Abstract
Background
Preclinical research has demonstrated that vericiguat can improve myocardial microcirculation. However, real-world evidence of using vericiguat alongside guideline-directed medical therapy (GDMT) in patients with acute coronary syndrome (ACS) complicated by heart failure with reduced ejection fraction (HFrEF) remains limited.
Methods
In this prospective cohort study, 149 ACS patients with left ventricular ejection fraction (LVEF) below 45% were grouped by their treatment preference. Multivariable analyses were used to evaluate the effect of vericiguat on the composite endpoint of hospitalization for heart failure (HHF) or cardiovascular death (CVD). A linear mixed-effects model was used to analyze changes in LVEF and N-terminal pro-brain natriuretic peptide (NT-proBNP) from baseline to 12 months. Differences in the absolute values of Kansas City Cardiomyopathy Questionnaire quality of life (KCCQ-QoL) scores during the follow-up were also evaluated.
Results
In this study, 75 patients (50.3%) received vericiguat plus GDMT treatment, while 74 patients (49.7%) received GDMT treatment alone. The average age of enrolled patients was 65 years, with approximately 17.4% being female. 33.6% were classified as NYHA class III heart failure, and the average LVEF was 38.11%. Over the 12-month follow-up, the composite endpoint event occurred in 5 (6.67%) of 75 patients in the vericiguat group and 13 (17.57%) of 74 patients in the GDMT group (hazard ratio [HR] 0.31 [95% confidence interval (CI) 0.10–0.96]; P = 0.041). By the end of the follow-up, LVEF improved significantly more with vericiguat (least squares mean [LSM] difference 3.31% [95%CI 0.58%-6.04%]; P = 0.018). From the first month, the vericiguat group demonstrated greater reductions in NT-proBNP (P = 0.004) and improvements in KCCQ-QoL (P = 0.042) than the GDMT group, which were maintained through follow-up. Adverse events were mild with vericiguat, similar to the GDMT group.
Conclusion
In the first prospective cohort study focusing on patients with ACS and HFrEF, vericiguat plus GDMT treatment for 12 months reduced the risk of CVD and HHF, while improving cardiac function and quality of life.
Trial registration
ClinicalTrials.gov (NCT06321094). Registered on 06 March 2024, prior to the enrollment of the first participant. https://clinicaltrials.gov/search?cond=NCT06321094.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-026-05622-1.
Keywords: Vericiguat, Acute coronary syndrome, Heart failure with reduced ejection fraction
Introduction
Heart failure (HF) is one of the main manifestations in the end-stage cardiovascular disease, with higher mortality and hospitalization rate. Coronary artery disease (CAD) is the most common cause of HF worldwide [1]. Current guidelines classify CAD into acute coronary syndrome (ACS) and chronic coronary syndrome (CCS). ACS is characterized by a sudden reduction in coronary blood flow, including ST-elevation myocardial infarction (STEMI), non-ST-elevation myocardial infarction (NSTEMI), and unstable angina (UA). It is estimated that there are more than 7 million new cases of ACS worldwide each year, with about 5% of patients died during hospitalization [2, 3].
For patients with ACS complicated by heart failure with reduced ejection fraction (HFrEF), the current guideline-recommended medication regimen (GDMT) includes beta-blockers, renin-angiotensin system inhibitors (angiotensin-converting enzyme inhibitors or angiotensin receptor blockers or angiotensin receptor-neprilysin inhibitors, ACEI/ARB/ARNI), sodium-glucose cotransporter 2 inhibitors (SGLT2i), and mineralocorticoid receptor antagonists (MRA). In addition, guidelines suggest that dual antiplatelet therapy (DAPT) should be carried out within 12 months after ACS, along with intensive lipid-lowering, comprehensive secondary prevention, and lifestyle interventions [4]. Although prompt reperfusion therapy significantly decreases mortality during the acute phase, patients with ACS and HFrEF continue to face an elevated risk of hospitalization for heart failure (HHF) and unfavorable long-term outcomes. Moreover, the risk of major adverse cardiovascular events following acute myocardial infarction (AMI) remains high [5–7]. Therefore, it is of great significance to explore new treatment strategies to further improve clinical outcomes of these patients.
Vericiguat is a novel oral soluble guanylate cyclase (sGC) stimulator. It not only directly stimulates sGC to enhance its activity and increase the level of intracellular cyclic guanosine monophosphate (cGMP) but also increases sGC’s sensitivity to lower nitric oxide (NO) concentrations, thereby potentiating the NO-sGC-cGMP signaling pathway [8]. The core pathological mechanism of ACS involves the rupture or erosion of atherosclerotic plaques, precipitating atherothrombosis and subsequent coronary occlusion. Its pathophysiology involves complex biochemical pathways, including the NO-sGC-cGMP signaling cascade [9–11]. Asymmetric dimethylarginine (ADMA), an endogenous inhibitor of nitric oxide synthase (NOS), is closely associated with endothelial dysfunction and the progression of atherosclerosis [12]. Clinically, after the completion of the VICTORIA trial, vericiguat was approved for the treatment of worsening HFrEF [13]. The VICTOR study further extended its use to patients with stable heart failure, indicating that vericiguat may offer extra survival advantages for stable HFrEF patients in addition to standard GDMT treatments [14]. Whether vericiguat benefit patients with ACS and HFrEF remains unclear, and more evidence is needed to determine if adding vericiguat to a comprehensive GDMT regimen provides additional clinical benefits.
