A New Perspective on NSTE-ACS Patients in the Emergency Department: Cardiac Risk Prediction with New Inflammation Markers

Introduction

Chest pain is the second most common concern among patients presenting to emergency departments (ED), accounting for 6% of total visits [1]. Acute coronary syndrome (ACS) is diagnosed in up to 25% of ED patients with chest pain, and NSTE-ACSs account for 60–70% of ACS-related hospitalizations [1–3].

ACSs are due to thrombus formation and occlusion of the affected vessel, usually as a result of local inflammatory and prothrombotic activity by plaque erosion or rupture, which stops coronary artery blood flow and hence oxygen supply to cardiac tissues. Ultimately, this causes an infarction in the myocardial muscle that the vessel supports [4–7]. Atherosclerosis, which can be defined as a non-contagious, low-grade, and chronic inflammatory condition, is also associated with age, lifestyle, environment, and resolution of acute inflammation [8].

Many inflammatory biomarkers, including the neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR), and systemic immune-inflammation index (SII), are increasingly used in research because they are cheap, simple, reproducible, non-invasive, and can be easily used in very large populations [9–13]. Recently, the pan-immuno-inflammation value (PIV) has been added to these inflammatory markers. PIV has been shown to be a useful marker for predicting poor clinical outcomes, primarily in cancer and inflammatory diseases [14,15]. Studies on cardiovascular diseases (CVD) found PIV was a good predictor of in-hospital and long-term clinical outcomes in elderly patients with ACS who underwent percutaneous coronary intervention (PCI) and adverse clinical outcomes in chronic heart failure (HF) [16–21].

However, to date, no study has specifically assessed the relationship between PIV and GRS. Additionally, the difficulties in using current risk assessment tools to predict in-hospital major cardiac events (MACE) in patients with NSTE-ACS and the need for tools to calculate them in methods such as GRS encourage clinicians to find new methods that are easier to use and less costly. The aim of this study was to evaluate the relationship between PIV and in-hospital MACE risk assessed by GRS in patients diagnosed with NSTE-ACS in AD and to test our hypothesis that this will contribute to risk stratification in these patients.

Material and Methods

LABORATORY:

Antecubital venous blood samples were collected from all patients at the time of initial presentation to the ED. Venous blood samples were assessed for basic blood variables (eg, comprehensive metabolic panel and complete blood count). Biochemistry blood samples were obtained the morning after admission to the coronary intensive care unit and at 8–10 AM after a 12-hour fast. All routine biochemical tests were performed on an automatic analyzer (COBAS^® c701, Roche Diagnostics, Mannheim, Germany). Blood samples were stored at 4°C and evaluated with an autoanalyzer (Sysmex K-1000 Hematology Analyzer, Cobe, Japan) within 30 minutes after sampling. NLR was calculated as the number of neutrophils divided by the number of lymphocytes. SII was calculated as platelet count multiplied by NLR. PIV was calculated as neutrophil count times platelet count times monocyte count divided by lymphocyte count (or monocyte count multiplied by SII).

TRANSTHORACIC ECHOCARDIOGRAPHY:

We conducted transthoracic echocardiography for all participants. All measurements were performed using a machine equipped with a 3.5 MHz transducer (Vivid 5, GE Medical Systems, Horten, Norway). We utilized two-dimensional echocardiographic measurements to evaluate left ventricular ejection fraction (LVEF) and valve pathologies. We evaluated valve pathologies with color Doppler in paraternal/apical dual-chamber/four-chamber views. We utilized the Simpson method to evaluate LVEF.

CORONARY ANGIOGRAPHY, PERCUTANEOUS CORONARY INTERVENTION, AND MEDICATIONS:

Coronary angiography was performed in all patients within 24 hours after hospitalization. Coronary artery intervention and perioperative management were performed according to the 2021 American College of Cardiology/American Heart Association/Society for Cardiovascular Angiography and Interventions guidelines and the 2020 European Society of Cardiology guidelines for the management of acute coronary syndromes in patients without persistent ST-segment elevation [22,23]. Coronary angiography was performed using a catheter via the radial or femoral artery, according to the operator’s preference, and multiple matched angiographic images were obtained for each patient after intracoronary nitrates were injected, if necessary.

Patients presenting with NSTE-ACS were classified into 3 groups according to treatment strategy: medical therapy, PCI after coronary angiography, and CABG. Additionally, patients deemed to require CABG after angiographic evaluation were referred for surgical consultation and possible coronary artery bypass grafting and were excluded from the study. PCI-stenting was performed in at least 1 vessel of all patients immediately after coronary angiography. In patients presenting with severe multivessel stenosis, we implemented a staged approach, addressing the identified culprit lesion in the initial procedure. The interventional protocol standardized the use of drug-eluting stents for all cases, independent of manufacturer.

