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. 2025 Jun 15;17(6):4237–4246. doi: 10.62347/LQLD9274

Association of heparin-binding protein with risk of in-hospital heart failure in patients with acute coronary syndrome

Xiao Liang 1, Qingxian Tu 1, Jie Zhang 1, Min Xu 1, Zhenglong Wang 1
PMCID: PMC12261142  PMID: 40672631

Abstract

Objectives: To investigate the relationship between heparin-binding protein (HBP) levels and the risk of involving heart failure occurring during hospitalization in patients with acute coronary syndrome (ACS). Methods: This single-center retrospective study included 274 patients with ACS hospitalized at the Third Affiliated Hospital of Zunyi Medical University between June and December 2023. The primary outcome was the occurrence of in-hospital heart failure (HF). Multivariable logistic regression and restricted cubic spline (RCS) models were used to assess the relationship between HBP and HF. Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of HBP for HF events. Results: During hospitalization, 56 patients (20.4%) developed HF. Patients with HF patients had higher HBP levels (p < 0.01). HBP was significantly associated with in-hospital HF risk (Model 3 OR = 4.232, P < 0.001). ROC analysis showed that HBP had a predictive value for HF events (AUC = 0.696, sensitivity = 55.36, specificity = 76.61, P < 0.001). The RCS model indicated a nonlinear dose-response relationship between HBP levels and in-hospital HF (P for nonlinearity = 0.007). Conclusion: HBP levels are associated with an increased risk of in-hospital HF in patients with ACS and serve as a superior predictor compared to traditional parameters and inflammatory markers.

Keywords: Heparin-binding protein, acute myocardial infarction, in-hospital heart failure, restricted cubic spline curve

Introduction

Acute coronary syndrome (ACS) is a clinical emergency that can lead to sudden cardiac death. According to the 2023 statistics by the American Heart Association, approximately 850,000 new and recurrent cases of acute myocardial infarction (AMI) occur yearly [1]. A recent systematic review and meta-analysis reported a global prevalence of AMI of 3.8% in individuals aged 60 years or less and 9.5% in those older than this age [2]. Despite advancements in percutaneous coronary intervention (PCI) techniques, many patients with ACS still experience varying degrees of heart failure (HF) symptoms during hospitalization [3]. Previous studies have indicated that up to 38% of patients with ACS experience HF events, representing nearly 1 million cases annually [4]. Data from Asian countries indicate that the incidence of HF following AMI is the highest during the initial hospitalization period and persists over the subsequent 1 to 6 years post-AMI [5]. Mechanisms leading to HF during hospitalization for myocardial infarction include myocardial damage from necrosis, myocardial stunning, and mechanical complications. Within the first 30 min of ischemia, myocardial cells swell and undergo structural changes, which lead to progressive cell death after 3 h. Reperfusion injury further damages myocardial cells [6]. Additionally, advanced age, hypertension, and diabetes increase the likelihood of developing HF following ACS (ACS-HF) [7]. Patients with ACS-HF have poor prognosis and higher hospital re-admission rates, which increases the healthcare burden [8]. It is thus essential to determine a reliable and straightforward marker to predict the likelihood of HF during hospitalization in patients with ACS and improve their short-term prognosis during their hospital stay.