Therefore, this study aims to evaluate the efficacy of adding vericiguat to GDMT in reducing the risk of cardiovascular death (CVD) and HHF in patients with ACS complicated by HFrEF (left ventricular ejection fraction, LVEF < 45%) and further explore its impact on cardiac function and quality of life of the patients.
Methods
Study design
The EVE-ACSrEF study (Clinical registration number: NCT06321094) is a prospective cohort study that consecutively enrolled patients diagnosed with ACS complicated by HFrEF (LVEF < 45%) between March 2024 and October 2024. Inclusion criteria were as follows: (i) meeting the current guideline diagnostic criteria for ACS; [15] (ii) echocardiographic confirmation of LVEF less than 45%; (iii) symptomatic heart failure classified as New York Heart Association (NYHA) class II-IV; (iv) age between 18 and 90 years. Exclusion criteria included: (i) systolic blood pressure (SBP) under 100 mmHg or symptomatic hypotension; (ii) severe renal impairment defined as chronic dialysis or estimated glomerular filtration rate (eGFR) below 15 ml/(min·1.73 m²); (iii) severe liver dysfunction classified as Child-Pugh class C; (iv) presence of severe non-cardiac diseases with an expected survival of shorter than 3 years; (v) allergy to any sGC stimulators; (vi) pregnant or breastfeeding women.
All participants signed written informed consent forms prior to enrollment. The study strictly adhered to the ethical principles of the Declaration of Helsinki and received approval from the Ethics Committee of the First Affiliated Hospital with Nanjing Medical University (Approval No. 2023-SR-731). Based on patients’ willingness to use vericiguat and the physician’s decision, patients were assigned to either the vericiguat plus GDMT group or the standard GDMT group. A total of 160 patients were screened, with 149 meeting the inclusion criteria and included in the analysis—75 in the vericiguat group and 74 in the GDMT group (Fig. 1). All patients were enrolled during their hospitalization. A very small number of patients (5.4%) had a previously established diagnosis of ACS, while the vast majority of patients (94.6%) were initially diagnosed with ACS during the current admission and enrolled accordingly. The initiation of GDMT is primarily predicated on a confirmed diagnosis of HFrEF and eligibility for pharmacotherapy, while its optimization is tailored to the patient’s specific clinical status following the index ACS event, strictly adhering to evidence-based stepwise optimization protocols (Supplementary Methods). Based on individual hemodynamic assessments and after obtaining informed consent, vericiguat was initially administered during the hospital stay as an add-on therapy to GDMT. Treatment was continued long-term following discharge. The total expected follow-up period for all patients was one year. During follow-up, patients underwent regular monitoring at outpatient clinics or hospital wards, with documentation of medication usage, adverse events, and endpoint events recorded at each visit. The initial dose of vericiguat was 2.5 mg once daily, which was gradually increased to 5 mg and ultimately to the target dose of 10 mg under the guidance of blood pressure and clinical symptom assessments. The specific dose escalation of vericiguat is provided in Table S1.
Fig. 1.
Flow chart: screening and follow-up. GDMT, guideline-directed medical therapy; eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure. Some patients had more than one reason for exclusion
Study endpoints
The primary endpoint was the composite of CVD or the first hospitalization for heart failure (HHF). Specifically, HHF is defined as a confirmed re-hospitalization during follow-up with heart failure as the primary cause, on the basis of objective biomarkers or imaging results, as well as the need for intravenous medication and/or oxygen therapy [16]. Secondary endpoints included dynamic changes in LVEF, levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP), and Kansas City Cardiomyopathy Questionnaire-Quality of Life (KCCQ-QoL) scores at baseline and at 1-, 3-, 6-, and 12-months during follow-up [17]. We also further explored the effect of vericiguat on ACS-related endpoints, including new-onset AMI, revascularization procedures, and hospitalization for angina. Clinically relevant safety outcomes included symptomatic hypotension, anemia, syncope and bleeding events.
Statistical analysis
Depending on the distribution of variables, continuous variables were presented as mean and standard deviation or as median with interquartile range (IQR). Categorical variables were described using as numbers and percentages, with group comparisons analyzed by the chi-squared test. For missing data resulting from patient deaths during follow-up, the last observation carried forward before death was used for imputation.
For the primary outcome, graphical representation was performed using the log-rank test alongside risk curves. Cox proportional hazards regression model was applied to estimate the hazard ratio for composite endpoint events between the two groups (vericiguat + GDMT group vs. GDMT group). To address confounding arising from non-randomized design, multivariable adjustment was performed incorporating variables identified as significant at P < 0.05 in univariable analysis.
For secondary outcomes, a linear mixed-effects model was used to assess changes in LVEF and NT-proBNP from baseline to 12 months of follow-up. Prior to analysis, non-Gaussian distributed data were transformed to approximate a Gaussian distribution (log transformation with base 10). Factors influencing outcome events identified by univariable analysis (P < 0.05) were included as covariates for adjustment. Differences in KCCQ-QoL scores between groups were assessed by comparing change scores as well as the use of minimum clinically important difference (MCID). Comparisons between groups were conducted using t-tests or Mann-Whitney U tests; comparisons within groups used paired t-tests or Wilcoxon signed-rank tests.
