Evaluating the effectiveness and safety of Azvudine for hospitalised patients with COVID-19 and hypertension: a multicenter retrospective cohort study

Abstract

Hypertension is widely acknowledged as a major risk factor for disease severity and death in patients with coronavirus disease 2019 (COVID-19). Azvudine is recommended for COVID-19 patients in China. However, its clinical efficacy and safety for individuals with hypertension remain unclear. This nine-center retrospective cohort study included 32,864 hospitalized COVID-19 patients in Henan Province, China, from December 2022 to January 2023. Among these patients, those with hypertension were identified and divided into the Azvudine and control groups (standard treatment without antiviral medication) after propensity score matching (PSM) at a 1:1 ratio. The primary outcomes measured were all-cause mortality and composite disease progression. Subgroup analyses and sensitivity tests were conducted to verify the robustness of the results. Safety was assessed based on adverse events (AEs). After PSM to balance baseline characteristics, the analysis included 2434 Azvudine recipients and 2434 controls, forming a final matched cohort. Azvudine was associated with a lower risk of all-cause mortality (HR: 0.64, 95% CI 0.519–0.780; P < 0.001) and composite disease progression (HR: 0.84, 95% CI 0.719–0.985; P = 0.032) in hypertensive patients with COVID-19. In five sensitivity analyses, Azvudine showed a highly robust effect in reducing all-cause mortality, while the evidence for a reduction in the progression of composite disease progression was less consistent. No significant difference in severe AEs (≥ Grade 3) was observed between groups. These real-world findings suggest Azvudine may be a promising antiviral option for hypertensive COVID-19 patients, but further prospective trials are necessary to confirm these results.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-34514-1.

Introduction

The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, with over 770 million confirmed cases and 6.9 million reported deaths as of March 2024¹. Although vaccination efforts and antiviral treatments have significantly reduced morbidity and mortality rates, vulnerable groups, including individuals with comorbidities like hypertension, still remain disproportionately affected^2–4. Hypertension, affecting approximately one-third of the population worldwide, has emerged as an independent risk factor for adverse COVID-19 outcomes, including intensive care unit (ICU) admission, mechanical ventilation, and mortality^5–8. Several lines of pathophysiological evidence have identified potential mechanisms, including dysregulated immune responses, endothelial dysfunction, and chronic inflammation, that contribute to the link between the deterioration of hospitalized COVID-19 patients and hypertension⁹. Despite advances in therapeutic strategies, there remains an unmet need for effective and safe antiviral agents for this high-risk group.

Current guidelines recommend initiating antiviral treatment promptly to decrease viral replication and prevent disease progression¹⁰. Oral antivirals such as Nirmatrelvir/Ritonavir (Paxlovid), Remdesivir, and Molnupiravir have demonstrated modest benefits for hospitalized patients^11–14. However, their use is often limited by factors such as cost, drug-drug interactions, and contraindications for patients with renal or liver injury^15–17. Azvudine, a novel nucleoside analog targeting the RNA-dependent RNA polymerase (RdRp) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has demonstrated significant antiviral activity in vitro and a favorable pharmacokinetic profile¹⁸. Since its approval in China in 2022, Azvudine has been widely adopted in clinical practice, particularly in resource-limited settings¹⁹. Our previous randomized, open-label, controlled clinical trial indicated that Azvudine may reduce the time to nucleic acid negativity conversion²⁰, and several retrospective studies have reported therapeutic benefits in COVID-19 patients treated with oral Azvudine^21–23. Collectively, these findings suggest a possible role for Azvudine in treating COVID-19. However, data on its safety and effectiveness in patients with hypertension are still limited.

Therefore, this study aimed to evaluate the real-world effectiveness and safety of Azvudine in hospitalized COVID-19 patients with hypertension. Using a multicenter retrospective cohort design, we compared outcomes in patients treated with Azvudine to those receiving standard care without antiviral therapy. The results of this analysis are intended to inform clinical decision-making and guide future research on antiviral strategies for this high-risk population.

Material and methods

Study design and participants

This retrospective cohort study involved nine centers in Henan Province and included all hospitalized patients with confirmed SARS-CoV-2 diagnoses. The participating hospitals were the First Affiliated Hospital of Zhengzhou University, Henan Provincial Chest Hospital, Luoyang Central Hospital, the Fifth People’s Hospital of Anyang, Henan Infectious Disease Hospital, Nanyang Central Hospital, Shangqiu Municipal Hospital, Guangshan County People’s Hospital, and Fengqiu County People’s Hospital. The research period spanned from December 5, 2022, when China officially began easing stringent COVID-19 restrictions, to January 31, 2023.