CALCULATION OF THE GRACE RISK SCORE:

The GRS was calculated using a computer program (https://www.outcomes-umassmed.org/grace/) [24]. The GRS takes into account various clinical and laboratory data, including Killip class for congestive heart failure, systolic blood pressure at admission, heart rate at admission, age of the patient, creatinine level, history of cardiac arrest, ST-segment change on index electrocardiographic (ECG), and elevated cardiac enzyme levels (eg, troponin). The GRS, calculated using these parameters, determines the patients’ risk level (low, intermediate, or high risk) and guides the selection of more aggressive or conservative treatment strategies. After calculating the GRS values, patients were divided into groups as recommended by the American Heart Association and the European Society of Cardiology guidelines for NSTE-ACS patients. GRS of NSTE-ACS patients were divided into 3 groups according to the limits determined by the current NSTE-ACS patient treatment guidelines and the GRS calculation computer program. Low GRS (LGRS) was <108, intermediate GRS (IGRS) was 108–140, and high GRS(HGRS) was >140 [22–24].

STUDY ENDPOINTS:

In-hospital MACEs were defined as malignant cardiac arrhythmia, cardiac death, recurrent myocardial infarction (MI), target lesion revascularization, and HF. All deaths were classified as cardiac deaths unless there was another cause [25]. Re-infarction was defined as signs and symptoms of recurrent ischemia at rest, new or recurrent ST-segment elevations of ≥0.1 mV in at least 2 contiguous leads, and lasting ≥30 minutes [26].

STATISTICAL ANALYSIS:

Statistical evaluation was conducted through 2 platforms: SPSS software package version 24 (IBM Corporation, Chicago) and Turcosa Analytics version 1.0.0 (Melikgazi, Kayseri, Türkiye). Data were evaluated for normal distribution by the Shapiro-Wilk test and histogram Q-Q plots. When 2 groups with MACE score+ and MACE score-were compared, the independent-samples t test was used for normally distributed parameters and the Mann-Whitney U test was used for non-normally distributed parameters. When demographic and laboratory data were categorized and compared according to GRS, for variables showing normal distribution and homogeneity of variance, parametric analysis using one-way ANOVA was applied, while data failing these assumptions underwent nonparametric assessment via the Kruskal-Wallis approach. The Pearson chi-square test was used for categorical variables. Pearson’s correlation coefficient was used to evaluate the correlation of inflammatory markers with GRS in patients with normal distribution and Spearman’s correlation analysis was used in patients without normal distribution. To assess diagnostic performance in patients with high GRS, we employed receiver operating characteristic (ROC) curve methodology to evaluate PIV, SII, and NLR measurements. Initial screening of potential predictors was performed through univariate assessments. Variables demonstrating statistical significance (P<0.05) in preliminary analyses were subsequently incorporated into multivariate models. To minimize the impact of multicollinearity among inflammatory markers, separate regression analyses were conducted for each parameter.

Results

According to GRS, 91 (18.6%) patients had LGRS, 183 (37.3%) had IGRS, 215 (44%) had HGRS. Demographic characteristics and laboratory findings of these patients are shown in Table 1. There was no statistically significant difference in age between the groups (61.3±10.9, 63±9.3, 64.1±11.1; P=0.41). The HGRS group had the highest proportion of men – 158 (73.5%) – and there was a statistically significant difference between the groups (53 [58.2%] in the LGRS group and 112 [61.2%] in the IGRS group, P=0.002). The HGRS group had the highest number of patients with HT and was statistically significantly higher than the other groups (146 [67.9%] in the HGRS group, 42 [46.2%] in the LGRS group and 64 [34.4%] in the IGRS group, P=0.004). Systolic/diastolic blood pressure measurements were lowest in the HGRS group and highest in the LGRS group (P<0.001). While there was no difference between the LGRS and the IGRS groups in LVEF and heart rate, LVEF was lower in the HGRS group and heart rate was higher (P<0.001, for both). Regarding Killip classes, there were significantly more patients with Killip scores in the HGRS group (P<0.001). Serum creatinine levels were lowest in the LGRS group and highest in the HGRS group. For GFR values, there was no difference between the IGRS and the LGRS groups, while in the HGRS group they were significantly lower (P<0.001). AST values were higher in the HGRS group than in the other groups, but there was no statistically significant difference between the IGRS and the LGRS groups (P<0.001). Analysis of the HGRS group revealed significantly elevated levels of 5 inflammatory markers (monocyte count, platelet count, NLR, SII, and PIV) compared to the IGRS and the LGRS groups (P<0.001).