In addition, to enhance the robustness of the results, a sensitivity analysis employing propensity score matching (PSM) was performed. Vericiguat use was treated as the dependent variable, with gender, age, glomerular filtration rate, NYHA classification, NT-proBNP, and LVEF as covariates. Propensity scores were estimated via multivariable logistic regression and matched using 1:1 nearest neighbor matching with a caliper of 0.2 [18]. The matched samples underwent renewed univariable analysis, followed by analyses of related outcomes including Cox proportional hazards regression, mixed-effects models, and difference comparisons. All statistical analyses were conducted using R software version 4.5 (R Foundation for Statistical Computing). A P-value below 0.05 was considered statistically significant.
Results
Baseline characteristics
A total of 149 patients with ACS complicated by HFrEF (LVEF < 45%) were ultimately included in the statistical analysis for this study. Among them, 75 patients (50.3%) received vericiguat plus GDMT treatment, while 74 patients (49.7%) received GDMT treatment alone. A comparison of baseline characteristics between the two groups revealed no statistically significant differences in gender, age, medication treatments, comorbidities, laboratory parameters, and other factors. The average age of enrolled patients was 65 years, with approximately 17.4% being female. NSTEMI/STEMI accounted for about 51.7%, 33.6% were classified as NYHA class III heart failure, and the average LVEF was 38.11%. To be more specific, the baseline LVEF was 37.31% in the vericiguat group and 38.92% in the GDMT group, with no significant difference between the two groups (P = 0.077). NT-proBNP levels were also showed similar results (median in the vericiguat group 2284.00 pg/mL [IQR 1349.00-4724.55] vs. median in the GDMT group 2717.80 pg/mL [IQR 937.35-6491.95]; P = 0.714). Regarding comorbidities, 63.1% of patients had hypertension, and 50.3% had diabetes, 14.1% had atrial fibrillation, and nearly 40.3% had a history of CAD. Concerning heart failure treatments, baseline use rates of ARNI were 74.7% in the vericiguat group and 64.9% in the GDMT group; beta-blocker use rates were 85.3% and 81.1%, MRAs use rates were 81.3% and 70.3%, and SGLT2i use rates were 68.0% and 68.9%, respectively. However, there was no significant differences between the two groups in these medications use (all P > 0.05) (Table 1). By the end of follow-up, 12 (16.0%), 11 (14.7%), and 52 (69.3%) patients in the vericiguat group achieved vericiguat at a dose of 2.5 mg, 5 mg, and 10 mg, respectively.
Table 1.
Characteristics of the patients before and after PSM
| Characteristics | Before PSM | After PSM | ||||
|---|---|---|---|---|---|---|
| GDMT (N = 74) |
Vericiguat (N = 75) |
P value | GDMT (N = 61) |
Vericiguat (N = 61) |
P value | |
| Age (year) |
68.00 [57.00, 74.00] |
66.00 [54.50, 72.50] |
0.276 |
64.59 (12.51) |
64.52 (11.29) |
0.976 |
| Gender, n (%) | 0.493 | 0.807 | ||||
| Male | 59 (79.7) | 64 (85.3) | 52 (85.2) | 50 (82.0) | ||
| Female | 15 (20.3) | 11 (14.7) | 9 (14.8) | 11 (18.0) | ||
| SBP (mmHg) | 123.85 (16.0) | 128.99 (19.29) | 0.079 | 122.56 (15.97) | 127.70 (19.64) | 0.115 |
| DBP (mmHg) | 75.73 (11.37) | 77.39 (13.69) | 0.423 | 75.80 (10.04) | 75.87 (12.98) | 0.975 |
| LVEF (%) | 38.92 (5.51) | 37.31 (5.51) | 0.077 | 38.35 (5.80) | 38.22 (5.16) | 0.898 |
| Laboratory findings | ||||||
|
NT-proBNP (pg/mL) |
2717.80 [937.35, 6491.95] |
2284.00 [1349.00, 4724.55] |
0.714 |
2447.00 [1038.00, 6573.80] |
2284.00 [1365.60, 4856.10] |
0.770 |
| Hb (g/L) | 130.78 (20.36) | 133.35 (23.04) | 0.473 |
132.00 [117.00, 145.00] |
134.00 [114.00, 147.00] |
0.636 |
|
eGFR (ml/min/1.73 m2) |
69.00 [52.25, 79.75] |
70.00 [54.00, 85.00] |