ACS-HF is primarily caused by persistent coronary microcirculation disorders, which are important predictors of adverse cardiovascular events, including HF readmission and all-cause mortality [9,10]. The incidence of microcirculation disorders in individuals with myocardial ischemia has been reported to be approximately 40-64% [11]. The main mechanisms underlying myocardial ischemia involve endothelial cell (EC) damage, intravascular microthrombosis formation, and coronary microvascular endothelial inflammation [12,13]. Heparin-binding protein (HBP), also known as CAP37/azurocidin, is a granule protein located in neutrophils and among the initial inflammatory mediators released by neutrophils in response to infection [14-16]. HBP can bind to EC surface glycosaminoglycans, activate the PKC and Rho-kinase pathways, induce EC cytoskeletal rearrangement, disrupt the vascular endothelial barrier, increase vascular permeability, and promote the amplification of the inflammatory cascade, which contribute to tissue damage and microcirculation disorders [17,18]. Previous studies have demonstrated that HBP levels above 11.46 ng/mL are a sensitive indicator for predicting AMI and are positively correlated with the Thrombolysis in Myocardial Infarction (TIMI) risk score [19]. Additionally, HBP has been confirmed as an independent risk factor in studies related to myocardial injury-associated cardiogenic shock (MIRCS) [20]. These studies indicated that HBP plays a crucial role in non-infectious inflammation, such as myocardial infarction. However, research exploring the relationship between HBP and the risk of in-hospital HF in patients with ACS remains limited.

In this study, we aimed at examining the relationship between HBP levels and the risk of developing HF during hospitalization in patients with ACS and to explore whether this relationship follows a dose-response pattern.

Methods

Study population

This single-center, retrospective observational study included 274 patients initially diagnosed with ACS and hospitalized in the Department of Cardiology at the Third Affiliated Hospital of Zunyi Medical University from June to December 2023. The Ethics Committee of the Third Affiliated Hospital of Zunyi Medical University approved the research (approval no. 2024-1-137), which adhered to the ethical requirements of the Declaration of Helsinki and its subsequent amendments. No interventions were performed with respect to the patients, and informed consent was waived due to the observational nature of the study. Inclusion criteria comprised patients diagnosed with ACS upon admission, including NSTEMI, STEMI, and unstable angina. The exclusion criteria were as follows: (1) patients with acute exacerbation of chronic HF; (2) patients who recently experienced acute infections; (3) patients with non-cardiac chest pain; (4) patients with severe valvular heart disease; (5) patients with malignant tumors; (6) patients with severe liver or spleen dysfunction; (7) pregnant women; and (8) cases with incomplete general clinical data or relevant examination data.

Data acquisition, handling, and outcome specification

Clinical features, historical medical data, and lab test outcomes were collected from the electronic medical records system upon the patients’ admission and throughout their hospitalization. Upon admission, laboratory tests primarily comprised measurements of HBP, N-terminal pro-B-type natriuretic peptide (NT-proBNP), troponin levels, renal function, ion concentrations in serum, and cardiac enzymes. HBP detection was performed using an immunofluorescence dry quantitative method with an immunoassay analyzer (Jet-iStar 3000, Zhonghan Shengtai Biotechnology Co., Ltd., Shandong, China). Venous blood samples were taken after a minimum of 12 h of fasting, measuring indicators such as complete blood count, lipid profile, liver function, and C-reactive protein (CRP). Inflammatory markers, including monocyte-to-lymphocyte ratio (MHR), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and systemic immune inflammation index (SII), were calculated based on these measurements. HBP was measured immediately after patient admission. A NT-proBNP value ≥ 300 pg/mL was defined as indicative of heart failure during hospitalization [21,22].

Statistical analysis

Statistical analysis was conducted with SPSS version 22.0, MedCalc version 15.0, and R version 4.2.2. Categorical variables are presented as frequencies or percentages, while continuous variables are presented as the mean ± standard deviation for normally distributed data and as quartiles (median (25th and 75th percentiles) for non-normally distributed data. For normally distributed, continuous variables, t-tests or analysis of variance were used for comparisons across groups, while non-parametric tests were employed when normality was not met. For comparisons involving categorical data, Fisher’s exact test or the Chi-square test was used. Receiver operating characteristic (ROC) curves were employed to determine the diagnostic accuracy of HBP. Multivariable logistic regression was used to evaluate the relationship between HBP and outcome events. Propensity score matching (PSM) was performed at a 1:2 ratio, with sex, age, BMI, SBP, and LDL-C included as matching variables, to further validate the robustness of the association between HBP and outcome events. Additionally, restricted cubic splines (RCS) were employed to assess the dose-response relationship between HBP levels and in-hospital HF. A p-value less than 0.05 was considered statistically significant.