Eligible patients included those who (1) were hospitalized with a confirmed SARS-CoV-2 infection, verified by either a rapid antigen test or reverse transcriptase–polymerase chain reaction, (2) were diagnosed with hypertension, defined as either the use of antihypertensive medications or a formal diagnosis (systolic blood pressure (SBP) ≥ 140 mmHg, and/or diastolic blood pressure (DBP) ≥ 90 mmHg), and (3) received either oral Azvudine or standard treatment without antiviral medications during their hospital stay. Key exclusion criteria included: (1) individuals under 18 years, and (2) those who received antiviral treatments other than Azvudine. Furthermore, hypertensive COVID-19 patients were divided into two groups: those receiving standard treatment plus Azvudine and those receiving standard treatment without antiviral agents. The diagnosis and treatment of COVID‐19 referred to the “diagnosis and treatment of novel coronavirus pneumonia (Trial version 9 or version 10)” issued by the National Health Commission of the People’s Republic of China^24,25.

Data source

Data collection was primarily derived from patients’ medical records and included demographics, medical history, and clinical records that reflected admission dates, diagnoses, prescriptions, laboratory tests, imaging results, ICU admissions, and discharge or death dates.

Procedures

Hypertensive COVID-19 patients were divided into two groups: the Azvudine group (receiving oral Azvudine at 5 mg once daily) and the control group (receiving standard treatment without any antiviral agents) during the observation period. To minimize confounding, we performed propensity score matching (PSM) to compare outcomes between the Azvudine and control groups. Propensity scores were estimated using a multivariable logistic regression model that included all baseline variables independently associated with Azvudine treatment (those with a multivariable P < 0.05). Patients in the Azvudine and control groups were matched in a 1:1 ratio using nearest-neighbor matching without replacement. Covariate balance after matching was assessed with standardized mean differences (SMDs), with an SMD < 0.10 indicating adequate balance. Full details of the PSM procedure, including variables and matching parameters, were performed as described previously²¹.

Definition of covariates

The baseline characteristics of patients, including age, gender, body mass index (BMI), and the severity of SARS-CoV-2 infection, were collected on the day of their COVID-19 diagnosis in accordance with the guidelines in China.

Upon admission, patients were categorized as having “mild”, “moderate”, or “severe” conditions based on the COVID-19 diagnosis and treatment guidelines (trial version 9 or version 10). The use of systemic steroid therapy or antibiotics within 24 h of admission was classified as either “No” or “Yes”. The timing of Azvudine and Paxlovid administration was categorized as “ > 5 days” or “0–5 days” from the initial diagnosis.

Disease severity was classified as follows: Mild: Patients exhibited only typical symptoms of respiratory tract infection, such as a dry throat, sore throat, cough, and fever. Moderate: Patients experienced a persistent high fever for more than 3 days, a respiratory rate of fewer than 30 breaths per minute, resting oxygen saturation greater than 93%, or imaging that displayed characteristic manifestations of COVID-19 pneumonia. Severe: Patients demonstrated a respiratory rate of 30 breaths per minute or more, resting oxygen saturation of 93% or less, a PaO₂/FiO₂ ratio of 300 mmHg or less, or lung lesions that progressed by more than 50% within 24 to 48 h. The necessity for mechanical ventilation, shock, or admission to the intensive care unit (ICU) for monitoring signifies critical illness.

Additional data collected included the number of vaccination doses at diagnosis, and comorbidities such as diabetes, liver disease, cardiovascular disease, kidney disease, chronic respiratory diseases, autoimmune diseases, and malignancies. Key laboratory parameters were also recorded, including neutrophil (Neut), lymphocyte (Lymph), glucose (Glu), high-density lipoprotein (HDL), low-density lipoprotein (LDL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (CREA), glomerular filtration rate (GFR), C–reactive protein (CRP), procalcitonin (PCT), prothrombin time (PT), activated partial thromboplastin time (APTT), cholesterol (CH), triglyceride (TG), alkaline phosphatase (ALP), gamma-glutamyl transpeptidase (GGT), albumin (ALB), and total bilirubin (TBIL)..

Outcomes

The outcomes of this study included all-cause mortality and composite disease progression. All-cause mortality refers to death from any cause that occurs in the study population during the study’s defined follow-up period. It records the patient’s survival status from enrollment to a predefined observation endpoint (discharge date, or day 30). All-cause mortality was determined using electronic medical records. Composite disease progression was defined by events such as death, advancement to severe or critical illness in mild or moderate patients, and escalation to critical disease for those with severe cases.

The safety outcomes were evaluated based on changes in clinical laboratory analyses and reported AEs. AEs were categorized according to the Common Terminology Criteria for Adverse Events (version 5.0)²⁶ for participants in both the Azvudine and control groups. The results were collected during the treatment period from the administration of Azvudine to five half-lives after the last dose. When multiple abnormalities were observed, the most severe results were selected for further analysis.

Sensitivity analysis

To evaluate the robustness of our findings, we employed a series of complementary sensitivity analyses. First, we employed a 1:1 greedy propensity score match using a probabilistic model. To assess the impact of missing data, missing values were imputed via the mean, followed by an additional 1:1 greedy match based on a logistic regression model. Furthermore, to ensure a biologically plausible temporal association between treatment and outcome, we restricted the analysis by excluding patients discharged on the first day after admission, consistent with the drug’s time to peak blood concentration. To quantify the potential for unmeasured confounding, we incorporated E-value analysis and double-robust methods. Finally, to maximize the use of our data, we applied inverse probability of treatment weighting (IPTW) to create a balanced pseudo-population.