0.498 |
73.00 [56.00, 83.00] |
70.00 [58.00, 85.00] |
0.945 |
| ALT (U/L) |
22.55 [14.87, 40.90] |
25.70 [13.90, 39.55] |
0.950 |
25.00 [14.70, 45.20] |
25.70 [12.50, 40.00] |
0.570 |
| LDL-C (mmol/L) |
2.28 [1.81, 2.88] |
2.19 [1.76, 2.98] |
0.946 |
2.27 [1.81, 2.89] |
2.15 [1.71, 2.96] |
0.589 |
| NYHA functional class, n (%) | 0.360 | > 0.999 | ||||
| II | 49 (66.2) | 45 (60.0) | 39 (63.9) | 39 (63.9) | ||
| III | 24 (32.4) | 26 (34.7) | 21 (34.4) | 21 (34.4) | ||
| IV | 1 (1.4) | 4 (5.3) | 1 (1.6) | 1 (1.6) | ||
| UA, n (%) | 31 (41.9) | 39 (52.0) | 0.284 | 27 (44.3) | 30 (49.2) | 0.717 |
| NSTEMI/STEMI, n (%) | 43 (58,1) | 36 (48.0) | 0.284 | 34 (55.7) | 31 (50.8) | 0.717 |
| Medical history, n (%) | ||||||
| AF | 9 (12.2) | 12 (16.2) | 0.662 | 6 (9.8) | 10 (16.4) | 0.421 |
| Hypertension | 49 (66.2) | 45 (60.0) | 0.538 | 37 (60.7) | 36 (59.0) | > 0.999 |
| DM | 40 (54.1) | 35 (46.7) | 0.461 | 35 (57.4) | 29 (47.5) | 0.365 |
| CAD | 27 (36.5) | 35 (46.7) | 0.274 | 21 (34.4) | 31 (50.8) | 0.099 |
| Smoking | 31 (41.9) | 41 (54.7) | 0.163 | 27 (44.3) | 34 (55.7) | 0.277 |
| Drinking | 20 (27.0) | 25 (33.3) | 0.516 | 18 (29.5) | 18 (29.5) | > 0.999 |
| ACS-related parameters | ||||||
| CABG, n (%) | 11 (14.9) | 6 (8.0) | 0.289 | 11 (18.0) | 5 (8.2) | 0.180 |
| Number of lesion vessels | 2.69 (0.91) | 2.45 (0.78) | 0.090 | 2.66 (0.96) | 2.49 (0.81) | 0.311 |
| Stent number | 1.34 (1.37) | 1.47 (1.36) | 0.565 | 1.30 (1.37) | 1.59 (1.38) | 0.239 |
| Medication, n (%) | ||||||
| Beta-blocker | 60 (81.1) | 64 (85.3) | 0.493 | 48 (78.7) | 51 (83.6) | 0.643 |
| ARNI | 48 (64.9) | 56 (74.7) | 0.261 | 38 (62.3) | 45 (73.8) | 0.244 |
| SGLT2i | 51 (68.9) | 51 (68.0) | > 0.999 | 43 (70.5) | 43 (70.5) | > 0.999 |
| MRAs | 52 (70.3) | 61 (81.3) | 0.166 | 43 (70.5) | 50 (82.0) | 0.202 |
| Other diuretics | 51 (68.9) | 62 (82.7) | 0.077 | 42 (68.9) | 52 (85.2) | 0.053 |
| Statins | 71 (95.9) | 69 (92.0) | 0.505 | 58 (95.1) | 57 (93.4) | 0.999 |
| Aspirin/Indobufen | 65 (87.8) | 68 (90.7) | 0.769 | 54 (88.5) | 57 (93.4) | 0.527 |
Continuous variables presented as mean ± standard deviation or as median with interquartile range (IQR). Categorical variables are given as percent and were compared using the chi-squared test
PSM propensity score matching, GDMT guideline-directed medical therapy, SBP systolic blood pressure, DBP diastolic blood pressure, LVEF left ventricular ejection fraction, NT-proBNP N-terminal B-type natriuretic peptide, Hb Hemoglobin, ALT alanine aminotransferase, eGFR estimated glomerular filtration rate, LDL-c low density lipoprotein cholesterol, NYHA,New York Heart Association, UA unstable angina, NSTEMI non-ST-elevation myocardial infarction, STEMI ST-elevation myocardial infarction, AF atrial fibrillation, DM diabetes mellitus, CAD coronary disease, VHD valvular heart disease, PCI percutaneous coronary intervention, ACEI angiotensin-converting enzyme inhibitor, ARB angiotensin-receptor blocker, ARNI angiotensin receptor-neprilysin inhibitor, SGLT2i sodium-glucose co-transporter 2 inhibitor, MRAs mineralocorticoid receptor antagonists
Primary endpoint
At the end of the follow-up, the incidence of the primary combined endpoint (CVD or HHF) in the vericiguat group was 6.67% (5/75), which was significantly lower than the conventional GDMT treatment group at 17.57% (13/74). (Table S2) Univariable analysis revealed several factors associated with the primary outcome, including baseline NT-proBNP, age, NYHA class, and vericiguat administration, as detailed in Table S3. After adjusting for these variables in a multivariable Cox model, vericiguat was found to significantly decrease the risk of the primary endpoint event (hazard ratio [HR] 0.31 [95% confidence interval (CI): 0.10–0.96]; P = 0.041) (Fig. 2).
Fig. 2.
