Beneficial effects of Angiotensin II Receptor Blockers on Mortality in Patients with COVID-19: a Retrospective Study from 2019 to 2020 in China

doi:10.21203/rs.3.rs-2930798/v1

Download PDF

Research Article

Beneficial effects of Angiotensin II Receptor Blockers on Mortality in Patients with COVID-19: a Retrospective Study from 2019 to 2020 in China

https://doi.org/10.21203/rs.3.rs-2930798/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 11 Aug, 2023

Read the published version in Cardiovascular Drugs and Therapy →

You are reading this latest preprint version

Background

COVID-19 pandemic has become a serious global public health problem. Although the use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor type 1 blockers (ARBs) has been recommended in patients with COVID-19 and cardiovascular diseases (CVDs), according to the results of some small-sample retrospective analyses; however, there is still a lack of sufficient evidence to validate their efficacy. This multicenter retrospective study investigated whether ACEI/ARB administration was beneficial in patients with COVID-19 and CVDs.

Methods

A total of 11,231 patients with confirmed COVID-19 and CVDs, from 138 hospitals in Hubei Province, were included in this multicenter retrospective study. We compared the clinical characteristics and outcomes between the ARB and non-ARB groups and analyzed the risk factors for in-hospital death using univariate and multivariate Cox regression analyses and Kaplan–Meier curves.

Results

In the multivariate Cox regression model, after adjusting for age, gender, comorbidities, and in-hospital medications, ARB use was associated with lower all-cause mortality (adjusted HR, 0.53; 95% CI, 0.38–0.73; P < 0.001). After propensity score-matched analysis, the adjusted HR for the use of ARB associated with all-cause mortality was 0.62 (95% CI, 0.40–0.88; P = 0.02). Further subgroup analyses found that the adjusted HRs for the use of ARB associated with all-cause mortality were 0.52 (95% CI, 0.30–0.89; P = 0.016), 0.37 (95% CI, 0.21–0.64; P < 0.001), 0.42 (95% CI, 0.28–0.64; P < 0.001), and 0.55 (95% CI, 0.37–0.84; P = 0.005) in patients with heart failure, diabetes, and hypercholesterolemia, and severe COVID-19, respectively.

Conclusions

ARB administration was significantly associated with a lower risk of all-cause mortality in patients with COVID-19 and CVDs.

Trial registration

ClinicalTrials.gov NCT05615792.

COVID-19

SARS-CoV-2

In-hospital mortality

ARB

RAS inhibitor

Cardiovascular diseases

Since December, 2019, Corona Virus Disease 2019 (COVID-19) pandemic has existed for three years. Globally, as of February 28, 2023, 758,390,564 confirmed cases of COVID-19, including 6,859,093 deaths, were reported to the World Health Organization. This pandemic has undoubtedly brought new challenges to many fields, especially medicine. Many risk factors contributing to the death of patients with COVID-19 have been studied and identified, including older age, male sex, obesity, diabetes, and hypertension, all of which are associated with higher severity rates and mortality^{[1, 2]}. Among these, cardiovascular diseases (CVDs) are the most common diseases worldwide in terms of morbidity and mortality and should be considered. The COVID-19 pandemic has created more requirements and challenges for the prevention and management of CVDs^[3].

Angiotensin-converting enzyme inhibitors (ACEIs), and angiotensin II receptor blockers (ARBs) have been recommended as first-line agents for different types of CVDs, such as hypertension and heart failure, without obvious side effects^{[4, 5]}. Some small-scale studies found an association between the use of ACEIs/ARBs and a lower risk of mortality in patients with COVID-19 and hypertension^[6–8]. However, the argument about the role of RAS inhibitors still exists because of the possible upregulation of ACE2, an angiotensin-converting enzyme that has been identified to be dependent on SARS-CoV-2 cell entry^[9]. Thus, it is still thought that the use of RAS inhibitors in patients may lead to an increase in ACE2 levels, which may contribute to the development of infection, even though most studies found that treatment with ACEI/ARB is harmless in patients with COVID-19^[10]. Considering the widespread use of RAS inhibitors in patients with CVDs, the withdrawal or maintenance of RAS inhibitors has led to intense discussion^[11].

In light of this, our study aimed to determine the association between ARB use and the risk of all-cause mortality in patients with COVID-19 and CVDs and to provide more evidence for the use of ARBs to treat patients with COVID-19.

Study design and patient information

All patients with COVID-19, according to the China and WHO interim guidelines, from 138 hospitals in Hubei Province were screened; those who died or were discharged between December 29, 2019 (i.e., when the first patients were admitted) and August 31, 2021, were included in our study. Laboratory-confirmed SARS-CoV-2 infection was defined as at least two consecutive positive results in a real-time reverse transcriptase–polymerase chain reaction assay of nasal and pharyngeal swabs. Those aged < 18 years and lacking important basic information, laboratory test results, treatment information, and reverse transcriptase–polymerase chain reaction results for SARS-CoV-2 were excluded. All data were collected from the electronic medical record database of the Health Commission of Hubei Province.

A total of 53,030 inpatients aged > 18 years, who were diagnosed with COVID-19 were included in this study. Among them, 11,231 had CVDs, including hypertension, heart failure, and coronary heart disease, according to their documented medical history and discharge diagnosis.

This study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Research Ethics Committee of Tongji Medical College (no. TJ-IRB20210138). Owing to the retrospective nature and anonymity of this study, the ethics committee waived the need for written informed consent.