Statistical analysis

Demographic characteristics and baseline data were summarized through descriptive statistics, with presentation formats adapted to variable types across two independent cohorts. Continuous variables were compared using independent t-tests or non-parametric Mann–Whitney U tests as appropriate, while categorical variables were analyzed through χ² tests. Survival distributions were estimated via Kaplan–Meier curves with between-group differences assessed through log-rank testing. Time-varying Cox regression models were employed to calculate adjusted hazard ratios (HRs) along with corresponding 95% confidence intervals (CIs), identifying potential predictors of the primary outcomes. Model adequacy was verified through Schoenfeld residual analysis to confirm proportional hazards assumptions, complemented by variance inflation factor (VIF) evaluation to detect multicollinearity (VIF threshold ≥ 5 indicating significant collinearity). Absolute risk reduction (ARR) and the number needed to treat (NNT) were also calculated.

We removed variables with a missing value greater than 20%, and did multiple imputation via the “mice” package in R to fill in the missing data for variables with a missing value less than 20%. Specifically, the first step is to generate a random distribution of missing variables through a regression model, and then randomly select the imputed values used to replace the missing values, and set the number of imputations to 5 times to generate 5 different filled datasets. In the second step, the required statistical analysis is performed on each complete data set to obtain the corresponding parameter estimates and standard errors. In the third step, according to the Rubin’s rules, the analysis results of each dataset are adjusted for weighted average and variance to obtain the final parameter estimate and standard error.

Safety outcomes were presented as frequency proportions or median values with interquartile ranges (IQRs), and compared using χ² tests for categorical data and Wilcoxon rank-sum tests for continuous measures. Prespecified subgroup analyses stratified by baseline covariates were performed to validate the consistency of results across population subsets. All analyses were conducted in the R statistical computing environment (version 4.0.3; R Foundation), with two-tailed statistical significance established at α = 0.05.

Ethics statement

This study was conducted and designed in accordance with the principles of the Declaration of Helsinki. It received approval from the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (approval number: 2023-KY-0865-001). It is also registered on ClinicalTrials.gov (NCT06349655). Since the private information of all patients was not disclosed in this retrospective study, the Ethics Committee of the First Affiliated Hospital of Zhengzhou University approved waiving the requirement for informed consent.

Results

Patient characteristics

During the study period, 32,864 hospitalized COVID-19 patients were initially screened (Fig. 1). The eligibility criteria identified 11,031 patients with a history of hypertension. Among these patients, 2556 received Azvudine therapy, while 8475 controls were managed with standard care without antiviral agents. After PSM to reduce confounding effects, 2434 patients in the control group were enrolled to match with 2434 recipients of Azvudine for the final comparative analysis (Table 1). Statistical differences were observed between the two groups before matching across several clinical parameters, including age, gender, disease severity at diagnosis, concurrent steroid therapy and antibiotics, comorbidities (such as diabetes, cardiovascular and cerebrovascular diseases, liver and kidney diseases, chronic respiratory diseases, autoimmune diseases, and primary malignant tumors), and key laboratory test results. However, the post-matching analysis confirmed successful covariate balance, establishing comparable baseline profiles between the two groups (all P > 0.05, Fig. S1).

Fig. 1.

The flowchart of study design.

Table 1.

Baseline characteristics of COVID-19 patients with hypertension before and after 1:1 propensity score matching.