Results

Sample characteristics

This retrospective study involved 274 patients with consecutive ACS, among whom 56 (20.4%) developed HF during the inpatient period. The mean age of patients in the HF group (69.679±10.357 years) was higher than that in the non-HF group (60.766±12.575 years). In the HF group, HBP levels were substantially greater than those in the non-HF group [24.905 (12.925, 45.125) vs. 12.555 (7.005, 23.278)]. Additionally, the HF group exhibited higher CTNT, CK-MB, CRP, NLR, and serum creatinine (Cre) levels, as well as higher neutrophil counts, than the non-HF group, with statistically significant differences (P < 0.01). Detailed information is presented in Table 1.

Table 1.

Baseline data of each group

Variables Total Heart failure group Non-heart failure group P
N = 274 N = 56 N = 218
Age (years) 62.588±12.660 69.679±10.357 60.766±12.575 < 0.01
Height (cm) 167.00 (160.00, 173.00) 165.00 (160.00, 173.00) 168.00 (160.00, 173.00) 0.637
Weight (kg) 70.00 (62.00, 80.00) 69.50 (61.25, 75.00) 70.00 (62.00, 80.00) 0.482
HR (BPM) 78.00 (66.00, 86.00) 80.00 (65.00, 96.00) 78.00 (66.00, 84.00) 0.343
BMI (kg/m2) 24.977 (22.818, 27.548) 24.655 (22.770, 27.497) 25.007 (22.818, 27.548) 0.606
Sex 0.850
    Male/[n, (%)] 145 (52.92) 29 (51.79) 116 (53.21)
    Female/[n, (%)] 129 (47.08) 27 (48.21) 102 (46.79)
Past history
    Hypertension/[n, (%)] 162 (59.12) 41 (73.21) 121 (55.50) 0.016
    Diabetes/[n, (%)] 98 (35.77) 28 (46.48) 70 (32.35) 0.013
Heart Failure Indicators
    NYHA III or IV/[n, (%)] 18 (6.57) 18 (25.35) 1 (0.49) < 0.01
    WMA 51 (18.61) 31 (43.66) 21 (10.29) < 0.01
Biochemical indicators
    HBP (ng/mL) 14.535 (7.830, 27.030) 24.905 (12.925, 45.125) 12.555 (7.005, 23.278) < 0.01
    NT-proBNP 108.250 (99.025, 128.875) 1013.450 (678.32, 45.125) 105.050 (96.700, 109.900) < 0.01
    cTnT (ug/L) 0.004 (0.002, 0.023) 0.027 (0.008, 1.367) 0.003 (0.002, 0.007) < 0.01
    CK-MB (ug/L) 0.900 (0.600, 1.600) 1.650 (1.125, 5.600) 0.800 (0.600, 1.325) < 0.01
    CRP (mg/L) 1.470 (0.618, 3.933) 3.965 (1.435, 12.740) 1.270 (0.570, 2.713) < 0.01
SII 398.799 (264.162, 578.068) 540.312 (340.222, 1021.704) 379.657 (249.897, 529.681) < 0.01
NLR 1.918 (1.430, 2.506) 2.815 (1.933, 4.309) 1.784 (1.379, 2.308) < 0.01
Neutrophils (109/L) 3.325 (2.615, 4.488) 4.245 (3.383, 5.863) 3.130 (2.578, 4.153) < 0.01
Lymphocytes (109/L) 1.785 (1.380, 2.233) 1.520 (1.050, 1.963) 1.830 (1.448, 2.278) 0.01
Monocyte (109/L) 0.380 (0.310, 0.490) 0.490 (0.375, 0.618) 0.360 (0.308, 0.440) < 0.01
PLT (109/L) 210.000 (176.000, 247.000) 199.500 (165.500, 245.750) 212.500 (178.000, 247.000) 0.424
TC (mmol/L) 4.370 (3.590, 5.225) 4.105 (3.330, 5.398) 4.495 (3.740, 5.188) 0.424
TG (mmol/L) 1.400 (0.978, 1.933) 1.270 (0.950, 1.750) 1.410 (0.978, 1.983) 0.351
HDL-C (mmol/L) 1.015 (0.870, 1.220) 1.000 (0.883, 1.208) 1.020 (0.868, 1.220) 0.883
LDL-C (mmol/L) 2.590 (2.018, 3.293) 2.520 (1.943, 3.333) 2.650 (2.055, 3.293) 0.585
Cre (ml/min) 68.890 (58.393, 80.723) 77.795 (63.525, 99.405) 67.265 (56.858, 77.975) < 0.01
ALT (U/L) 19.655 (14.213, 29.645) 22.920 (15.800, 31.998) 18.730 (13.708, 29.143) 0.041
AST (U/L) 20.020 (16.445, 26.033) 27.320 (17.015, 41.495) 19.435 (16.103, 24.003) < 0.01
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Abbreviations: HR, heart rate; BMI, body mass index; WMA, wall motion abnormal; HFpEF, heart failure with preserved ejection fraction; HFmrEF, heart failure with mid-range-also called mildly reduced EF; HFrEF, heart failure with reduced EF; HBP, heparin-binding protein; NT-proBNP, N-terminal pro-B-type natriuretic peptide; CTNT, cardiac troponin T; CK-MB, creatine kinase-MB; CRP, C-reactive protein; SII, systemic immune inflammation index; NLR, neutrophil-to-lymphocyte ratio; PLT, platelet; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; Cre, creatinine; EF%, ejection fraction; ALT, alamine aminotransferase; AST, aspartate aminotransferase.