Data collection and endpoint definitions

Three researchers independently examined the epidemiological, demographic, clinical, and laboratory data extracted on admission from electronic medical records to obtain basic information and clinical characteristics of each patient. The collected information included age, sex, comorbidities, exposure history, oxygen support during hospitalization, vital signs [temperature, respiratory rate, pulse rate, and systolic and diastolic blood pressures], serum laboratory tests (including routine blood tests, blood chemical variables, procalcitonin, and coagulation function tests), therapeutic strategy during hospitalization, and outcomes. Patients treated with or without ARBs during hospitalization were classified into ARB or non-ARB group, respectively. Patients who were unable to take medication because of hypotension or mechanical ventilation without nasal feeding and those who discontinued treatment were included in our study. In this retrospective study, the primary endpoint of concern was all-cause mortality during hospitalization.

Statistical analysis and mapping

Statistical analyses and mapping were performed using SPSS (version 24.0; IBM, Armonk, NY, USA), R (version 4.1.1; R Foundation for Statistics Computing, Vienna, Austria) and GraphPad Prism (version 9.0; San Diego, CA, USA). The distribution and homoscedasticity of each dataset were tested using D'Agostino’s and Pearson’s omnibus normality tests. Continuous normally distributed data were reported as mean (deviation) and compared using the Student’s t-test. Continuous non-normally distributed data were reported as median (interquartile range, IQR) and compared using the Wilcoxon rank-sum test. Categorical data were presented as number (percentage) and were compared using the chi-square, Fisher’s exact, and Cochran–Mantel–Haenszel tests, as appropriate. The optimal caliper width was set at 0.05.

Propensity score-matched analysis was used to reduce the possible influence of different diseases or treatments and increase the credibility of the results. We included possible confounding factors such as sex, age, temperature, and other associated diseases and drugs in our analysis, and completed the analysis using the R MathIt Package. The caliper width was set at 0.05. The standardized mean difference (SMD) was used to evaluate the balance of covariates after matching and the value < 0.1 was considered as a successful matching. The ratio of the ARB group to non-ARB group was 1:1.

Univariate and multivariate Cox regression models were used to determine the risk factors for death during hospitalization and are displayed as hazard ratios (HRs) and 95% confidence intervals (CIs). Survival analysis methods, including Kaplan–Meier (K-M) curves and Cox proportional hazard model analysis, were used to compare the time of endpoint events in different subgroups.

Participants

The study included 68,128 patients with COVID-19 from 138 hospitals in Hubei Province before August 31, 2021. Of these, 15,098 (22.2%) were excluded because of missing data. Ultimately, 53,030 inpatients were included in the analysis. Among them, 11,231 (21.2%) were diagnosed with CVDs, including hypertension, heart failure, and coronary heart disease, and 823 (7.3%) of these patients with CVDs were treated with ARBs (Fig. 1). The median age of the 823 patients was 69 years (interquartile range [IQR], 59–80 years), and 439 (53.3%) were males (Table 1). The other characteristics of the ARB and non-ARB groups are shown in Table 1. Compared with the non-ARB group, there was a higher proportion of patients with diabetes, chronic obstructive pulmonary disease (COPD), chronic kidney disease, and lower levels of indices related to liver and coagulation functions.