Kaplan-Meier curve for the primary endpoint. The primary outcome was a composite of death from cardiovascular causes or first hospitalization for heart failure; Hazard ratio (HR) and P-value reflect adjusted analyses: (i) HR calculated by multivariable Cox model by adjusting for baseline age, NYHA class, and NT-proBNP; (ii) P-value from log-rank test
Secondary endpoints
The linear mixed-effects model, which accounted for baseline NT-proBNP, age and NYHA class, revealed differences both within and between groups in LVEF and NT-proBNP. Over the 12-month follow-up, both groups showed improvements in LVEF and NT-proBNP outcomes, as detailed in Table S4 and Fig. 3(a), 3(b). Starting from the 1-month follow-up, the vericiguat group exhibited significantly lower log-transformed NT-proBNP levels compared to the GDMT group (log-transformed least squares mean [LSM] difference − 0.24 [95% CI -0.40-0.07]; P = 0.004), and this difference persisted through the end of the study (Table 2). For LVEF, the difference between groups increased progressively over time. At 12 months, the vericiguat group showed a significantly greater improvement in LVEF than the GDMT group (LSM difference 3.31% [95% CI 0.58–6.04]; P = 0.018) (Table 2). Regarding quality of life, both groups demonstrated significant improvements from baseline to the end of the follow-up (Table S4). From the 3-month follow-up onward, the vericiguat group’s quality of life scores were significantly better than those of the GDMT group, with this advantage persisting through the end of the follow-up (Table 3; Fig. 4). Furthermore, although there was no significant difference in the proportion of patients who achieved the minimal clinically important difference (MCID) on the KCCQ-QoL between the two groups, the vericiguat group showed a greater trend toward improvement in quality of life (Table S5) [19]. In the exploratory analysis on ACS-related endpoints, no significant differences in new-onset AMI, revascularization procedures, and hospitalization for angina were observed between the two groups during a 12-month follow-up (Table S6).
Fig. 3.
Trends of LVEF and NT-proBNP changes throughout the follow-up period before PSM. a The changes of LVEF in the vericiguat group and the GDMT group during the follow-up and the differences between the two groups; b The changes of log-transformed NT-proBNP in the vericiguat group and the GDMT group during the follow-up and the differences between the two groups. The two dashed lines represent the baseline LVEF and NT-proBNP values, respectively. Solid circles represent the estimated means, and the error bars indicate the 95% confidence intervals of the means
Table 2.
Covariance analysis for secondary outcomes (LVEF and NT-proBNP) from baseline to 12-month follow-up before PSM
| Parameters | Time, | GDMT (N = 74) |
Vericiguat (N = 75) |
Difference vs. GDMT | P value |
|---|---|---|---|---|---|
| month | LS Mean (95%CI) † | LS Mean (95%CI) † | (95%CI) ‡ | ||
|
LVEF (%) |
1 |
40.65 (37.61, 43.70) |
40.77 (38.07, 43.48) |
0.12 (-2.55, 2.79) |
0.929 |
| 3 |
42.20 (39.15, 45.25) |
43.06 (40.37, 45.74) |
0.86 (-1.81, 3.53) |
0.527 | |
| 6 |
42.85 (39.79, 45.90) |
45.03 (42.35, 47.10) |
2.18 (-0.49, 4.85) |
0.108 | |
| 12 |
43.32 (40.26, 46.39) |
46.63 (43.91, 49.36) |
3.31 (0.58, 6.04) |
0.018 | |
|
Lg (NT-proBNP) (pg/ml) |
1 |
3.23 (3.05, 3.40) |
2.99 (2.84, 3.15) |
-0.24 (-0.40, -0.07) |
0.004 |
| 3 |
3.16 (2.99, 3.34) |
2.90 (2.75, 3.06) |
-0.26 (-0.42, -0.10) |
0.001 | |
| 6 |
3.08 (2.90, 3.25) |
2.72 (2.56, 2.87) |
-0.36 (-0.52, -0.20) |
< 0.001 | |
| 12 |
2.94 (2.76, 3.11) |
2.59 (2.43, 2.74) |
-0.35 (-0.52, -0.18) |
< 0.001 |
Adjusting for age, NYHA class and baseline NT-proBNP
PSM propensity score matching, GDMT guideline-directed medical therapy, LVEF left ventricular ejection fraction, NT-proBNP N-terminal B-type natriuretic peptide
†Based on a linear mixed-effects model; LS indicates least squares
‡Difference in least square means
Table 3.
Comparisons of intergroup differences of KCCQ-QoL before PSM and after PSM
| Parameters | Time, month | GDMT | Vericiguat | P value |
|---|---|---|---|---|
| Difference | Difference | |||
| Before PSM | 1 |
0.00 (0, 13.3) |
6.67 (0, 13.3) |
0.042 |
| 3 |
6.67 (0, 13.3) |
13.33 (0, 20.0) |
0.008 | |
| 6 |
6.67 (0, 13.3) |
13.33 (0, 20.0) |
0.012 | |
| 12 |
6.67 (0, 13.3) |
13.33 (6.67, 20.0) |
0.007 | |
| After PSM | 1 |
0.00 (0, 13.3) |
6.67 (0, 13.3) |
0.066 |
| 3 |
6.67 (0, 13.3) |
0 (0, 18.3) |
0.053 | |
| 6 |
6.67 (5.0, 13.3) |
13.33 (0, 20.0) |
0.047 | |
| 12 |
6.67 (0, 13.3) |
13.33 (6.67, 20.0) |
0.021 |
PSM propensity score matching
Fig. 4.