Association between HBP and the risk of heart failure during hospitalization in ACS patients

According to ROC curve analysis, the ideal cutoff value for HBP was 23.76. Accordingly, patients were categorized into two groups: a high HBP group (> 23.76) and a low HBP group (≤ 23.76). Logistic regression analysis was conducted to identify factors influencing in-hospital HF in patients with ACS. Univariate logistic regression analysis indicated that age, HBP, NLR, SII, CTNT, and SCR were associated with the risk of in-hospital HF in patients with ACS (Figure 1). To adjust for potential confounders and further investigate the association between HBP and the risk of in-hospital heart failure in ACS patients, multivariable logistic regression models were employed. Three models were established: Model 1 without adjustment for confounding factors, Model 2 adjusted for sex and age, and Model 3 adjusted based on Model 2 to account for hypertension, diabetes, CTNT, EF%, and the use of diuretics. Prior to performing multivariate logistic regression analysis, we performed a collinearity analysis of variables included in the final model (Model 3), which showed that all variables had a tolerance > 0.2 and variance inflation factors < 10, indicating no collinearity between the included variables, as detailed in Supplementary Table 1. The results of the multivariable logistic regression analysis demonstrated a significant association between elevated HBP levels and an increased risk of in-hospital HF in patients with ACS [Model 3 OR = 4.232 (1.718-10.429), P < 0.001]. We additionally conducted a sensitivity analysis based on PSM to further clarify whether the association between HBP and heart failure remains stable. The results indicated that after matching participants from both groups for sex, age, BMI, SBP, and LDL-C levels, HBP remained positively associated with heart failure (PSM: Model 3 = 4.161, 95% CI: 1.581-10.952, P = 0.004), further confirming the robustness of the association between HBP and heart failure (Figure 2). Finally, we conducted subgroup analyses and interaction tests based on patient sex, age, and comorbidities, as shown in Figure 3.

Figure 1.