Table 1

Characteristics of Patients with Cardiovascular Diseases in ARB and Non-ARB Groups Before and After Propensity Score Matching
	Unmatched (n = 11231)				PSM 1:1 (n = 1430)
	AT receptor blockers (n = 823)	Non-AT receptor blockers (n = 10408)	SMD	P	AT receptor blockers (n = 715)	Non-AT receptor blockers (n = 715)	SMD	P
Gender: Male (%)	439 (53.3)	5516 (53.0)	0.007	0.878	383 (53.6)	387 (54.1)	0.011	0.874
Age, years	69 [59, 80]	68 [58, 78]	0.138	0.002	69 [59, 80]	71 [62, 80]	0.059	0.1
Age ≥ 65years (%)	526 (63.9)	6332 (60.8)	0.064	0.088	454 (63.5)	472 (66.0)	0.053	0.347
Age < 65years (%)	297 (36.1)	4076 (39.2)	0.064	0.088	261 (36.5)	243 (34.0)	0.053	0.347
Vital signs
Systolic blood pressure (mmhg)	130 [120, 142]	127 [114, 140]	0.306	< 0.001	130 [120, 142]	130 [120, 142]	0.047	0.598
Diastolic blood pressure (mmhg)	78 [70, 86]	80 [71, 90]	0.273	< 0.001	78 [70, 86]	78 [70, 85]	0.03	0.806
Respiratory rate (times per minute)	20 [19, 20]	20 [19, 20]	0.113	0.099	20 [19, 20]	20 [19, 20]	0.087	0.022
Pulse rate (times per minute)	78 [70, 85]	80 [74, 88]	0.207	< 0.001	78 [71, 86]	78 [72, 85]	0.041	0.443
Temperature (°C)	36.50 [36.30, 36.60]	36.50 [36.30, 36.80]	0.187	< 0.001	36.50 [36.30, 36.60]	36.50 [36.30, 36.70]	0.002	0.738
Original comorbidities
Hypertension (%)	801 (97.3)	8935 (85.8)	0.423	< 0.001	695 (97.2)	628 (87.8)	0.362	< 0.001
Diabetes (%)	287 (34.9)	3118 (30.0)	0.105	0.004	238 (33.3)	239 (33.4)	0.003	1
Hyperlipidemia (%)	436 (53.0)	5683 (54.6)	0.033	0.387	372 (52.0)	413 (57.8)	0.115	0.034
Chronic gastritis (%)	36 (4.4)	926 (8.9)	0.182	< 0.001	30 (4.2)	31 (4.3)	0.007	1
Coronary heart disease (%)	284 (34.5)	2929 (28.1)	0.138	< 0.001	231 (32.3)	235 (32.9)	0.012	0.866
COPD (%)	69 (8.4)	729 (7.0)	0.052	0.158	58 (8.1)	47 (6.6)	0.059	0.311
Heart failure (%)	221 (26.9)	1197 (11.5)	0.398	< 0.001	173 (24.2)	116 (16.2)	0.2	< 0.001
Arrhythmia (%)	110 (13.4)	1243 (11.9)	0.043	0.249	87 (12.2)	110 (15.4)	0.093	0.091
CKD (%)	88 (10.7)	774 (7.4)	0.114	0.001	74 (10.3)	64 (9.0)	0.047	0.42
Solid tumor (%)	20 (2.4)	428 (4.1)	0.095	0.023	19 (2.7)	26 (2.6)	0.056	0.363
Schizophrenia (%)	11 (1.3)	190 (1.8)	0.039	0.378	8 (1.1)	9 (1.3)	0.013	1
Depression (%)	16 (1.9)	95 (0.9)	0.087	0.007	12 (1.7)	10 (1.4)	0.023	0.83
Laboratory results
Routine blood test
Red blood cell count, ×10¹²/L	4.12 [3.76, 4.50]	4.10 [3.67, 4.51]	0.072	0.125	4.12 [3.76, 4.50]	4.10 [3.67, 4.51]	0.076	0.207
White cell count, ×10⁹/L	6.01 [4.88, 7.42]	6.06 [4.77, 7.91]	0.106	0.255	6.00 [4.84, 7.35]	5.88 [4.68, 7.78]	0.06	0.887
Hemoglobin, g/L	126 [114, 138]	125 [112, 137]	0.119	0.01	126 [114, 138]	125 [112, 138]	0.067	0.221
Neutrophil count, ×10⁹/L	3.89 [3.01, 5.19]	3.92 [2.87, 5.62]	0.131	0.454	3.89 [2.98, 5.18]	3.69 [2.82, 5.55]	0.079	0.504
Monocyte count, ×10⁹/L	0.39 [0.30, 0.52]	0.41 [0.30, 0.55]	0.058	0.132	0.40 [0.30, 0.53]	0.40 [0.30, 0.53]	0.017	0.925
Platelet count, ×109/L	195 [158, 233]	195 [154, 237]	0.002	0.936	194 [158, 233]	194 [156, 237]	0.035	0.715
Blood biochemistry
Alanine aminotransferase, U/L	20 [13, 33]	20.80 [14, 33]	0.055	0.113	20 [13, 33]	20 [14, 33]	0.048	0.806
Aspartate aminotransferase, U/L	21 [17, 29]	23 [17, 32]	0.065	< 0.001	21 [17, 29]	22 [17, 32]	0.091	0.026
Lactate dehydrogenase, U/L	180 [151, 229]	191 [156, 252]	0.155	< 0.001	180 [151, 229]	190 [157, 240]	0.081	0.015
Total bilirubin, umol/L	11.20 [8.10, 15.20]	10.80 [8.00, 14.70]	0.025	0.144	11.20 [8.10, 15.20]	11.00 [8.30, 15.10]	0.016	1
Total protein, g/L	68.20 [63.50, 72.50]	66.70 [61.70, 71.70]	0.189	< 0.001	68.00 [63.40, 72.30]	67.70 [62.50, 72.30]	0.043	0.391
Globulin, g/L	28.10 [24.90, 31.60]	27.90 [24.40, 31.80]	0.021	0.338	28.00 [24.90, 31.60]	27.70 [24.20, 31.60]	0.048	0.251
Albumin, g/L	40.00 [36.20, 43.40]	38.50 [34.10, 42.60]	0.225	< 0.001	39.90 [36.10, 43.30]	39.60 [35.50, 43.20]	0.013	0.652
Alkaline phosphatase, U/L	66.00 [53.00, 82.50]	71.00 [56.90, 90.30]	0.241	< 0.001	66.00 [53.00, 82.80]	68.00 [54.00, 86.00]	0.151	0.057
Total cholesterol, mmol/L	4.11 [3.46, 4.88]	4.10 [3.41, 4.88]	0.036	0.431	4.11 [3.47, 4.89]	4.10 [3.49, 4.81]	0.02	0.93
