European Journal of Cardiovascular Medicine

Cardiovascular diseases (CVDs) are the leading cause of death globally, accounting for more than 17 million deaths annually, with projections indicating further increases in coming decades (1). In India, the impact is particularly profound, where ischemic heart disease strikes at a younger age than in Western countries, leading to premature mortality and the loss of productive life years (1). Atherosclerotic heart disease remains the most significant contributor to this burden, underscoring the need for improved prognostication, risk stratification, and management strategies tailored to resource-limited settings.

Pathophysiology of Atherosclerosis and Acute Coronary Syndromes

Atherosclerosis is now understood as a lipid-driven, immune-inflammatory disease characterized by the accumulation of cholesterol, macrophages, and T-lymphocytes within the intima, forming atheromatous plaques (2). These plaques may remain stable for years but can become unstable due to ongoing inflammatory activity. Rupture of a vulnerable plaque leads to thrombus formation, which is the central event precipitating acute coronary syndromes (ACS) (3). Platelets play a pivotal role in this process, not only by forming thrombi but also through platelet-leukocyte interactions that amplify vascular inflammation (4,5,20,21).

Role of Platelets and Leukocytes in ACS

Platelet activation triggers the release of pro-inflammatory mediators, adhesion molecules, and growth factors, contributing to endothelial dysfunction and propagation of atherothrombosis (3,19). Platelet-leukocyte interactions are especially relevant, as increased platelet-leukocyte aggregates at the site of plaque rupture have been implicated in the no-reflow phenomenon following reperfusion therapy (4,20). These mechanisms highlight the central role of inflammatory and hematological processes in ACS pathophysiology (5).

Hematological Indices as Predictors

A growing body of evidence supports the role of hematological indices as prognostic tools in ACS (6,18). Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), red cell distribution width (RDW), mean platelet volume (MPV), and plateletcrit (PCT) are simple, cost-effective, and widely available markers derived from routine complete blood counts (CBC).

• NLR: Elevated NLR reflects heightened innate immunity and suppressed adaptive immunity, and has been strongly associated with adverse outcomes including impaired coronary flow, larger infarct size, ventricular arrhythmias, and increased mortality (7,8,37–43).
• Neutrophils: Neutrophilia itself predicts impaired reperfusion and long-term mortality (7,42).
• Leukocyte Count: Elevated leukocyte count has been consistently shown to predict in-hospital and long-term mortality after ACS and PCI (14,15).
• RDW: Increased RDW has been linked with cardiovascular mortality and recurrent ischemic events, correlating with inflammation and oxidative stress (11,29–33).
• Hemoglobin: Both low and high hemoglobin levels independently influence ACS outcomes (12).
• Platelet Indices:
• Elevated MPV has been associated with recurrent myocardial infarction, stent thrombosis, and long-term mortality (8,16,50–55).
• PLR has predictive value for angiographic no-reflow and long-term mortality (25,26,44–47).
• Plateletcrit (PCT) is emerging as an independent predictor in STEMI (23,35).
• PDW and platelet distribution changes correlate with severity of coronary artery disease (48,49).

Collectively, these markers provide valuable insights into systemic inflammation, platelet reactivity, and coagulation status—factors that are critical in ACS outcomes.

Risk Stratification in ACS

Early and accurate risk stratification is essential in ACS management. Current guidelines recommend validated tools such as the Global Registry of Acute Coronary Events (GRACE) risk score (9). The GRACE model incorporates age, hemodynamic parameters, renal function, ECG changes, and cardiac biomarkers to predict short- and long-term outcomes (10). However, it largely reflects clinical severity rather than underlying inflammatory and hematological status.

Integrating Hematological Parameters with GRACE

Recent studies suggest that combining CBC parameters with GRACE enhances predictive accuracy (11–13,46,54). For example:

• RDW in conjunction with GRACE better predicted outcomes post-PCI (11).
• Hemoglobin level added incremental prognostic value (12).
• NLR and GRACE together improved risk prediction in Chinese ACS cohorts (13).
• MPV combined with GRACE improved prediction of cardiovascular events (54).