Baseline characteristics	Before matching			After propensity score matching (1:1)
	Control (n = 8475)	Azvudine (n = 2556)	P value	Control (n = 2434)	Azvudine (n = 2434)	P value
Gender, n (%)			< 0.001			0.539
Male	4661 (55.0)	1536 (60.1)		1468 (60.3)	1446 (59.4)
Female	3814 (45.0)	1020 (39.9)		966 (39.7)	988 (40.6)
Age, year, (mean ± SD)	68.60 (13.53)	70.14 (13.52)	< 0.001	69.87 (13.95)	69.93 (13.58)	0.872
BMI, kg/m2, (mean ± SD)	24.36 (3.98)	24.61 (4.02)	0.007	24.54 (4.02)	24.57 (4.00)	0.762
Severity at admission, n (%)			< 0.001			0.946
Mild	824 (9.7)	154 (6.0)		155 (6.4)	152 (6.2)
Moderate	6298 (74.3)	1773 (69.4)		1710 (70.3)	1704 (70.0)
Severe	1353 (16.0)	629 (24.6)		569 (23.4)	578 (23.7)
Vaccination doses, n (%)
0 dose	2358 (27.8)	780 (30.5)	0.109	735 (30.2)	743 (30.5)	0.88
1 dose	481 (5.7)	155 (6.1)		148 (6.1)	144 (5.9)
2 doses	1252 (14.8)	349 (13.7)		345 (14.2)	339 (13.9)
3 doses	4289 (50.6)	1246 (48.7)		1189 (48.8)	1183 (48.6)
4 doses	93 (1.1)	25 (1.0)		16 (0.7)	24 (1.0)
5 doses	2 (0.0)	1 (0.0)		1 (0.0)	1 (0.0)
Concomitant systemic steroid, n (%)			< 0.001			0.817
No	6453 (76.1)	1392 (54.5)		1400 (57.5)	1391 (57.1)
Yes	2022 (23.9)	1164 (45.5)		1034 (42.5)	1043 (42.9)
Concomitant antibiotics, n (%)			< 0.001			0.886
No	5047 (59.6)	1137 (44.5)		1116 (45.9)	1110 (45.6)
Yes	3428 (40.4)	1419 (55.5)		1318 (54.1)	1324 (54.4)
Comorbidities, n (%)
Diabetes	2880 (34.0)	898 (35.1)	0.293	886 (36.4)	862 (35.4)	0.492
Liver diseases	891 (10.5)	200 (7.8)	< 0.001	209 (8.6)	200 (8.2)	0.679
Cardio-cerebral diseases	4376 (51.6)	943 (36.9)	< 0.001	966 (39.7)	940 (38.6)	0.463
Kidney diseases	1929 (22.8)	1033 (40.4)	< 0.001	919 (37.8)	916 (37.6)	0.953
Chronic respiratory diseases (%)	1383 (16.3)	515 (20.1)	< 0.001	503 (20.7)	483 (19.8)	0.498
Autoimmune diseases (%)	281 (3.3)	68 (2.7)	0.111	63 (2.6)	64 (2.6)	1
Primary malignant tumor	1365 (16.1)	152 (5.9)	< 0.001	142 (5.8)	152 (6.2)	0.588
Laboratory parameters, (mean ± SD)
Neutrophil, × 109/L	5.73 (4.05)	5.66 (3.76)	0.438	5.81 (4.07)	5.67 (3.78)	0.215
Lymphocyte, × 109/L	1.22 (1.15)	1.06 (0.89)	< 0.001	1.09 (0.66)	1.07 (0.90)	0.335
Glucose, mmol/L	7.55 (4.10)	8.14 (4.48)	< 0.001	8.03 (4.64)	8.05 (4.44)	0.862
High-density lipoprotein, mmol/L	1.07 (0.48)	1.06 (0.37)	0.154	1.07 (0.54)	1.06 (0.38)	0.346
Low-density lipoprotein, mmol/L	2.33 (1.00)	2.26 (0.91)	0.001	2.26 (1.03)	2.26 (0.92)	0.901
Alanine aminotransferase, IU/L	34.10 (99.79)	36.83 (116.56)	0.245	35.13 (103.16)	36.60 (117.69)	0.643
Aspartate aminotransferase, IU/L	41.56 (134.03)	43.31 (123.44)	0.555	39.20 (87.55)	42.32 (117.76)	0.294
Creatinine, μmol/L	150.25 (340.76)	125.36 (218.60)	< 0.001	128.38 (189.47)	127.18 (223.15)	0.84
Glomerular filtration rate, ml/min	73.21 (35.17)	71.43 (35.03)	0.025	71.66 (35.30)	71.40 (35.08)	0.8
C-reactive protein, mg/L	46.78 (61.53)	52.49 (64.44)	< 0.001	52.42 (65.76)	51.79 (64.24)	0.736
Procalcitonin, ng/ml	1.93 (10.66)	1.47 (8.31)	0.044	1.61 (9.61)	1.47 (8.46)	0.592
Prothrombin time, s	15.50 (9.19)	19.01 (11.24)	< 0.001	18.34 (11.44)	18.32 (10.84)	0.946
Activated partial thromboplastin time, s	27.80 (10.60)	24.33 (11.97)	< 0.001	25.11 (11.01)	24.84 (11.98)	0.41
Cholesterol, mmol/L	4.35 (4.28)	4.12 (2.36)	0.007	4.13 (2.56)	4.12 (2.41)	0.976
Triglyceride, mmol/L	1.78 (4.06)	1.66 (3.16)	0.177	1.67 (3.31)	1.66 (3.11)	0.962
Alkaline phosphatase, IU/L	88.83 (64.43)	80.07 (48.90)	< 0.001	81.37 (44.13)	80.57 (49.77)	0.556
Gamma-glutamyl transpeptidase, IU/L	53.59 (92.56)	55.98 (89.30)	0.251	55.68 (96.88)	56.14 (90.89)	0.862
Albumin, g/L	38.93 (20.53)	37.34 (39.09)	0.007	38.17 (32.19)	37.62 (40.00)	0.597
Total bilirubin, μmol/L	12.87 (19.89)	11.69 (9.27)	0.004	11.85 (8.73)	11.68 (9.36)	0.533