Triglyceride, mmol/L	1.27 [0.95, 1.81]	1.29 [0.95, 1.81]	0.06	0.866	1.27 [0.95, 1.78]	1.28 [0.94, 1.84]	0.041	0.744
LDL, mmol/L	2.34 [1.83, 3.04]	2.39 [1.86, 3.02]	0.009	0.763	2.35 [1.84, 3.04]	2.32 [1.78, 2.93]	0.091	0.161
HDL, mmol/L	1.06 [0.87, 1.24]	1.07 [0.87, 1.30]	0.067	0.216	1.07 [0.88, 1.24]	1.09 [0.89, 1.32]	0.143	0.041
Creatinine, µmol/L	73.00 [60.00, 92.20]	70.40 [57.90, 87.40]	0.068	0.003	73.00 [60.00, 92.00]	72.80 [59.00, 92.60]	0.01	0.892
Blood urea nitrogen, mmol/L	4.88 [3.81, 6.18]	4.80 [3.76, 6.50]	0.105	0.463	73.00 [60.00, 92.00]	72.80 [59.00, 92.60]	0.01	0.892
eGFR, mL/min	95.80 [80.60, 109.21]	97.36 [81.30, 111.82]	0.050	0.034	95.80 [80.60, 109.22]	95.20 [79.60, 108.70]	0.031	0.657
Sodium, mmol/L	140.00 [137.25, 142.00]	139.80 [137.00, 141.90]	0.003	0.156	140.00 [137.30, 142.00]	140.00 [137.20, 142.00]	0.047	0.751
Potassium, mmol/L	3.90 [3.60, 4.22]	3.96 [3.63, 4.30]	0.142	0.002	3.89 [3.59, 4.22]	3.90 [3.59, 4.20]	0.044	0.932
Calcium, mmol/L	2.30 [2.16, 2.41]	2.24 [2.11, 2.37]	0.084	< 0.001	2.30 [2.15, 2.41]	2.27 [2.12, 2.39]	0.033	0.094
Lactic acid, mmol/L	1.60 [1.20, 2.30]	1.70 [1.30, 2.34]	0.091	0.011	1.60 [1.20, 2.30]	1.70 [1.30, 2.30]	0.083	0.08
Coagulation function
APTT	29.30 [26.15, 33.75]	30.90 [27.20, 35.10]	0.239	< 0.001	29.40 [26.20, 33.80]	30.00 [26.70, 34.20]	0.122	0.024
Prothrombin time, s	11.00 [10.10, 12.70]	11.90 [10.80, 13.40]	0.398	< 0.001	11.10 [10.20, 12.80]	11.30 [10.40, 13.00]	0.123	0.006
Prothrombin activity, %	96.00 [85.60, 106.00]	95.00 [83.00, 106.00]	0.011	0.125	96.00 [85.00, 105.00]	96.00 [83.00, 106.00]	0.006	0.731
Thrombin time, s	17.30 [15.70, 18.45]	16.70 [15.40, 18.10]	0.011	< 0.001	17.30 [15.70, 18.40]	17.00 [15.60, 18.20]	0.002	0.226
International normalized ratio	0.92 [0.86, 1.03]	0.98 [0.90, 1.10]	0.327	< 0.001	0.93 [0.86, 1.05]	0.95 [0.88, 1.06]	0.11	0.005
Fibrinogen, mg/L	3.10 [2.44, 4.07]	3.24 [2.54, 4.21]	0.098	0.004	3.11 [2.44, 4.08]	3.22 [2.52, 4.21]	0.078	0.148
D-dimer, mg/L	0.42 [0.24, 0.73]	0.54 [0.29, 0.98]	0.229	< 0.001	0.42 [0.25, 0.73]	0.50 [0.28, 0.95]	0.165	0.001
Cardiac function related indicators
NT-proBNP	153.10 [45.50, 510.95]	132.00 [42.00, 480.00]	0.008	0.107	153.00 [45.00, 504.00]	145.40 [47.65, 470.00]	0.046	0.846
hscTnI	0.03 [0.00, 1.80]	0.04 [0.01, 3.50]	0.039	< 0.001	0.03 [0.00, 2.00]	0.03 [0.01, 3.00]	0.01	0.025
CK-MB	7.00 [0.98, 12.40]	7.00 [0.90, 12.10]	0.006	0.337	7.00 [0.90, 12.10]	7.00 [0.90, 13.00]	0.039	0.872
Diabetes related index
Glucose, mmol/L	5.89 [5.16, 7.83]	5.87 [5.09, 7.53]	0.073	0.122	5.87 [5.13, 7.81]	5.97 [5.12, 7.50]	0.074	0.769
HbA1c, %	6.30 [5.80, 7.20]	6.20 [5.70, 7.20]	0.012	0.310	6.30 [5.80, 7.20]	6.10 [5.70, 7.10]	0.098	0.046
Treatment
Corticosteroids	110 (13.4)	577 (5.5)	0.27	< 0.001	93 (13.0)	105 (14.7)	0.049	0.400
Intravenous immunoglobin	117 (14.2)	837 (8.0)	0.197	< 0.001	102 (14.3)	123 (17.2)	0.081	0.146
ECMO	15 (1.8)	54 (0.5)	0.121	< 0.001	12 (1.7)	9 (1.3)	0.035	0.66
Renal replacement therapy	5 (0.6)	143 (1.4)	0.077	0.09	5 (0.7)	7 (1.0)	0.031	0.772
Outcomes
Shock (%)	17 (2.1)	312 (3.0)	0.059	0.156	11 (5.4)	29 (14.3)	0.301	0.005
ARDS (%)	14 (1.7)	300 (2.9)	0.079	0.062	13 (1.8)	9 (1.3)	0.045	0.519
Acute heart injury	32 (3.9)	549 (5.3)	0.066	0.100	7 (3.4)	8 (3.9)	0.026	1.000
Acute kidney injury	52 (6.3)	796 (7.6)	0.052	0.186	17 (8.4)	40 (19.7)	0.331	0.002
Data were presented as median and interquartile range (Q1-Q3).
Abbreviation:
ARB, angiotensin II receptor blocker; AT, angiotensin II; SMD, standardized mean difference; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; LDL, low-density lipoprotein; HDL, high-density lipoprotein; eGFR, estimated glomerular filtration rate; APTT, activated partial thromboplastin time; NT-proBNP, N-terminal pro-B-type natriuretic peptide; hscTnI, hypersensitive cardiac troponin I; CK-MB, creatine kinase isoenzymes; HbA1c, hemoglobin A1c; ECMO, extracorporeal membrane oxygenation; ARDS, acute respiratory distress syndrome.