These findings highlight the complementary role of hematological markers alongside established risk models.

Coagulation Parameters in ACS

Beyond hematological indices, coagulation markers such as prothrombin time (PT) and activated partial thromboplastin time (aPTT) have also been studied. Abnormalities in PT/aPTT can indicate underlying coagulation activation, antithrombotic drug effects, or bleeding risk (18). Elevated fibrinogen and prolonged PT have been associated with worse outcomes in ACS, further strengthening the rationale for including coagulation markers in prognostic assessment.

Troponins versus Hematological Indices

While cardiac troponins (cTnI, cTnT) remain the gold standard for ACS diagnosis and risk stratification (17,36), they are not universally accessible, particularly in rural or resource-limited settings in India. In such scenarios, hematological and coagulation parameters—available from routine, inexpensive laboratory tests—may serve as practical adjuncts to guide clinical decision-making (18).

Indian and Global Perspectives

Indian studies remain limited, but evidence from international cohorts consistently demonstrates that simple hematological markers such as NLR, PLR, MPV, and RDW can predict outcomes ranging from impaired reperfusion to long-term mortality (6,7,22–28,34,41,43,45,47–55). The Indian context—with its high prevalence of diabetes, hypertension, and younger age of ACS onset—makes the exploration of such cost-effective markers particularly relevant.

Rationale for the Present Study

Despite advances in interventional cardiology and pharmacotherapy, mortality and morbidity in ACS remain high. Existing risk stratification models like GRACE, though validated, do not fully account for the inflammatory and hematological milieu central to ACS pathophysiology. Hematological and coagulation parameters are inexpensive, universally available, and easily reproducible—qualities particularly valuable in resource-constrained settings such as India.

This study aims to systematically evaluate the prognostic significance of hematological and coagulation parameters—including WBC count, NLR, PLR, RDW, MPV, PCT, PDW, PT, and aPTT—in predicting in-hospital mortality and morbidity among ACS patients. By integrating these indices with established risk models, it may be possible to refine risk stratification, optimize patient management, and improve outcomes.

Aims and Objectives

Aim

To establish the prognostic value of hematological parameters such as NLR, PLR, PCT, MPV, PDW and coagulation markers like PT and aPTT in acute coronary syndrome patients as predictors of in-hospital mortality and morbidity.

Objectives

1. To determine whether elevated NLR, PLR, MPV, PCT, PDW, PT, and aPTT are associated with increased all-cause mortality (cardiac and non-cardiac) among hospitalized ACS patients.
2. To determine whether elevated NLR, PLR, MPV, PCT, PDW, PT, and aPTT are associated with increased morbidity, defined as the development of ventricular tachyarrhythmia and cardiogenic shock during hospitalization.

1. Study Design -

This was a hospital-based longitudinal study conducted on patients presenting with acute coronary syndrome (ACS).

2. Study Setting -

The study was carried out in the Emergency Department of S.C.B. Medical College and Hospital (SCBMCH), Cuttack, Odisha, a tertiary care referral center.

3. Study Period -

The study was conducted over 12 months, from 1st September 2022 to 31st August 2023.

4. Study Population -

All patients presenting to the emergency department of SCBMCH with a clinical diagnosis of ACS (ST-elevation myocardial infarction [STEMI], non-ST elevation myocardial infarction [NSTEMI], or unstable angina [UA]) were considered for inclusion.

5. Sampling Method -

Purposive sampling technique was employed.

6. 
   Inclusion Criteria -
7. Patients diagnosed with ACS (STEMI, NSTEMI, or UA) according to the definitions and criteria published by the American College of Cardiology (ACC) and European Society of Cardiology (ESC).

7. Exclusion Criteria -
8. Patients who died immediately after arrival in the emergency department or required cardiopulmonary resuscitation (CPR) at home or in the emergency department.
9. Patients with known inflammatory diseases, malignancy, chronic kidney disease, chronic liver disease, or pregnancy.

8. Outcome Definitions -

Mortality: Death due to any cause (cardiac or non-cardiac) during hospitalization post-ACS.