All-cause mortality and composite disease progression

The Azvudine group had a mortality rate of 7.56% (n = 184), compared to 8.79% (n = 214) in the control group. Kaplan–Meier curve analysis indicated a significantly lower risk of all-cause mortality among patients treated with Azvudine compared to controls (log-rank P = 0.00017; Fig. 2A). Time-varying Cox proportional hazards regression identified Azvudine treatment as an independent protective factor, significantly decreasing overall mortality (HR: 0.64; 95% CI 0.519–0.780; P < 0.001). Other significant risk factors for mortality included advanced age, severe COVID-19, the number of vaccination doses, use of antibiotics, presence of liver diseases or cardio-cerebral disorders, elevated neutrophil count, hyperglycemia, increased CRP, elevated AST, hypoalbuminemia, elevated ALP, increased GGT, higher creatinine, lower HDL, and prolonged APTT. (all P < 0.05, Fig. S2). After adjusting for multiple variables, the all-cause mortality rate was 5.95 per 1000 person-days (184 cases in 30,943 person-days at risk) in the Azvudine group, compared to 8.64 per 1000 person-days (216 cases in 25,003 person-days at risk) in the control group (P < 0.001). Azvudine was associated with an absolute risk reduction (ARR) of 0.269 and a number needed to treat (NNT) of 372. These findings suggest that the use of Azvudine is associated with improved survival among hypertensive COVID-19 patients compared to standard care (Fig. 3).

Fig. 2.

Kaplan–Meier curves for COVID-19 patients with hypertension receiving Azvudine treatment, compared to those in the control group. Cumulative hazard of all‐cause death (A) and composite disease progression (B).

Fig. 3.

Time-varying Cox proportional hazards regression analysis of all-cause mortality and composite disease progression in COVID-19 patients with hypertension treated with Azvudine, compared to those in the control group. Abbreviations: HR, Hazard Ratio; 95% CI, 95% Confidence Interval; PDs, Person-days; Incidence, events/per 1000 PDs.

A total of 650 composite disease progression events were observed, with 330 (50.77%) in the Azvudine group and 320 (49.23%) in the control group. Differences in survival risk between the two groups were evaluated using Kaplan–Meier survival analysis. Azvudine treatment showed a higher survival rate for COVID-19 patients with hypertension (P = 0.034, Fig. 2B). Consistent with the mortality outcomes, Cox regression analysis demonstrated a significant reduction in composite disease progression with Azvudine treatment (HR: 0.84; 95% CI 0.719–0.985; P = 0.032), corresponding to an incidence of 11.74 per 1000 person-days in the Azvudine group and 14.05 per 1000 person-days in the control group (ARR: 0.231; NNT: 433; Fig. 3). Additional risk factors for composite disease progression included older age, severe COVID-19, the number of vaccination doses, cardio-cerebral disease, kidney disease, elevated neutrophil count, hyperglycemia, and hypoalbuminemia (all P < 0.05; Fig. S3). Collectively, these results indicate that Azvudine treatment is associated with a significantly lower risk of both all-cause mortality and composite disease progression in hospitalized hypertensive patients with COVID-19.

Subgroup analysis

We conducted prespecified subgroup analyses stratified by key clinical covariates to evaluate the heterogeneity of treatment effects on both all-cause mortality and composite disease progression outcomes among hypertensive COVID-19 patients receiving Azvudine therapy, using formal testing with interaction P values. (Table 2). The results showed that male patients exhibited higher all-cause mortality rates than female patients (P value for interaction = 0.047). It was worth noting that the all-cause death rate for patients receiving Azvudine treatment and systemic steroid therapy (HR, 0.51; 95% CI, 0.38–0.69) was significantly lower than the treatment effect observed without steroid use (HR, 0.87; 95% CI, 0.67–1.14), with a P for interaction of 0.007, which was consistent with previous reports²⁷. Nevertheless, there are no significant subgroup differences in all-cause mortality related to age, severity at admission, number of vaccination doses, use of antibiotics, diabetes, liver diseases, cardio-cerebral diseases, kidney diseases, chronic respiratory diseases, autoimmune diseases, and primary malignant tumors (all P values for interaction > 0.05). Additionally, there was no significant difference in composite disease progression outcomes between the Azvudine group and the control group. Overall, these results suggest that combining Azvudine and corticosteroids may provide enhanced protective effects.

Table 2.

Subgroup analyses on the effectiveness of Azvudine in reducing the risk of mortality from all causes and composite disease progression in COVID-19 patients with hypertension.