Figure 1

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Univariate logistic regression analysis. Abbreviations: OR, odds ratio; CI, confidence interval; EF%, ejection fraction; HBP, heparin-binding protein; CTNT, cardiac troponin T; CK-MB, creatine kinase-MB; CRP, C-reactive protein; SII, systemic immune inflammation index; NLR, neutrophil-to-lymphocyte ratio; CR, creatinine; ALT, alamine aminotransferase; AST, aspartate aminotransferase.

Figure 2.

Figure 2

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The associations between HBP and in-hospital heart failure in ACS Patients. Note: Model 1 was unadjusted. Model 2 included adjustments for sex and age. Model 3 further adjusted for sex, age, hypertension, diabetes, diuretics, CTNT, EF%. Abbreviations: ACS, acute coronary syndrome; PSM, propensity score matching; OR, odds ratio; CI, confidence interval; CTNT, cardiac troponin T; EF%, ejection fraction; HBP, heparin-binding protein.

Figure 3.

Figure 3

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Subgroup analysis and interaction test. Abbreviations: DM, diabetes mellitus.

Predictive value of HBP for the risk of in-hospital HF in patients with ACS

We used ROC curves to evaluate the predictive performance of HBP and other inflammatory markers for the accuracy of heart failure occurrence. The results indicate that HBP had the highest predictive ability for heart failure occurrence in patients with ACS [area under the curve (AUC) = 0.696, 95% CI: 0.637-0.749, P < 0.001]. The optimal cutoff value for HBP was 23.76, with a sensitivity of 55.36% and a specificity of 76.61%. The AUCs for other inflammatory markers such as SII, MLR, and PLR were 0.679, 0.649, and 0.605, respectively (Table 2). Subsequently, we combined HBP with CTNT or EF to evaluate whether this combination could improve predictive performance. The results showed that the AUC increased significantly when HBP was combined with CTNT or EF. Notably, the combination of HBP and EF achieved the highest accuracy in predicting in-hospital heart failure among ACS patients (AUC = 0.859, 95% CI: 0.812-0.898), as detailed in Figure 4.

Table 2.

ROC analysis for HBP and other inflammatory markers

Variable AUC SE 95% CI P
HBP 0.696 0.040 0.637-0.749 < 0.0001
SII 0.679 0.043 0.620-0.734 < 0.0001
MLR 0.649 0.044 0.589-0.705 0.001
PLR 0.605 0.046 0.545-0.663 0.015
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Abbreviations: AUC, area under curve; SE, standard error; CI, confidence interval; HBP, heparin-binding protein; SII, systemic immune inflammation index; MLR, monocyte-to-lymphocyte ratio; PLR, platelet to lymphocyte ratio.

Figure 4.

Figure 4

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ROC curve analysis for HBP, HBP+CTNT, and HBP+EF in predicting the risk of in-hospital heart failure among ACS patients. Abbreviations: ROC, receiver operating characteristic; HBP, heparin-binding protein; CTNT, cardiac troponin T; EF, ejection fraction.

Dose-response analysis of HBP levels and the incidence of in-hospital HF in patients with ACS

The dose-response relationship between HBP levels and the occurrence of in-hospital HF in patients with ACS was evaluated using RCS analysis. The results revealed a non-linear dose-response relationship between HBP levels and the risk of in-hospital HF in patients with ACS without adjusting for confounding factors (p for non-linearity = 0.007). This non-linear relationship remained significant even after accounting for potential confounding factors, as in Model 3 (p for non-linearity = 0.007; Figure 5).

Figure 5.

Figure 5

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The dose-response relationship between HBP levels and in-hospital heart failure in ACS patients. A: Crude model OR; B: Adjusted model. Abbreviations: OR, odds ratio; HBP, heparin-binding protein.