Morbidity: Defined as any of the following during hospitalization:

- Ventricular tachyarrhythmia

- Cardiogenic shock

- Prolonged hospital stay (>5 days)

METHODOLOGY -

After obtaining written informed consent, venous blood samples were collected from eligible patients on admission.

Patients were categorized into two groups:

1. Discharged group – patients who survived and were discharged.
2. Mortality group – patients who died during hospitalization.

Hematological parameters analyzed: NLR, PLR, MPV, PCT, PDW.

Coagulation parameters analyzed: PT, aPTT.

Clinical and demographic data, examination findings, and relevant investigations were recorded in a structured proforma.

All patients underwent coronary angiography. Significant stenosis was defined as ≥70% narrowing in non–left main vessels and ≥50% narrowing in the left main coronary artery (36).

10. Laboratory Methods -

Hematological analyses were performed using XN-1000™ Automated Hematology Analyzer (Sysmex, Japan).

This system utilizes impedance technology, VCS (Volume, Conductivity, Scatter) technology, and computer-based algorithms to provide precise hematological indices.

Coagulation parameters (PT, aPTT) were analyzed using CS-2400 Sysmex Coagulation Analyzer.

Both analyzers are fully automated, high-throughput systems used in tertiary care laboratories.

11. Sample Size Calculation -

Sample size was estimated using the formula: n = Zα² (Sn)(1-Sn) / L² × p

Where: Zα = 1.96 (95% confidence level), Sensitivity (Sn) = 85%, Prevalence (p) = 0.20, Precision (L) = 0.10 (90% power).

Based on calculations and reference from Bajari & Tak (2017), the minimum required sample size was 250.

However, a total of 516 patients were included in the present study to improve statistical validity.

12. Data Analysis -

Data were entered into Microsoft Excel and analyzed using SPSS software version 27.0 (IBM Corp, Armonk, NY, USA).

Descriptive statistics: frequencies, percentages, means, and standard deviations.

Comparisons between groups: Independent t-test for continuous variables, Chi-square test for categorical variables.

Regression analyses: Univariate logistic regression was performed for each hematological and coagulation parameter against the outcomes.

Variables showing statistical significance (p < 0.05) were entered into a multivariate logistic regression model to identify independent predictors.

13. Ethical Considerations-

The study was conducted after obtaining approval from the Institutional Ethics Committee of S.C.B. Medical College and Hospital, Cuttack.

Informed consent was obtained from all participants prior to inclusion.

14. Implications- This study seeks to evaluate whether simple, inexpensive hematological and coagulation parameters can serve as reliable predictors of in-hospital mortality and morbidity in ACS patients, thus offering a cost-effective prognostic tool in resource-limited settings.

15. Budget- No external funding or financial support was required.

A total of 516 patients with acute coronary syndrome (ACS) were enrolled in this longitudinal study. All patients received standard-of-care treatment as per institutional protocols, and a complete hemogram was obtained on admission. The demographic characteristics, vital parameters, Killip classification, cardiac biomarkers, electrocardiographic findings, hematological indices, outcomes, and associations with mortality and morbidity are described in detail below.

Demographic Characteristics

Of the 516 patients, 331 (64.1%) were male and 185 (35.9%) were female, resulting in a male-to-female ratio of approximately 1.8:1. This distribution reflects the recognized epidemiological trend of ACS affecting males more commonly than females in the Indian population. The mean age of the study participants was 60.63 years (±12.31), with a median of 61 years. The age range spanned from 25 years (youngest participant) to 92 years (eldest participant). The distribution indicates that while ACS remains predominantly a disease of older adults, it can also affect relatively younger individuals, highlighting the premature onset of coronary artery disease in the Indian context.