Changes of KCCQ-QoL scores during the follow-up in the vericiguat group and the GDMT group before and after PSM. Box plots for KCCQ-QoL. Center bars are medians; box tops and bottoms are interquartile ranges; and whiskers, fifth and 95th percentiles. Solid circles inside boxes are means; open circles are data that fall outside of the fifth and 95th percentiles
PSM results
After 1:1 PSM, there were 61 patients each in the vericiguat group and the GDMT group. After matching, the standardized mean differences (SMD) for all covariates were < 0.1 except for the baseline NT-proBNP variable, indicating a good balance between the two groups (Table S7 and Fig. S1). Univariable analysis was performed on the matched patient data, and the results from the multivariable Cox regression aligned with those observed before PSM (Table S8 and Fig. S2). In the mixed-effects model analysis of secondary endpoints including LVEF and NT-proBNP, several factors identified by univariable analysis as associated with the primary outcome—such as baseline age, NT-proBNP, and NYHA class —were adjusted, and the between-group comparison results remained consistent with the primary analysis (Table S9, S10). Regarding quality of life, although the differences between the two groups lessened after matching, the vericiguat group demonstrated significantly better KCCQ-QoL score than the GDMT group at both 6-month (P = 0.047) and 12-month (P = 0.021) (Table 3).
Safety analysis
The clinical adverse events of interest included symptomatic hypotension, anemia, syncope and bleeding. As shown in Table 4, at the 12-month follow-up, 6 patients in the vericiguat group experienced symptomatic hypotension, compared to 5 patients in the GDMT group (P > 0.999). 1 patient in the vericiguat group reported syncope, while no syncope events occurred in the GDMT group (P > 0.999). The change in systolic blood pressure from baseline at month 12 (mean difference ± SD) was − 9.51 ± 19.82 mmHg in the vericiguat group and − 3.38 ± 21.08 mmHg in the GDMT group (Table S11). Both groups showed a decrease in systolic blood pressure compared to baseline with a greater decrease observed in the vericiguat group; however, the difference between groups was not statistically significant (P = 0.074). Bleeding events were reported in 30.67% of the vericiguat group and 27.03% of the GDMT group, with no statistically significant difference between the two groups (P = 0.718).
Table 4.
Adverse events of clinical interest: symptomatic hypotension, anemia, syncope and bleeding
| Parameters | GDMT(N = 74) | Vericiguat(N = 75) | Difference in % vs. GDMT | |||
|---|---|---|---|---|---|---|
| N | (%) | N | (%) | Estimate (95%CI) * |
P value | |
| Symptomatic hypotension | 5 | 6.76% | 6 | 8.00% |
1.24% (-7.15, 9.63) |
> 0.999 |
| Anemia | 5 | 6.76% | 8 | 10.67% |
3.91% (-5.12, 12.94) |
0.562 |
| Syncope | 0 | 0.00% | 1 | 1.33% |
1.33% (-1.26, 3.93) |
> 0.999 |
| Bleeding | 20 | 27.03% | 23 | 30.67% |
3.64% (-10.9, 18.8) |
0.718 |
CI indicates confidence interval
*Based on the Miettinen & Nurminen method
Compared to the GDMT group, more patients developed anemia in the vericiguat group (10.67% vs. 6.76%), but this difference was not statistically significant between two groups. Baseline mean (± SD) hemoglobin levels were 133.26 ± 20.26 g/L in the vericiguat group and 133.28 ± 23.48 g/L in the GDMT group. Both groups showed an initial decline followed by an increase in hemoglobin levels during follow-up, with no significant differences between them. Lastly, no serious adverse events were reported in either group (Table S11).
Discussion
Our study fills an important gap in the scientific evidence regarding the impact of vericiguat combined with GDMT in treating ACS patients with concurrent HFrEF in the real world. Based on our analysis, we yielded three key findings: (i) vericiguat combined with GDMT significantly reduces the composite incidence of CVD or HHF compared to conventional GDMT alone. (ii) vericiguat treatment can significantly lower NT-proBNP levels early on, while demonstrating superior cardiac function improvement after 12 months of therapy superior to GDMT alone. (iii) compared to GDMT treatment, vericiguat shows statistically significant benefits on patients’ quality of life.
These findings are consistent with the general conclusions of the VICTORIA trial. Notably, the VICTORIA trial enrolled a worse HFrEF group, whereas this study was designed to preliminarily explore vericiguat’s benefits in a higher-risk, more challenging group of ACS patients with HFrEF. In comparison, such patients face substantially greater short-term risks of cardiovascular events, higher mortality rate, and worse overall prognosis [20]. Furthermore, although the VICTORIA trial confirmed that vericiguat significantly reduced the risk of the primary composite endpoint (CVD or first HHF) in HFrEF patients (HR 0.90 [95% CI 0.82–0.98]; P = 0.02), the use of ARNI and SGLT2 inhibitors was relatively limited in the VICTORIA trial [13, 21]. The latest heart failure treatment guidelines have incorporated vericiguat into standard HFrEF therapy, forming a ‘five-drug combination’ to achieve multi-pathway combined treatment and maximize improvement in patient prognosis [22–24]. However, the evidence from clinical research exploring the effects of vericiguat on heart failure patients undergoing GDMT treatment is insufficient. A pooled analysis shows that under the background of GDMT treatment, vericiguat can provide additional benefits in heart failure hospitalization and cardiovascular death for a broad range of HFrEF patients [25]. Consistently, this study demonstrates that adding vericiguat to quadruple GDMT regimen can still reduce the incidence of clinical events. This suggests that vericiguat provides consistent additional therapeutic value in patients with different treatment backgrounds and risk profiles. Although the proportion of patients with both ACS and HF is less than 10%, these individuals face significantly higher risks of recurrent HHF and mortality compared to those with isolated ACS [26, 27]. Therefore, the observed risk reduction in this specific population holds greater clinical significance, offering a meaningful contribution to contemporary precision medicine approaches for patients with ACS complicated by HFrEF.