Primary outcomes

In terms of mortality, patients in the ARB group had a significantly lower mortality rate (43/823) than those in non-ARB group (897/10,408) (5.2 vs. 8.6, P < 0.001). The ARB group had a higher severity ratio than the non-ARB group (25.0% vs. 31.9; P < 0.001). In the univariate Cox regression model, the HRs of the known variables for the all-cause mortality were 2.1 (95% CI, 1.8–2.4; P < 0.001), 1.75 (95% CI, 1.41–1.85; P < 0.001), 0.57 (95% CI, 0.42–0.77, P < 0.001), 0.82 (95% CI, 0.66-1.00, P = 0.055), 0.57(95% CI, 0.35–0.94, P = 0.026), and 1.80 (95% CI, 1.60–2.01, P < 0.001), for age, gender, ARBs, statins, metformin, and hypercholesterolemia, respectively. These variables were then included in the multivariate Cox regression model. After adjusting for age, gender, comorbidities, and in-hospital medications (such as metformin and statins), the use of ARBs was associated with lower all-cause mortality (adjusted HR, 0.54; 95% CI, 0.39–0.74; P < 0.001). Besides, the adjusted HRs for association of other variables for all-cause mortality were 1.91 (95% CI, 1.63–2.23; P < 0.001), 1.53 (95% CI, 1.35–1.75; P < 0.001), 0.59 (95% CI, 0.47–0.73, P < 0.001), 0.51(95% CI, 0.31–0.85, P = 0.009), and 1.63 (95% CI, 1.43–1.86, P < 0.001) for age, gender, statins, metformin, and hypercholesterolemia, respectively (Fig. 2). We then completed the K–M curves and found that in-hospital use of ARBs was significantly associated with a lower risk of all-cause mortality in patients with COVID-19 and CVDs (P < 0.001), which is consistent with our results from the multivariate Cox regression model (Fig. 3A).

Considering the existing imbalanced confounding factors, propensity score-matched analyses were conducted to ensure the accuracy of our results. We successfully matched 715 patients in the ARB and non-ARB groups at a ratio of 1:1. A total of 1,430 patients were included in the propensity score-matched sample. The evaluation of the balance of variables was completed using the SMD and P-value, and most of them reached balance (Table 1). However, the proportion of patients with hypertension and heart failure remains imbalanced. ARBs were strongly recommended for the treatment of these two diseases, and this imbalance was accepted. We also used a multivariate Cox regression model and K–M curves, which showed results similar to those described above (Fig. 2 and Table 1). The adjusted HR for the use of ARBs associated with all-cause mortality was 0.62 (95% CI, 0.40–0.88; P = 0.02), which indicated that the use of ARBs was related to the lower risk of all-cause mortality, consistent with the result of K–M curve analysis (P = 0.001).

The above results indicate that there was a strong association between the use of ARBs and a lower risk of all-cause mortality by the multivariate Cox regression model, K–M curves, and propensity score-matched analysis.

Secondary outcomes

We chose invasive mechanical ventilation, shock, acute respiratory distress syndrome (ARDS), acute heart injury, and acute kidney injury as secondary outcomes. The incidence of ARDS was lower in the ARB group than that in the non-ARB group (1.7% vs. 2.9%, P = 0.062) (Table 1). In the multivariate Cox regression model, the adjusted HR for the use of ARBs associated with acute kidney injury was 0.60 (95% CI, 0.45–0.79; P < 0.001) (Table 2).

Table 2

Hazard Ratios for Secondary Outcomes between Patients in the ARB and Non-ARB Groups
	All patients before PSM		Patients after PSM (1:1)
Secondary outcomes	Adjusted HR (95% CI)	P-value	Adjusted HR (95% CI)	P-value
Invasive mechanical ventilation	0.71 (0.51–0.99)	0.042	0.46 (0.22–0.96)	0.038
ARDS	0.48 (0.28–0.82)	0.008	0.43 (0.12–1.48)	0.080
Shock	0.46 (0.28–0.76)	0.002	0.53 (0.26–1.12)	0.090
Acute heart injury	0.65 (0.45–0.94)	0.020	0.93 (0.31–2.78)	0.893
Acute kidney injury	0.60 (0.45–0.79)	< 0.001	0.57 (0.31–1.73)	0.069
Abbreviation: ARDS, acute respiratory distress syndrome.

After propensity score-matched analysis, the incidences of shock (5.4% vs. 14.3%, P = 0.005) and acute kidney injury (8.4% vs. 19.7%, P = 0.002) in the ARB group were lower than those in the non-ARB group (Table 1). In the multivariate Cox regression model, the adjusted HRs for the use of ARBs associated with shock and acute kidney injury were 0.53 (95% CI, 0.26–1.12; P = 0.090) and 0.57 (95% CI, 0.31–1.73; P = 0.069), respectively (Table 2).