There was a positive, strong, and statistically significant correlation between PIV and GRS (r=0.71, P<0.001) There was a positive, moderate, and statistically significant correlation between SII and GRS.(r=0.56, P<0.001). There was a positive, moderately significant correlation between NLR and GRS (r=0.5, P<0.001) (Table 2).

Patients were further divided into 2 groups: associated MACE- and not associated MACE+ with in-hospital MACE. Of the MACE-related patients (total 34 patients), 7 (20.6%) had all-cause death, 10 (29.4%) had cardiac arrhythmia, 14 (41.2%) had repeat target vessel revascularization (PCI) and re-infarction, and 3 (8.8%) had stroke. The mean age of patients in the MACE-group was 64 years, while the mean age of patients in the MACE+ group was 71 years, and there was a statistically significant increase in those with the MACE+ (P=0.01). Systolic and diastolic blood pressure were significantly lower in the MACE+ group (P<0.001), while heart rate was significantly higher in the MACE+ group (P<0.001). When evaluated in terms of the rate of those with high Killip class and GRS, it was significantly higher in the MACE+ group (P<0.001). Platelet count and monocyte count were significantly higher in the MACE+ group than in the MACE-group (P=0.04, P<0.001, respectively). Inflammatory markers such as PIV, SII, and NLR were significantly higher in the MACE+ group (P<0.001). There were no statistically significant differences in other demographic and laboratory characteristics (Table 3).

Various risk factors for MACE+ in NSTE-ACS patients were evaluated using multivariate analysis. Systolic blood pressure, heart rate, GRS, GFR, monocyte count, platelet count, NLR, SII, and PIV were risk factors in univariate analysis. In univariate analysis, PIV, NLR, SII, platelet count, monocyte count, GFR, GRS, heart rate, and systolic blood pressure were significant predictors for MACE, while in multivariate analysis with these variables, NLR (OR: 1.2, 95% CI: 0.91–1.7, P=0.038), SII (OR: 0.9,% CI: 0.9–1, P=0.041 and PIV (OR: 1, 95% CI: 0.99–1, P=0.021) were independent predictors of MACE (Table 4).

The receiver-operator characteristic (ROC) analysis with inflammatory markers in MACE+ patients showed: PIV in cut-off value 756.03, 82% sensitivity, 84% specificity (area under ROC curve 0.86 [95% CI: 0.82–0.89], P<0.001); SII in cut-off value 850.1, 81% sensitivity, 74% specificity (area under ROC curve 0.77 [95% CI: 0.73–0.81], p<0.001); and NLR in cut-off value 4.01, 67% sensitivity, 79% specificity (area under ROC curve 0.74 [95% CI: 0.7–0.78], P<0.001) (Figure 2).

Discussion

To the best of our knowledge, this is the first study to demonstrate the prognostic of PIV in patients with NSTE-ACS in the ED and to examine its association with GRS. In our study, PIV, SII, and NLR values were significantly higher in patients with in-hospital MACE+. We also found that PIV was superior to SII and NLR in determining in-hospital prognosis in NSTE-ACS patients.

Several risk scores have been published to determine the risk of MACE in ACSs [27]. The most respected of these are the Thrombolysis in Myocardial Infarction (TIMI) and GRS scores [28–31]. Both were developed for risk stratification of patients admitted to the coronary intensive care unit with ACS and can account for observations at any time point. Although not designed for this purpose, these scores are widely applied and recommended in European and American guidelines for the full spectrum of ACSs in the ED [32–36]. GRS is a well-validated prediction model for mortality in ACS patients. GRS can be successfully applied to diagnose or exclude ACS in unselected ED populations with chest pain [37].

As human life expectancy increases, the prevalence of NSTE-ACS also rises [38]. Although early PCI in these patients has been shown to reduce long-term mortality, it is often recommended to use GRS to predict in-hospital/post-discharge mortality. Especially in patients with a HGRS, due to the increased risk of mortality and complications, early invasive intervention or intensive medical treatment is generally required, while patients with a low GRS may only need conservative treatment and monitoring [22,39,40]. Thus, unnecessary invasive procedures can be avoided in patients with a low GRS. By accurately predicting the risk of death and complications in ACS patients, GRS ensures that clinical decisions are more reliable and patient-centered. This can both extend the patient’s lifespan and improve their quality of life. Therefore, the use of this scoring system is recommended in the ACS management guidelines of organizations such as the ESC, and this is part of evidence-based treatment [6]. Based on this information, the prognosis for NSTE-ACS patients was predicted by examining various biomarkers and investigating their relationship with GRS.