Discussion

In this retrospective study, we included 274 patients with ACS and investigated the relationship between HBP and the risk of in-hospital HF. Multivariable logistic regression analysis demonstrated a strong relationship between elevated HBP levels and the risk of in-hospital HF in patients with ACS [Model 3 OR = 4.232 (1.718-10.429), P < 0.001]. ROC curve analysis indicated that HBP can accurately predict the occurrence of in-hospital HF in patients with ACS. Additionally, we for the first time explored the dose-response relationship between HBP and the risk of in-hospital HF in patients with ACS using the RCS model. The results revealed a non-linear relationship, suggesting that as HBP levels increase, the risk of developing HF during hospitalization increases as well.

Post-ACS occurrence, the incidence of ACS-related HF ranges from 7 to 38% [3]. Some ACS patients with pre-existing HF may experience acute HF with relatively minor ischemic events, while others without prior heart dysfunction may develop transient or permanent HF following severe myocardial ischemia [23]. Persistent microvascular obstruction contributes significantly to ACS-related HF [24]. Survey data indicated that nearly half of the patients undergoing direct PCI exhibit microcirculatory dysfunction [25], which is associated with worse clinical outcomes. Current indices for assessing cardiac function during hospitalization in patients with ACS include clinical signs, cardiac troponins, echocardiography, B-type natriuretic peptide, and NT-proBNP [26-28]. However, due to limitations in hospitalization time, these indicators may not accurately reflect the cardiac function status of patients with ACS within a short period. ACS represents the extreme manifestation of a long-standing low-grade inflammatory response in coronary plaques [29]. In recent years, emerging inflammatory markers such as CRP, SII, NLR, PLR, MLR, and the systemic inflammation response index have been considered independent predictors of ACS severity [30-33]. However, these markers primarily focus on the relationship with the severity of vascular lesions and less on the degree of cardiac dysfunction following acute coronary events. Moreover, patients with ACS-related HF have significantly higher mortality rates than those without HF [34]. Therefore, identifying a timely and accurate method for assessing the likelihood of post-ACS HF is crucial, as it is essential for clinical treatment and improving prognosis, ultimately serving as a foundation for ACS management.

HBP, secreted by neutrophils, is a multifunctional protein composed of α-helices and β-sheets. It features cationic regions formed by arginine and lysine residues, along with multiple binding sites. These cationic regions interact with anionic molecules during intercellular communication, enhancing cell adhesion and signal transduction [35]. Studies have shown that HBP significantly reduces EC resistance and increases protein permeability. This effect is mediated through elevated intracellular Ca2+ levels, leading to cytoskeletal reorganization and stress fiber formation in ECs, ultimately increasing vascular permeability [36,37]. HBP is considered a reliable prognostic factor for evaluating the severity of inflammation and has shown better predictive value than CRP and PCT in various infectious diseases [38]. In non-infectious inflammations, such as myocardial infarction, Ipek et al. [19] found that a HBP threshold of > 11.46 ng/mL predicted myocardial infarction with a sensitivity of 74% and specificity of 58% (ROC AUC = 0.713; P = 0.018), and that HBP levels were significantly correlated with the TIMI score (P < 0.001). Pan et al. [20] evaluated patients who developed MIRCS after cardiac surgery and found that HBP had an AUC of 0.85 (95% CI: 0.81-0.89), with a threshold of 220 ng/mL, a sensitivity of 92%, and a specificity of 70% for predicting MIRCS. After adjusting for various confounding factors, HBP emerged as an independent risk factor for MIRCS (OR: 7.65, 95% CI: 4.86-12.06, P < 0.01). Additionally, HBP levels showed a positive correlation with CTNT (P < 0.01). Pesonen et al. [39] studied 30 patients undergoing aortic valve replacement and found that local HBP concentrations increased in the coronary sinus after reperfusion, which was accompanied by myocardial injury and neutrophil adhesion in coronary vessels. This suggests that HBP plays a specific role in myocardial reperfusion injury. Vincent et al. [40] found that mediators of the inflammatory response might be central to microcirculatory dysfunction in septic patients, likely due to the interaction between activated neutrophils and ECs. This aligns with our initial hypothesis that sustained inflammation triggers ACS, leading to HBP release from activated neutrophils and interaction with ECs, which ultimately causes microcirculatory dysfunction and promotes HF occurrence. Our study confirmed a significant correlation between HBP levels and the incidence of in-hospital HF in patients with ACS. We also found a correlation between HBP and wall motion abnormalities following myocardial infarction, supporting the conclusion that HBP can induce microcirculatory dysfunction. Nevertheless, additional studies are necessary to confirm these results. Although numerous studies have investigated the role of HBP in infectious inflammation, its function under non-infectious inflammatory conditions remains underexplored. Many cardiovascular diseases are associated with inflammation, and HBP, as a crucial regulator of the inflammatory response, holds significant clinical potential. Its application may offer novel therapeutic avenues for inflammation-related cardiovascular diseases. Our study indicates that monitoring HBP levels can help identify high-risk patients prone to developing HF following ACS. HBP can be utilized as an effective tool for risk stratification, enabling optimized pharmacological treatment and personalized cardiac rehabilitation strategies, which will ultimately improve patient outcomes [41-43].