Characteristic	All-Cause mortality		Composite disease progression
	HR (95%CI)	P value for interaction	HR (95%CI)	P value for interaction
Gender
Male	0.79 (0.62 − 1.00)	0.047	0.94 (0.78 − 1.13)	0.052
Female	0.50 (0.35 − 0.72)		0.68 (0.51 − 0.89)
Age, year
≤ 60 years	0.94 (0.58 − 1.52)	0.141	0.68 (0.47 − 0.98)	0.178
> 60 years	0.63 (0.51 − 0.79)		0.89 (0.75 − 1.05)
Severity at admission
Mild	1.79 (0.46 − 6.99)	0.141	1.05 (0.28 − 3.93)	0.091
Moderate	0.82 (0.56 − 1.19)		1.23 (0.84 − 1.81)
Severity	0.61 (0.48 − 0.77)		0.79 (0.67 − 0.93)
Vaccination doses
0 dose	0.73 (0.54 − 1.00)	0.768	0.91 (0.71 − 1.16)	0.978
1 dose	0.86 (0.45 − 1.67)		0.93 (0.51 − 1.67)
2 doses	0.54 (0.29 − 0.99)		0.82 (0.52 − 1.29)
3 doses	0.66 (0.48 − 0.90)		0.79 (0.62 − 1.00)
4 doses	0.23 (0.02 − 2.84)		0.69 (0.15 − 3.26)
5 doses	0.73 (0.54 − 1.00)		NA (NA − NA)
Concomitant systemic steroid
No	0.87 (0.67 − 1.14)	0.007	0.86 (0.70 − 1.06)	0.678
Yes	0.51 (0.38 − 0.69)		0.83 (0.66 − 1.04)
Concomitant antibiotics
No	0.82 (0.59 − 1.15)	0.283	0.93 (0.72 − 1.21)	0.388
Yes	0.63 (0.50 − 0.81)		0.81 (0.67 − 0.98)
Diabetes
No	0.72 (0.56 − 0.94)	0.499	0.83 (0.69 − 1.01)	0.841
Yes	0.63 (0.47 − 0.86)		0.87 (0.68 − 1.12)
Liver diseases
No	0.69 (0.56 − 0.86)	0.942	0.85 (0.72 − 1.00)	0.973
Yes	0.66 (0.41 − 1.08)		0.85 (0.53 − 1.36)
Cardio-cerebral diseases
No	0.77 (0.57 − 1.05)	0.227	0.88 (0.71 − 1.09)	0.486
Yes	0.62 (0.48 − 0.80)		0.81 (0.65 − 1.01)
Kidney diseases
No	0.64 (0.50 − 0.82)	0.354	0.87 (0.71 − 1.07)	0.825
Yes	0.77 (0.56 − 1.07)		0.83 (0.65 − 1.05)
Chronic respiratory diseases
No	0.66 (0.53 − 0.81)	0.427	0.83 (0.70 − 0.99)	0.694
Yes	0.83 (0.48 − 1.41)		0.91 (0.66 − 1.25)
Autoimmune diseases
No	0.69 (0.56 − 0.84)	0.972	0.84 (0.72 − 0.98)	0.373
Yes	0.70 (0.14 − 3.49)		1.27 (0.37 − 4.28)
Primary malignant tumor
No	0.69 (0.56 − 0.84)	0.768	0.83 (0.71 − 0.97)	0.299
Yes	0.62 (0.30 − 1.28)		1.11 (0.60 − 2.08)

HR, Hazard ratio; 95%CI, 95% confidence interval.

Sensitivity analysis

To rigorously examine the robustness of our mortality risk estimates, we conducted a comprehensive series of sensitivity analyses. Initial propensity score matching using a probabilistic approach revealed that Azvudine was associated with a significantly lower risk of both all-cause mortality (log-rank P = 0.00016) and composite disease progression (log-rank P = 0.0028) in hypertensive COVID-19 patients (Fig. S4, Table S1). This protective association remained consistent after mean imputation for missing baseline data (all-cause mortality: log-rank P < 0.0001; composite disease progression: log-rank P = 0.0042; Fig. S6, Table S2) and after excluding patients discharged within the first day of treatment to account for the drug’s pharmacodynamics (all-cause mortality: log-rank P = 0.0011; composite disease progression: log-rank P = 0.037; Fig. S8, Table S3). The Cox regression analysis confirmed that Azvudine was associated with a reduced risk of all-cause death, yet revealed no statistically significant difference in survival times between the two groups for the composite disease progression, except for the analysis employing mean imputation for missing data (Figs. S5, S7 and S9).

To further substantiate these findings and address potential confounding, we employed doubly robust estimation and E-value analysis. The doubly robust analysis maintained the HR for all-cause mortality at 0.64 (P < 0.001), while the HR for composite disease progression attenuated to 0.93 and was no longer statistically significant (P = 0.291; Fig. S10a). In the E-value Analysis, the E-value for the association with all-cause mortality (HR = 0.64) and composite disease progression (HR = 0.84) is 2.50 and 1.67, respectively (Fig. S10b).

Finally, application of IPTW created a balanced pseudo-population in which Azvudine continued to demonstrate a significant reduction in all-cause mortality (Kaplan–Meier P = 0.001; Cox regression HR: 0.74, 95% CI 0.620–0.892, P = 0.001). However, its effect on composite disease progression was not significant in this model (Kaplan–Meier P < 0.001; Cox regression HR: 0.93, 95% CI 0.802–1.069, P = 0.294; Figs. S11 and S12). Collectively, these analyses robustly confirm the association between Azvudine and reduced all-cause mortality, while indicating that the effect on the composite progression endpoint is less consistent across statistical models.

