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Figure 13. Sensitivity analysis for ICU admissions [21,22,23,24,26,27,29,30,33,35,36,37,38,39,40,41,44,45,54,58].
Figure 13. Sensitivity analysis for ICU admissions [21,22,23,24,26,27,29,30,33,35,36,37,38,39,40,41,44,45,54,58].
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Figure 14. Sensitivity analysis for length of hospital stays [20,21,23,24,26,38,40,41,43,47,48,51,53,54,55,57,58,59,60].
Figure 14. Sensitivity analysis for length of hospital stays [20,21,23,24,26,38,40,41,43,47,48,51,53,54,55,57,58,59,60].
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Table 1. Basic characteristics of the included studies.
Table 1. Basic characteristics of the included studies.
First AuthorCountriesYpMean AgePfPhPdPcPrControl GroupExperimental Group
No.CmCl, Mean, dCiNo.EmEl, Mean, dEi
Abbas SoleimaniIran202066.390.371.000.000.430.0913235NANA12233NANA
Abderrahim OussalahFrance202065.340.470.440.260.500.101059NANA4410NANA
Axel BauerAustria and Germany202173.440.261.000.340.490.16100810.3520991212.3218
Baihuan FENGChina202164.030.331.000.290.180.1817NA49.29NA13NA18.73NA
Bastien GenetFrance202086.280.610.620.190.580.1513852NANA6314NANA
Carla FeliceItaly202073.020.411.000.260.600.115118NA258215NA21
Ci TianChina202171.460.511.000.400.250.0027NA13.071527NA14.29NA
Daniel M BeanUK202067.970.160.570.370.140.25801182NA106399106NA21
David J BaeUSA202045.980.590.050.260.190.3251237.08137818.717.2
Diego Lo’pez-OteroSpain202159.500.140.310.130.120.2375527NA2021011NA13
Emil L FosbølDenmark202040.520.100.190.090.200.143585297NANA895181NANA
Farhad NajmeddinIran202166.210.621.000.500.250.022943.64NA3154.714
Gokhan CetinkalTurkey202068.280.371.000.400.110.16148208.7027201298.7045
Guang YangChina202066.900.321.000.300.180.05831129.70NA43228.40NA
Hae-Young LeeRepublic of Korea202044.400.070.190.170.070.14728962NANA97750NANA
Hakeam A. HakeamSaudi Arabia202160.610.400.940.630.320.11937NA3324515NA69
Hee Kyoung ChoiRepublic of Korea202066.310.341.000.450.000.1969369NA5389242NA33
Huai-yu WangChina202265.000.050.210.110.050.022491168NANA28027NANA
Imran ChaudhriUSA202059.110.430.440.250.320.18220257.005980149.0022
Jeong-Hoon LimRepublic of Korea202067.140.380.400.250.100.061002224.9028301420.3010
Jiuyang XuChina202065.600.481.000.190.130.02612111.1217401112.298
Jordana B Cohen7 countries202162.000.361.000.520.180.1777106.061475116.7116
Ju Hwan KimRepublic of Korea202062.090.271.000.320.270.3360812NA1668210NA13
Juan MengChina202064.300.881.000.100.190.00251NANA170NANA
Juyi LiChina202066.600.241.000.350.390.072475619.00561152119.70NA
Katherine W. LamUSA202070.300.301.000.410.370.19279626.7055335587.7065
Marcello CovinoItaly202073.550.381.000.130.420.05559NA1311120NA38
Margaux LafaurieFrance202074.090.640.940.310.310.20366NA14739NA36
Mariano DuarteArgentina202165.300.350.490.210.000.16711615.00157139.006
Masoud Nouri-VaskehIran202163.790.561.000.240.190.153957.30NA4124.57NA
Massimo CugnoItaly202160.700.120.550.170.110.0030845NANA11937NANA
Mustafa KuzeytemizUSA202236.920.280.000.000.000.0011626NA313426NA3
Neil MehtaUSA202063.000.120.390.190.090.07152334NA152128NA22
Nian-Di TanChina202065.420.631.000.280.180.09691134.24NA31032.64NA
Renato D. LopesBrazil202155.390.231.000.320.050.0433497.80NA32596.70NA
Xian ZhouChina202064.740.471.000.310.360.1121511.70NA15210.10NA
Xiulan LiuChina202066.390.481.000.270.100.00831NANA745NANA
Yanjun ZhongChina202066.310.481.000.330.250.068915NANA376NANA
Yasushi MatsuzawaJapan2020600.401.000.1670.060.0018NA21.80NA21NA20.92NA
Zheyong HuangChina202057.900.461.000.080.040.02300NANA203NANA
Zhongchao WangChina202067.180.471.000.440.690.1562NA17.00662417.008
Abbreviations: Yp, year of publication; Pf, proportion of females; Ph, proportion of patients with hypertension; Pd, proportion of patients with diabetes; Pc, proportion of patients with cardiovascular diseases; Pr, proportion of patients with respiratory system diseases; No, number; Cm, control group mortality; Cl, length of hospital stays in control group; Ci, number of ICU admissions in control group; Em, experimental group mortality; El, length of hospital stays in experimental group; Ei, number of ICU admissions in experimental group. NA indicates that this value was not recorded and this article will not be used for a meta-analysis of this outcome indicator. The software used in this study uses the mean and standard deviation (rather than the median) for the statistical treatment of continuous variables, so we used the method recommended by Luo [18] and Wan’s [19] method to estimate the mean and standard deviation using the median [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60].
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Li, R.; Zhang, J.; Ren, L. A Meta-Analysis of the Impact of Using Angiotensin-Converting Enzyme Inhibitors (ACEIs) or Angiotensin II Receptor Blockers (ARBs) on Mortality, Severity, and Healthcare Resource Utilization in Patients with COVID-19. Adv. Respir. Med. 2025, 93, 4. https://doi.org/10.3390/arm93010004

AMA Style

Li R, Zhang J, Ren L. A Meta-Analysis of the Impact of Using Angiotensin-Converting Enzyme Inhibitors (ACEIs) or Angiotensin II Receptor Blockers (ARBs) on Mortality, Severity, and Healthcare Resource Utilization in Patients with COVID-19. Advances in Respiratory Medicine. 2025; 93(1):4. https://doi.org/10.3390/arm93010004

Chicago/Turabian Style

Li, Ruijuan, Jie Zhang, and Liang Ren. 2025. "A Meta-Analysis of the Impact of Using Angiotensin-Converting Enzyme Inhibitors (ACEIs) or Angiotensin II Receptor Blockers (ARBs) on Mortality, Severity, and Healthcare Resource Utilization in Patients with COVID-19" Advances in Respiratory Medicine 93, no. 1: 4. https://doi.org/10.3390/arm93010004

APA Style

Li, R., Zhang, J., & Ren, L. (2025). A Meta-Analysis of the Impact of Using Angiotensin-Converting Enzyme Inhibitors (ACEIs) or Angiotensin II Receptor Blockers (ARBs) on Mortality, Severity, and Healthcare Resource Utilization in Patients with COVID-19. Advances in Respiratory Medicine, 93(1), 4. https://doi.org/10.3390/arm93010004

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