This study had certain limitations: First, the limited sample size may have increased the risk of type II errors, indicating that certain associations may have existed but not reached statistical significance. Additionally, the limited sample size may have led to the sparse effect, which primarily affects subgroup analyses as the sample size within different subgroups is further reduced. Second, a smaller sample size limits the number of confounding variables that can be adjusted for in the multivariable regression model. Finally, a small sample size may affect the representativeness of the results, indicating that our conclusions require further validation in larger cohorts. Despite these limitations, we applied PSM, with sex, age, BMI, SBP, and LDL-C as matching variables, to enhance the robustness of our study findings and reduce potential biases. The rationale for choosing PSM was that it helps balance key confounding factors (age, sex, BMI, SBP, and LDL-C), thereby minimizing systematic differences between the two investigated groups. This allows for a more accurate estimation of the association between hypertension and outcomes while reducing potential biases. Moreover, PSM enhances the robustness of our findings by ensuring that observed associations are not driven solely by confounding variables. Notably, we measured HBP levels only at the early stage of patient admission, without monitoring dynamic changes during hospitalization. Therefore, we were unable to assess the variation in HBP during hospitalization or determine whether this trend affected the risk of HF. As an inflammatory biomarker, HBP may fluctuate with disease progression or resolution. However, due to the lack of dynamic monitoring, we cannot ascertain whether changes in this biomarker during hospitalization influence its value as a predictor of HF. Consequently, relying solely on baseline HBP levels to evaluate the association of HBP with HF risk may lead to either an overestimation or underestimation of this relationship. Future studies should consider employing multi-time-point dynamic monitoring strategies to comprehensively assess the impact of HBP level variations on HF occurrence. Despite these limitations, our study is the first to reveal an association between HBP and the risk of in-hospital HF in ACS patients, providing a new direction for future research to further explore its potential pathophysiological mechanisms.

Conclusions

Our findings revealed that elevated HBP levels (> 23.76) were positively correlated with a higher risk of in-hospital HF in patients with ACS. Furthermore, a nonlinear dose-response relationship was observed between HBP levels and the risk of in-hospital HF in these patients. HBP has also been associated with wall motion abnormalities in patients with ACS. Therefore, clinicians can use HBP to risk-stratify hospitalized patients with ACS, potentially reducing the risk of HF during hospitalization.

Acknowledgements

This study was funded by the Regional Fund Project of the National Natural Science Foundation of China (81760072), Guizhou Provincial Science and Technology Foundation Key Project (ZK [2024] Key 085), and 2023 Science and Technology Fund of Guizhou Provincial Health Commission (ZSKH HZ No. [2024] 43).

This study was exempted from informed consent under the approval of the Ethics Committee of the Third Affiliated Hospital of Zunyi Medical University.

Disclosure of conflict of interest

None.

Supporting Information

ajtr0017-4237-f6.pdf (154KB, pdf)

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