Subgroup analysis

Considering the multiplicity of the original comorbidities, we compared all-cause mortality in patients with heart failure, diabetes, hypercholesterolemia, and severe COVID-19. We constructed K–M curves and adjusted for the same covariates as described above in the multivariate Cox regression model. The adjusted HRs for the use of ARBs associated with all-cause mortality were 0.52 (95% CI, 0.30–0.89; P = 0.016), 0.37 (95% CI, 0.21–0.64; P < 0.001), 0.42 (95% CI, 0.28–0.64; P < 0.001), and 0.55 (95% CI, 0.37–0.84; P = 0.005) in patients with heart failure, diabetes, hypercholesterolemia, and severe COVID-19, respectively, which were similar with the result shown by the K–M curves (Fig. 4A-4D). The data suggest that there was a significantly lower risk of all-cause mortality in the ARB group than in the non-ARB group among patients with heart failure, diabetes, hypercholesterolemia, or severe COVID-19. We also conducted the study in non-heart failure, non-diabetes, non-hypercholesterolemia, and non-severe groups. The adjusted HRs for the use of ARBs associated with all-cause mortality were 0.51 (95% CI, 0.34–0.76; P < 0.001), 0.55 (95% CI, 0.37–0.80; P = 0.002), 0.54 (95% CI, 0.33–0.88; P = 0.013), and 0.40 (95% CI, 0.24–0.67; P = 0.013) in the non-heart failure, non-diabetes, non-hypercholesterolemia, and non-severe groups, respectively (Supplementary Fig. 1A-1D and Supplementary Table 1–8). Results showed a clear association between ARBs use and lower all-cause mortality in these subclasses.

Comparison between patients using ACEIs and ARBs

Considering that ACEIs are common RAS inhibitors in clinical practice, we firstly compared the use of ACEIs or ARBs in patients with CVDs. We conducted K-M curve and multivariate Cox regression analyses and found that the use of ACEI/ARB had a significant association with the decreased risk of all-cause mortality (adjusted HR 0.56; 95%CI, 0.44–0.73; P < 0.001) (Supplementary Figure S2A). Then we separated ACEIs and ARBs and then found that there was no significant association between the use of ACEI and the risk of the all-cause mortality (adjusted HR 0.89; 95% CI, 0.59–1.34; P = 0.900) (Supplementary Figure S2B). We then compared patients who used ACEIs only with those who used ARBs only. The adjusted HR between the ARB and ACEI groups for the association of the all-cause mortality was 0.60 (95% CI, 0.34–1.05; P = 0.073) (Supplementary Figure S2C). And the adjusted HR between the ACEI groups and no treatment was 0.83 (95%CI, 0.53-1.0, P = 0.424) while the adjusted HR between the ARB groups and no treatment was 0.52 (95%CI, 0.37–0.73, P < 0.001). In the secondary outcomes, the use of ARB performed lower risk in invasive mechanical ventilation (Adjusted HR, 0.10, 95%CI, 0.02–0.47, P = 0.004) (Supplementary Table 9). These results suggest that the risk of all-cause mortality was lower in the ARB group than that in the ACEI group, and that the use of ARBs might be superior to the use of ACEIs.

In this retrospective study, we observed that ARBs use was significantly associated with a lower risk of all-cause mortality in 11,231 inpatients with COVID-19 and CVDs in Hubei Province between December 29, 2019 and August 31, 2021. To avoid bias from the ignored confounding factors, we also conducted the study in terms of severity and other comorbidities and drew the same conclusion, which might ensure the association between the use of ARBs and the lower risk of all-cause mortality. We then compared the use of ARBs with that of ACEIs and found that ARB use might be associated with a lower risk of all-cause mortality. Considering this phenomenon, we hypothesized that ACEIs might have no effect on non-ACE-mediated Ang II production, such as chymase-mediated Ang II production, which may be the main production mechanism in severe infection and inflammation^{[12, 13]}. In addition, ACEIs can inhibit ACE-mediated bradykinin degradation in the lungs, which may lead to bradykinin accumulation, aggravate lung inflammation, and cause severe ARDS^[14]. However, ARBs directly inhibit the Ang II receptor and have no effect on bradykinin metabolism.

According to international guidelines and expert consensus, RAS inhibitors are recommended for the treatment of patients with COVID-19 and CVDs to prevent potential comorbidities and organ damage^{[15, 16]}. More global randomized controlled trials and prospective cohort studies should be conducted to provide stronger evidence supporting the continued use of ARBs in patients with COVID-19.

Current clinical studies and meta-analyses have pointed out that CVDs such as hypertension and heart failure are associated with the severity and death of inpatients with COVID-19 and result in poor prognosis in discharged patients during long-time follow-up^{[1, 2, 17–20]}. Many studies on the drugs commonly used in the therapies have been conducted^[21–23]. The use of RAS inhibitors is a contentious issue in the management of inpatients with COVID-19 and CVDs. From the viewpoint of pathophysiology, it has been proven that ACE2 expression is upregulated in the blood vessels following treatment with ACEIs/ARBs^[24]. ACE2 is the cellular receptor of the SARS-Cov-2, which helps in the virus cell entry^{[9, 25]}. Therefore, RASS inhibitors may increase the likelihood of COVID-19 and promote disease development. In contrast, SARS-Cov-2 combine with a large amount of ACE2 and leads to a decrease in ACE2, which negatively regulates the RASS and protects the lung from acute injury, and the RASS was activated excessively to produce a large amount of angiotensin II, which may induce reactive oxygen species, DNA damage, and T-cell apoptosis^{[26, 27]}.