In ACS patients, endothelial dysfunction, oxidative stress, and sGC inactivation lead to dysregulation of the NO-sGC-cGMP pathway, which in turn accelerates myocardial injury, vascular dysfunction, and the progression of heart failure [28]. In animal models, nitrites and organic nitrates drugs have demonstrated an ability to alleviate myocardial ischemia-reperfusion injury, but results from clinical trials have been disappointing. Two human trials using nitrites as a preconditioning agent reported negative results for both primary and all secondary endpoints [29–31]. Organic nitrates drugs, such as nitroglycerin and isosorbide mononitrate, also exert their effects by modulating the NO-sGC-cGMP pathway. However, they are primarily used to symptoms relief, and currently there is no clinical evidence demonstrating that they can improve prognosis [32, 33]. In light of these theoretical and practical challenges, vericiguat represents a mechanistically distinct therapeutic approach. Nevertheless, further studies incorporating mechanistic biomarkers are required to clarify the biological pathways underlying these clinical observations.
A systematic review and network meta-analysis on the efficacy of modern HFrEF therapies highlighted vericiguat as a promising option across various patient subgroups [34]. Preclinical research has demonstrated that vericiguat can significantly enhance coronary blood flow and myocardial microcirculation, as well as inhibit myocardial hypertrophy, fibrosis, and remodeling [35–37]. The present study represents a further exploratory investigation of the clinical use of vericiguat based on previous evidence. Although these findings provide biological plausibility, whether such mechanisms directly contributed to the clinical benefits observed in our study population cannot be determined. The observed early reduction in NT-proBNP levels and the longer-term improvement in ventricular remodeling indices may be consistent with these preclinical observations, but causal inferences cannot be drawn from this observational analysis. Ventricular remodeling is a gradual reversal process. In this study, vericiguat showed a unique advantage in ventricular remodeling indicators after one year of continuous treatment. This aligns with the drug’s chronic anti-fibrotic and anti-remodeling mechanisms, distinguishing it from the potential development of tolerance associated with organic nitrate drugs [38].
The main strength of this study lies in being the first to evaluate the effectiveness of vericiguat in a high-risk, understudied population of ACS patients with heart failure in a ‘real-world’ setting. To maximally control inherent confounding bias in observational study, we employed not only multivariable Cox regression models but also sensitivity analyses using PSM. The strong consistency of results after matching significantly strengthened the robustness of our primary conclusions and mitigated the impact of baseline characteristic imbalances on outcomes.
Limitations
Several limitations of this study should be considered. Firstly, although statistical methods were used for propensity score matching and adjustment, confounding factors cannot be completely ruled out. The specific details of important drug use, the patient’s subjective wishes, and the clinical judgment of the physician may have introduced additional confounding. Secondly, this was a single-center prospective study. Given the small sample size and the limited number of outcome events, subgroup analyses or sensitivity analyses stratified by ACS subtype may be limited. Moreover, when handling the known confounding factors that may affect the outcomes, although we have cautiously selected the variables included in the model analysis, there may still be a risk of overfitting, especially considering the limited number of outcome events. Therefore, the conclusions are primarily hypothesis-generating and need to be further validated in future prospective, multicenter, large-scale studies. Thirdly, a considerable proportion of patients with unstable angina pectoris in this study suggested the possibility of selection bias. Finally, the 12-month follow-up period may still be insufficient to comprehensively assess the long-term changes in ventricular remodeling and the long-term safety of the medication.
Conclusion
This real-world study indicates that in ACS patients and reduced LVEF receiving contemporary standard GDMT, adding vericiguat can significantly reduce the composite risk of cardiovascular death and heart failure hospitalization. This provides preliminary and hypothesis-generating evidence for the use of vericiguat in this specific high-risk population, endorsing it as an important option for optimizing heart failure management and achieving multi-pathway combination therapy. Given the nonrandomized study design, interpretation of the results should be cautious and requires further investigation in randomized controlled trials. Future trials with longer treatment and follow-up durations are needed to evaluate the sustained effects of vericiguat on left ventricular remodeling and long-term prognosis.