Correia et al [41], Ahmad et al [42], and Thupakula et al [43] have shown that inflammatory markers (cytokines and chemokines) provide prognostic information independent of GRS in STEMI patients. Oncel et al [44] demonstrated a strong correlation between NLR and GRS in their study. Additionally, NLR was associated with in-hospital MACE independently of GRS. Wang et al [45] have stated that the combination of SII and GRS can predict short-term MACE formation in STEMI patients. In our study, we found a statistically significant correlation between NLR and GRS, and we determined that the NLR in the group of patients who experienced in-hospital MACE was significantly higher compared to the group that did not experience MACE. Analysis of SII and its correlation with GRS demonstrated significantly higher SII levels in patients who developed in-hospital MACE compared to those without MACE, aligning with previous research findings [45].

PIV is a new biomarker that has been used in recent years as a tool for CVD prognosis and risk assessment. This index is derived from peripheral blood parameters to assess systemic inflammation and immune response. Şen et al [46] demonstrated that impaired coronary flow after PCI is associated with a high pre-procedural PIV value. In NSTE-ACS patients, Cetinkaya et al [19,47] found an independent relationship between PIV at the time of admission and the severity/complexity of CAD, as well as the development of CIN after coronary angiography. Liu et al [48] showed that in STEMI patients, the PIV value 12 hours after PTCA is associated with long-term MACE. Byoun et al suggested that there is a relationship between PIV and 1-year MACE rates in non-ACS patients [21]. Additionally, Yilmaz et al [18] demonstrated a relationship between coronary collateral development and PIV in patients with chronic coronary syndrome. In the present study, we found that PIV is significantly associated with the development of in-hospital MACE in NSTE-ACS patients. Additionally, we found a statistically significant relationship between PIV and GRS in NSTE-ACS patients. Our results are consistent with the results of the study conducted by Byoun et al [21]. Although the follow-up period of the patients in our study was longer in terms of MACE, the superiority of our study is that we evaluated it together with risk factors such as GRACE.

PIV includes 4 types of inflammatory cells – neutrophils, lymphocytes, monocytes, and platelets – and it can be speculated that it is a better predictor than single-component inflammatory markers, two-component (eg, PLR and NLR), and three-component inflammatory markers like SII. Neutrophilia is thought to occur due to suppressed neutrophil apoptosis, neutrophil demargination, stem cell stimulation via growth factors (such as G-CSF), elevated serum cortisol and catecholamines, and sympathetic activation [49]. Neutrophils strongly influence all processes of atherosclerotic plaque formation, both by directly invading the plaque and indirectly through the proteolytic enzymes they release [50,51]. The resulting endothelial dysfunction increases the adhesion of other inflammatory cells to the surface, leading to plaque progression and, along with plaque rupture, causing local ischemia, hypoxia, and microthrombosis [52]. This contributes to the further progression of atherosclerotic plaques through involvement of mononuclear cells (monocytes and lymphocytes) and, similar to neutrophils, directly and/or through proteolytic enzymes and cytokines/growth factors [53]. In addition to the increase in these cells, the presence of lymphopenia – defined as changes in the T4/T8 lymphocyte ratio due to neuroendocrine stress and tissue damage, margination and redistribution of lymphocytes within the lymphatic system, increased apoptosis due to some cytokines (especially interleukin [IL-10] and tumor necrosis factor beta – ischemia-reperfusion injury, and some proinflammatory cytokines that can contribute to emergence of lymphopenia, also leads to pathophysiological plaque destabilization [49,53,54]. Platelets release proinflammatory chemokines, which attract leukocytes and facilitate their adhesion to the vascular wall, and help activate angiogenesis in lesion areas [55,56]. Thrombocytosis causes platelet aggregation, increased blood vessel permeability, platelet activation, and changes in microcirculation, which intensifies inflammatory responses [49].

Recent reports suggests PIV can serve as a comprehensive indicator for evaluating host immune and inflammatory states. While earlier research demonstrated SII’s effectiveness as a sensitive marker for monitoring inflammatory and immune responses, the evolution to PIV is a significant advancement [57]. Specifically, PIV expands upon SII’s triple inflammatory parameter by integrating monocytes, which are key drivers of local inflammation, thereby creating a more complete blood cell profile [58]. The expanding field of inflammation research continues to demonstrate these markers’ increasing relevance in understanding disease processes.

Conclusions

LIMITATIONS:

Our study had several important limitations to consider. The investigation was limited by its retrospective nature, single-center design, and relatively small cohort size. We did not use a control group and did not compare with groups with normal test results. PIV measurements were restricted to a single time point during Emergency Department admission. The analysis did not account for patients’ concurrent medications. In assessing MACE risk, we exclusively utilized GRS without evaluating other established risk stratification tools like HEART or TIMI scores. Lastly, while elevated PIV demonstrated a significant predictive value for MACE, its potential role in therapeutic decision-making requires further investigation.