Safety

To evaluate the safety of Azvudine during the observed period, we analyzed the incidence of treatment-related AEs among patients who received either Azvudine or no antiviral treatment (Table 3). Anemia was the most common AE seen in patients taking Azvudine compared to the control group (Azvudine 31% vs. control 24%, P = 0.006). Conversely, the Azvudine group had a significantly lower risk of abnormal serum phosphorus levels (14% vs. 23%, P = 0.001) and elevated creatinine levels (14% vs. 19%, P = 0.017) compared to the control group. For adverse events graded as grade 3 or higher, there was no statistically significant difference between the two groups. Although there were more cases of anemia in the Azvudine group, Azvudine can still be considered safe for hypertensive patients with COVID-19, as no significant increase in serious adverse events was observed.

Table 3.

Incidence of adverse event of COVID-19 patients with hypertension receiving Azvudine.

Adverse events (N, %)	Available dataa		All gradesb			Grade ≥ 3b
	Control	Azvudine	Control	Azvudine	P value	Control	Azvudine	P value
Lymphocyte count decreased	1857	1567	216(23%)	272(20%)	0.2	117(12%)	159(12%)	0.8
Lymphocyte count increased	1527	1142	2 (0.2%)	6 (0.4%)	0.5	1 (0.1%)	0 (0%)	0.4
Neutrophil count increased	2259	2162	13 (5.6%)	24 (7.0%)	0.5	3 (1.3%)	5 (1.5%)	> 0.9
PLT-count decreased	1895	1690	93 (13%)	124(13%)	> 0.9	23 (3.3%)	29 (3.1%)	0.8
Anemia	2080	2057	139(24%)	215(31%)	0.006	43 (7.4%)	61 (8.8%)	0.4
Serum phosphorus	2223	2083	74 (23%)	62 (14%)	0.001	0 (0%)	0 (0%)
Hypokalemia	1783	1547	200 (21%)	278(21%)	0.8	56 (5.9%)	89 (6.9%)	0.4
Hyperkalemia	1558	1231	26 (2.7%)	46 (3.5%)	0.3	5 (0.5%)	5 (0.4%)	0.8
ALT increased	1822	1511	113(14%)	201(17%)	0.13	19 (2.4%)	21 (1.8%)	0.3
AST increased	1693	1310	40 (4.7%)	48 (3.9%)	0.3	20 (2.4%)	25 (2.0%)	0.6
ALP increased	1872	1586	2 (0.3%)	3 (0.3%)	> 0.9	0 (0%)	0 (0%)
GGT increased	2069	1942	68 (14%)	89 (14%)	> 0.9	3 (0.6%)	5 (0.8%)	> 0.9
Hyperuricemia	1805	1521	35 (5.0%)	63 (6.2%)	0.3	0 (0%)	0(0%)
CREA increased	1938	1671	126(19%)	137(14%)	0.017	7 (1.0%)	12 (1.3%)	0.7
Hyperglycemia	2077	1887	7 (1.7%)	8 (1.3%)	0.6	2 (0.5%)	2 (0.3%)	> 0.9
Hypercholesteremia	2373	2276	5 (4.6%)	5 (2.4%)	0.3	0 (0%)	0 (0%)
Hypertriglyceridemia	2465	2454	2 (2.4%)	2 (1.6%)	0.5	70 (84%)	100(81%)	> 0.9

PLT, platelets; Hb, hemoglobin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; UA, uric acid; CREA, creatinine; Glu, glucose; CH, Cholesterol; TG, Triglyceride. ^a: Number of people who completed the follow-up of data collection for this indicator. ^b: Severity grades were defined according to the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE), version 5.0

Discussion

During the pandemic, nearly half of COVID patients with comorbidities were observed to be at higher risk of hospitalization or death. Previous studies have demonstrated that Azvudine is associated with better outcomes in COVID-19 patients with pre-existing diabetes, liver disease, kidney diseases, or cardiovascular disease^28–31. However, the clinical efficacy and safety of Azvudine for individuals with hypertension remain unclear. This multicenter, retrospective cohort study provided robust evidence supporting the efficacy and safety of Azvudine in hospitalized COVID-19 patients with hypertension. Our findings demonstrated that Azvudine significantly reduced all-cause mortality and composite disease progression, showing a favorable safety profile primarily characterized by transient and clinically manageable hematologic adverse events at the 30-day follow-up. This may serve as a potential alternative for treating COVID-19 patients with hypertension.

The key strength of this study lies in its retrospective comparison of standard treatment with a combination of Azvudine, evaluated based on the number of hypertensive participants. The mechanism of SARS-CoV-2 infection may partially explain the association between hypertension and severe COVID-19 in patients. Accumulating evidence suggests that the pathophysiology of hypertension is linked to dysregulation of the renin–angiotensin–aldosterone system (RAAS), chronic low-grade inflammation, and endothelial dysfunction, which worsens the severity of COVID-19 through mechanisms such as ACE2 downregulation, impaired viral clearance, and hypercoagulability⁹. By inhibiting RdRp, Azvudine effectively suppresses early viral replication, helping alleviate the hyperinflammatory cascades triggered by viral load and the associated organ damage. Notably, the observed reduction in mortality (HR: 0.64) was comparable to the efficacy reported in randomized controlled trials of other antivirals, such as Nirmatrelvir/Ritonavir (HR: 0.81)³². However, Azvudine achieves this without needing the co-administration of protease inhibitors. This feature could streamline therapeutic protocols in resource-limited settings, where monitoring for drug-drug interactions is difficult.