To explain this contradiction in physiological mechanisms, numerous clinical observational and cohort studies have been conducted. Most studies have identified that the use of RAS inhibitors did not increase the risk of death in patients with COVID-19. A study from nine hospitals in Hubei Province in 2020 showed that among hospitalized patients with COVID-19 and coexisting hypertension, inpatient use of ACEIs/ARBs was associated with a lower risk of all-cause mortality than ACEIs/ARBs nonusers^[6]. In another case-control study of 1,139 patients with COVID-19 and 11,390 non-infected control in Spain, there was no association between the use of RAS inhibitors and a higher risk of SARS-Cov-2 infection^[28]. A study of 3,617,556 patients exposed to at least one type of antihypertensive drug from the French National Health Data System (SNDS) reported that ACEIs, ARBs, and calcium channel blockers (CCBs) were negatively associated with the risk of COVID-19, and stronger associations were observed for ACEIs than for ARBs and CCBs^[8], which was contrary to our study results. However, conclusions about the priority of ACEIs and ARBs cannot be confirmed because of the lack of evidence and different perspectives^[29]. In 2021, a randomized clinical trial was conducted on 659 patients hospitalized in Brazil with mild-to-moderate COVID-19^[30]. The trial found no difference between the mean number of days alive and out of the hospital for those assigned to discontinue or continue these medications. In a meta-analysis in 2023 Mar, 10 studies were included and the use of ACEI/ARB was not associated with increased risk of mortality, ICU admission, or mechanical ventilation compared to control^[31]. Except for these studies, which supported that the use of RAS inhibitors had a harmless effect or even a protective effect in patients with COVID-19, a few studies have also shown different results. In a study on 499 patients in France, long-term use of RAS inhibitors was independently associated with a higher risk of severe COVID-19 and a poor outcome^[32]. The European Society of Cardiology (ESC) guidance for the diagnosis and management of CVDs during the COVID-19 pandemic, published in 2022, suggests that despite much early speculation of a link between the use of ACEIs or ARBs and increased risk from COVID-19, a series of clinical studies across the world have shown an increased risk of COVID-19 or the risk of developing severe complications or death from COVID-19, when compared to the risk in patients taking other antihypertensive drugs^[16].

Overall, these studies suggest that the use of RAS inhibitors may protect patients from injury caused by excessive activation of the RAS, and have a benefit on the risk of mortality in patients with COVID-19. Our study collected clinical data in the early stages of the pandemic in Hubei Province, conducted an epidemiological analysis, and determined that the use of ARBs in patients with COVID-19 and CVDs contributed to a good prognosis.

Study limitations

This study had several limitations. First, this was a retrospective study and all data were obtained from a clinical database. The lack of clinical data is unavoidable; not all laboratory data were collected completely, and some imaging results were not available. Second, because of the large number of hospitals and the difference between professional levels, there were some differences in data quality among different hospitals, and the treatments were not the same. In addition, we were unable to assess the severity of complications, psychological factors, or compliance with medical prescriptions in patients with COVID-19. Finally, all patients with COVID-19 were admitted to hospitals in Hubei Province between December 29, 2019 and August 31, 2021. Geographical diversity and various ethnic groups were not included in our study, and the results only reflected in-hospital patients with COVID-19 in the early stages of the pandemic.

ARB treatment was significantly associated with a lower risk of all-cause mortality in patients with COVID-19 and CVDs. This strong association generally considers the different types of comorbidities. AT receptor blockers are recommended for the treatment of patients with COVID-19 and CVDs.

Acknowledgements

The authors would like to thank all the patients, their families, and all investigators involved in this study.

Funding

This study was supported in part by the National Natural Science Foundation of China (Nos. 82241034 and C-0052) and Top-Notch Talent Program of Hubei Province and Tongji Hospital (No. 2021YBJRC005).

Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Availability of data and material

The data underlying this article will be shared on reasonable request to the corresponding author.

Code availability

The code underlying this article will be shared on reasonable request to the corresponding author.

Authors’ contribution

KX and WH designed the study, collected and analyzed data, performed the statistical analysis, and wrote the manuscript. BY, KZ and DZ completed the data collation and was responsible for quality control. DWW designed the project, edited the manuscript, and supervised the study. All authors gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.

Ethics approval

This study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Research Ethics Committee of Tongji Medical College (no. TJ-IRB20210138).

Consent to participate

Not applicable

Consent for publication

The authors declare that this manuscript is being submitted for consideration for publication