Supplementary Information
Supplementary Material 1: Table S1. Systolic blood pressure criteria for study treatment dose modification. Table S2. The number of cardiac death and hospitalization for heart failure in both groups. Table S3. Univariable and multivariable analysis before PSM. Table S4. Changes in the absolute values of secondary outcomes from baseline to 12-month follow-up in the vericiguat group compared to the GDMT group before PSM. Table S5. The proportion of patients achieving a MCID between the two groups. Table S6. Exploratory analysis on ACS-related endpoints during a 12-month follow-up between the two groups. Table S7. SMD of covariates before and after PSM. Table S8. Univariable and multivariable analysis after PSM. Table S9. Covariance analysis for secondary outcomes（LVEF and NT-proBNP）from baseline to 12-month follow-up after PSM. Table S10. Changes in the absolute values of secondary outcomes from baseline to 12-month follow-up in the vericiguat group compared to the GDMT group after PSM. Table S11. Safety analysis. Figure S1. Love Plot. Love plot displays covariate balance before and after PSM. Figure S2. Kaplan-Meier curve for the primary endpoint after PSM.HR calculated by multivariable Cox model by adjusting for baseline age, NYHA class, NT-proBNP, and LVEF. Supplementary Methods. Supplementary methods for definition of GDMT use in this study.
Acknowledgements
We thank all the investigators and patients who participated in this project from the First Affiliated Hospital with Nanjing Medical University. We gratefully thank Professor Yang Zhao from Nanjing Medical University for his contribution to statistical guidance.
Abbreviations
- ACS
Acute coronary syndrome
- AF
Atrial fibrillation
- ALT
Alanine aminotransferase
- AMI
Acute myocardial infarction
- ARNI
Angiotensin receptor-neprilysin inhibitor
- CAD
Coronary artery disease
- CCS
Chronic coronary syndrome
- cGMP
Cyclic guanosine monophosphate
- CVD
Cardiovascular death
- CI
Confidence interval
- DBP
Diastolic blood pressure
- eGFR
Estimated glomerular filtration rate
- GDMT
Guideline-directed medical therapy
- Hb
Hemoglobin
- HF
Heart failure
- HFrEF
Heart failure with reduced ejection fraction
- HHF
Hospitalization for heart failure
- HR
Hazard ratio
- IQR
Interquartile range
- KCCQ
Kansas City Cardiomyopathy Questionnaire
- LSM
Least squares mean
- LVEF
Left ventricular ejection fraction
- LDL-C
Low density lipoprotein cholesterol
- MCID
Minimally important clinical change
- MRAs
Mineralocorticoid receptor antagonists
- NSTEMI
Non-ST-elevation myocardial infarction
- NT-proBNP
N-terminal pro-B-type natriuretic peptide
- NYHA
New York Heart Association
- PCI
Percutaneous coronary intervention
- PSM
Propensity score matching
- QoL
Quality of life
- SBP
Systolic blood pressure
- SGLT2i
Sodium-glucose co-transporter 2 inhibitor
- sGC
Soluble guanylate cyclase
- STEMI
ST-elevation myocardial infarction
- UA
Unstable angina
Authors’ contributions
LSW and SBW conceived and designed the study, made key revision and approved the manuscript submission. XYS, LFG, TTY finished the data analysis. YD, HW, QMW participated in the data curation. XYS, ARZ wrote the manuscript. All authors read and approved the final manuscript.
Funding
This work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No. 2025ZD0548202), the Natural Science Youth Foundation of Jiangsu Province of China (No. BK20241110), the China Postdoctoral Science Foundation (No. 2025M772273), and the Jiangsu Funding Program for Excellent Postdoctoral Talent (Sibo Wang).
Data availability
The original contributions presented in the study are included in the article and supplementary material, further inquiries can be directed to the corresponding author(s).
Declarations
Ethics approval and consent to participate
The studies involving human participants were reviewed and approved by the Ethics Committee of the First Affiliated Hospital with Nanjing Medical University (Approval No. 2023-SR-731).The participants provided their written informed consent to participate in this study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Sibo Wang, Email: sibowang@njmu.edu.cn.
Liansheng Wang, Email: drlswang@njmu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 1: Table S1. Systolic blood pressure criteria for study treatment dose modification. Table S2. The number of cardiac death and hospitalization for heart failure in both groups. Table S3. Univariable and multivariable analysis before PSM. Table S4. Changes in the absolute values of secondary outcomes from baseline to 12-month follow-up in the vericiguat group compared to the GDMT group before PSM. Table S5. The proportion of patients achieving a MCID between the two groups. Table S6. Exploratory analysis on ACS-related endpoints during a 12-month follow-up between the two groups. Table S7. SMD of covariates before and after PSM. Table S8. Univariable and multivariable analysis after PSM. Table S9. Covariance analysis for secondary outcomes（LVEF and NT-proBNP）from baseline to 12-month follow-up after PSM. Table S10. Changes in the absolute values of secondary outcomes from baseline to 12-month follow-up in the vericiguat group compared to the GDMT group after PSM. Table S11. Safety analysis. Figure S1. Love Plot. Love plot displays covariate balance before and after PSM. Figure S2. Kaplan-Meier curve for the primary endpoint after PSM.HR calculated by multivariable Cox model by adjusting for baseline age, NYHA class, NT-proBNP, and LVEF. Supplementary Methods. Supplementary methods for definition of GDMT use in this study.
Data Availability Statement
The original contributions presented in the study are included in the article and supplementary material, further inquiries can be directed to the corresponding author(s).