Subgroup analyses suggested that Azvudine could reduce all‐cause mortality regardless of age, admission severity, diabetes, liver diseases, cardio-cerebral diseases, kidney diseases, and primary malignant tumors. Nevertheless, Azvudine combined with corticosteroids had been shown to provide survival benefits (HR, 0.51; 95% CI, 0.38–0.69) compared to Azvudine alone (HR, 0.87; 95% CI, 0.67–1.14) in COVID-19 patients with hypertension (P for interaction = 0.007), suggesting a synergistic interaction between antiviral and anti-inflammatory approaches. This finding was consistent with emerging evidence that early viral suppression enhances corticosteroid effectiveness by reducing antigenic triggers of cytokine release syndrome³³. Therefore, the combination of Azvudine and corticosteroids may offer improved protective effects. Besides, subgroup analysis showed that the efficacy of azvudine may differ between male and female patients (interaction P = 0.047). A more significant reduction in the risk of death was observed in female patients (HR, 0.50, 95% CI 0.35–0.72). This difference may stem from gender-related differences in immune response, hormone levels, or comorbidity distribution, but further prospective studies are needed to confirm this. We suggest further exploration of gender as an important consideration in individualized treatment.

Limited information exists on the safety of Azvudine for treating COVID-19. Previous studies have primarily indicated that it is associated with headaches, nausea, elevated transaminase levels, and increased D-dimer levels³⁴. This study found that Azvudine was associated with a higher occurrence of anemia (31% vs. 24%; P = 0.006). However, the incidence of severe adverse events (≥ Grade 3 according to CTCAE criteria) was similar between the two groups, further supporting the safety of Azvudine in hypertensive patients. Additionally, Azvudine demonstrated a superior renal safety profile compared to the control, as evidenced by lower rates of creatinine elevation (14% vs. 19%; P = 0.017) and abnormal phosphatemia (14% vs. 23%; P = 0.001). However, this study did not gather symptom information from patients and could only identify some potential adverse events. Nevertheless, this research provides a reference for medication choices for hypertensive COVID-19 patients.

Our study has several limitations that should be acknowledged. First, as a retrospective study, there is a potential for confounding bias from unmeasured variables, such as immune function, despite our use of PSM and sensitivity analyses. Second, residual confounding may still be present even after adjusting for available confounders. Third, although composite disease progression was significantly reduced in the main analysis, this effect was not consistently seen across all sensitivity analyses. Therefore, this finding should be interpreted with caution and needs further validation. Fourth, we could not assess the long-term efficacy and safety of Azvudine because of the relatively short follow-up period. Finally, as SARS-CoV-2 continues to evolve, the effectiveness of Azvudine against new emerging variants remains to be seen. Fifth, while our study demonstrates that Azvudine significantly reduces the relative risk of clinical outcomes, the higher NNT and the lower ARR suggest that treating a large number of patients is necessary to benefit one individual, implying that its clinical significance may be limited.

Conclusion

In conclusion, Azvudine seems to be a promising treatment option for hypertensive patients with COVID-19, demonstrating a favorable safety profile and an association with reduced mortality in this group. Although a decrease in combined disease progression was observed in the main analysis, this result was not consistently confirmed across all sensitivity analyses and should be interpreted carefully. In clinical practice, early use in high-risk patients and regular blood monitoring are recommended. These findings provide valuable real-world evidence supporting the use of Azvudine in managing COVID-19 in patients with hypertension; however, more prospective studies are necessary to confirm these results.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Yu Chen and Huan Li contributed equally to this work. Zhigang Ren conceived and designed the study; Zhigang Ren, Guangming Li, Guotao Li, Shixi Zhang, Hong Luo, Donghua Zhang, Silin Li, and Guowu Qian managed the patients; Haiyu Wang, Yichen Ma, Ling Wang, Huan Li, Yun Zheng, and Qin Bai collected the data; Huan Li and Haiyu Wang analyzed the data; Yu Chen and Huan Li wrote the manuscript. All authors reviewed and approved the manuscript.

Funding

This work was supported by Prevention and Control of Emerging and Major Infectious Diseases-National Science and Technology Major Project (2025ZD01906305), the Medical Science and Technique Foundation of Henan Province, China (Grant No. 232102311084, 242102310046, 252102311157, and 252102310224), Key Scientific Research Projects of Colleges and Universities in Henan Province (Grant No. 24B320034). It was also supported by the Henan Zhongyuan Medical Science and Technology Innovation and Development Foundation (ZYYC202301ZD), and the Natural Science Foundation Key Project of Henan Province (232300421124).

Data availability

The dataset utilized in the current study is available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was reviewed and approved by the Institutional Review Board of The First Affiliated Hospital of Zhengzhou University (2023-KY-0865-001). This study is retrospective, and no patient privacy information is exposed. Informed consent from the subject was not obtained.