Zheng Z, et al. Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J Infect. 2020;81(2):e16–e25.
Li J, et al. Epidemiology of COVID-19: A systematic review and meta-analysis of clinical characteristics, risk factors, and outcomes. J Med Virol. 2021;93(3):1449–58.
Dale CE, et al. The impact of the COVID-19 pandemic on cardiovascular disease prevention and management. Nat Med. 2023;29(1):219–25.
Whelton PK, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension. 2018;71(6):e13–15.
Heidenreich PA, et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022;145(18):e895–e1032.
Zhang P, et al. Association of Inpatient Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers With Mortality Among Patients With Hypertension Hospitalized With COVID-19. Circ Res. 2020;126(12):1671–81.
Cannata F, et al. Continuation versus discontinuation of ACE inhibitors or angiotensin II receptor blockers in COVID-19: effects on blood pressure control and mortality. Eur Heart J Cardiovasc Pharmacother. 2020;6(6):412–4.
Semenzato L, et al. Antihypertensive Drugs and COVID-19 Risk: A Cohort Study of 2 Million Hypertensive Patients. Hypertension. 2021;77(3):833–42.
Hoffmann M et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell, 2020, 181(2): 271 – 80.e8.
Gallo G, et al. Hypertension and COVID-19: Current Evidence and Perspectives. High Blood Press Cardiovasc Prev. 2022;29(2):115–23.
Danser AHJ, et al. Renin-Angiotensin System Blockers and the COVID-19 Pandemic: At Present There Is No Evidence to Abandon Renin-Angiotensin System Blockers. Hypertension. 2020;75(6):1382–5.
Borland JA, et al. Expression, localisation and function of ACE and chymase in normal and atherosclerotic human coronary arteries. Vascul Pharmacol. 2005;42(3):99–108.
Kaparianos A, Argyropoulou E. Local renin-angiotensin II systems, angiotensin-converting enzyme and its homologue ACE2: their potential role in the pathogenesis of chronic obstructive pulmonary diseases, pulmonary hypertension and acute respiratory distress syndrome. Curr Med Chem. 2011;18(23):3506–15.
Tan WSD, et al. Targeting the renin-angiotensin system as novel therapeutic strategy for pulmonary diseases. Curr Opin Pharmacol. 2018;40:9–17.
Volpe M, et al. Recommendations for Cardiovascular Prevention During the Sars-Cov-2 Pandemic: An Executive Document by the Board of the Italian Society of Cardiovascular Prevention. High Blood Press Cardiovasc Prev. 2020;27(5):373–7.
ESC guidance for the. diagnosis and management of cardiovascular disease during the COVID-19 pandemic: part 2-care pathways, treatment, and follow-up. Eur Heart J. 2022;43(11):1059–103.
Grasselli G, et al. Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Intern Med. 2020;180(10):1345–55.
Zhang Y, et al. Risk Factors for Death Among the First 80 543 Coronavirus Disease 2019 (COVID-19) Cases in China: Relationships Between Age, Underlying Disease, Case Severity, and Region. Clin Infect Dis. 2022;74(4):630–8.
Huang L, et al. Health outcomes in people 2 years after surviving hospitalisation with COVID-19: a longitudinal cohort study. The Lancet Respiratory Medicine. 2022;10(9):863–76.
Zhang H, et al. Lung-function trajectories in COVID-19 survivors after discharge: A two-year longitudinal cohort study. EClinicalMedicine. 2022;54:101668.
Son M, et al. Association Between Renin-Angiotensin-Aldosterone System Inhibitors and COVID-19 Infection in South Korea. Hypertension. 2020;76(3):742–9.
Bavishi C, et al. Renin-angiotensin-system inhibitors and all-cause mortality in patients with COVID-19: a systematic review and meta-analysis of observational studies. J Hypertens. 2021;39(4):784–94.
Mehta N, et al. Association of Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers With Testing Positive for Coronavirus Disease 2019 (COVID-19). JAMA Cardiol. 2020;5(9):1020–6.
Igase M, et al. Increased expression of angiotensin converting enzyme 2 in conjunction with reduction of neointima by angiotensin II type 1 receptor blockade. Hypertens Res. 2008;31(3):553–9.
Ni W, et al. Role of angiotensin-converting enzyme 2 (ACE2) in COVID-19. Crit Care. 2020;24(1):422.
Kundura L, et al. Angiotensin II induces reactive oxygen species, DNA damage, and T-cell apoptosis in severe COVID-19. J Allergy Clin Immunol. 2022;150(3):594–603e2.
Imai Y, et al. Angiotensin-converting enzyme 2 protects from severe acute lung failure. Nature. 2005;436(7047):112–6.
de Abajo FJ, et al. Use of renin-angiotensin-aldosterone system inhibitors and risk of COVID-19 requiring admission to hospital: a case-population study. Lancet. 2020;395(10238):1705–14.
Zhao HJ, et al. ARB might be superior to ACEI for treatment of hypertensive COVID-19 patients. J Cell Mol Med. 2021;25(23):11031–4.
Lopes RD, 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(3):254–64.
Paguio JA, et al. Angiotensin converting enzyme inhibitors and angiotensin II receptor blockers and outcomes in hospitalized patients with COVID-19: an updated systematic review and meta-analysis of randomized clinical trials. Expert Rev Cardiovasc Ther. 2023;21(3):219–26.
Liabeuf S, et al. Association between renin-angiotensin system inhibitors and COVID-19 complications. Eur Heart J Cardiovasc Pharmacother. 2021;7(5):426–34.

Supplementalmaterials.docx

Download PDF

Journal Publication

published 11 Aug, 2023

Read the published version in Cardiovascular Drugs and Therapy →

Reviewers agreed at journal
22 Jun, 2023
Reviewers invited by journal
19 Jun, 2023
Editor invited by journal
02 Jun, 2023
Editor assigned by journal
02 Jun, 2023
First submitted to journal
31 May, 2023
Editorial decision: Minor Changes Required
18 May, 2023

You are reading this latest preprint version

Beneficial effects of Angiotensin II Receptor Blockers on Mortality in Patients with COVID-19: a Retrospective Study from 2019 to 2020 in China

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusions

Trial registration

Figures

Introduction

Methods

Data collection and endpoint definitions

Statistical analysis and mapping

Results

Primary outcomes

Secondary outcomes

Subgroup analysis

Comparison between patients using ACEIs and ARBs

Discussion

Study limitations

Conclusion

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1