Table 1. Baseline Demographics and Admission Vitals (N = 516)

Table 2. Clinical Presentation on Admission (N = 516)

Table 3. Hematological Indices at Presentation (N = 516)

Table 4. Independent Predictors of Adverse Outcomes on Multivariable Logistic Regression (N = 516)

Table 5. Discrimination of NLR and PLR for Outcomes (ROC Metrics and Operating Points, N = 516)

Vital Signs at Admission

At the time of presentation to the emergency department, the mean heart rate was 85.57 beats per minute (±18.06), with values ranging between 45 and 150 beats per minute. The systolic blood pressure (SBP) on admission averaged 138.13 mmHg (±24.93), with extremes ranging from 80 mmHg to 250 mmHg. The diastolic blood pressure (DBP) showed a mean of 82.88 mmHg (±12.86), ranging between 42 and 150 mmHg. The mean oxygen saturation (SpO₂) at admission was 96% (±5.55), with recorded values varying from a minimum of 60% to a maximum of 100%. These findings demonstrate that most patients presented in a hemodynamically stable state, although there was wide variation, with some individuals presenting with severe hypertension, tachycardia, or hypoxemia.

Killip Classification

On arrival, patients were stratified according to the Killip classification, which is widely used to categorize the severity of heart failure in ACS. A majority, 417 patients (80.8%), were in Killip Class I, indicating absence of clinical heart failure. 22 patients (4.3%) were in Killip Class II, showing signs of mild-to-moderate heart failure. 67 patients (13.0%) were in Killip Class III, consistent with frank pulmonary edema, while 10 patients (1.9%) presented in Killip Class IV, consistent with cardiogenic shock. This distribution indicates that while most patients presented without overt heart failure, nearly one in five had evidence of cardiac decompensation on arrival.

Cardiac Biomarker (Troponin) Status

Troponin testing was performed for all participants at presentation. Out of the 516 patients, 337 (65.3%) tested positive, while 179 (34.7%) had negative results. A majority of patients with STEMI and NSTEMI demonstrated elevated troponin, while a significant proportion of unstable angina cases remained negative, in keeping with established diagnostic criteria.

Electrocardiographic Findings

Electrocardiography (ECG) was carried out in all patients at admission. 275 patients (53.3%) were diagnosed with ST-elevation myocardial infarction (STEMI), representing the largest subgroup. 44 patients (8.5%) had non-ST-elevation myocardial infarction (NSTEMI), while 195 patients (37.8%) were classified as unstable angina (UA). Only 2 patients (0.4%) were categorized as having isolated anterior wall myocardial infarction (AWMI). These findings reaffirm that STEMI constitutes the predominant clinical presentation of ACS in this cohort.

Hematological Parameters

A detailed analysis of hematological parameters was performed. The mean hemoglobin concentration was 13.37 g/dL (±1.82) with values ranging from 6.2 g/dL to 20 g/dL, while the median was 14 g/dL. The red blood cell count had a mean of 4.49 million cells/µL (±0.66), with values ranging from 2.67 to 6.45 million cells/µL.

The total leukocyte count (TLC) showed a mean of 9654.07 cells/µL (±3359.9), with a median of 9000. The lowest count recorded was 4300, while the highest was 23,000, indicating the presence of leukocytosis in a subset of patients. The neutrophil count averaged 6852.30 (±3181.0), ranging from 1867 to 21,505, while the lymphocyte count was markedly lower, with a mean of 1864.66 (±961.81).

Derived indices such as the neutrophil-to-lymphocyte ratio (NLR) demonstrated a mean of 5.22 (±4.67), with a wide range from 0.7 to 23.9. Similarly, the platelet-to-lymphocyte ratio (PLR) had a mean of 193.04 (±148.87), ranging between 34 and 1149.

Platelet indices were also assessed: the mean platelet count was 2.68 lakh/µL (±0.89). The mean platelet volume (MPV) was 8.35 fL (±0.89), with values between 5.8 and 12 fL. Plateletcrit (PCT) showed a mean of 0.22 (±0.07), with values ranging between 0.05 and 0.59, while the platelet distribution width (PDW) had a mean of 16.9 (±0.60).

Additional red cell indices were analyzed: packed cell volume (PCV) averaged 39.2% (±4.9), with a range of 21–55%. The mean corpuscular volume (MCV) was 84.41 fL (±6.88), the mean corpuscular hemoglobin (MCH) was 28.77 pg (±2.78), and the mean corpuscular hemoglobin concentration (MCHC) averaged 33.56 g/dL (±1.67).

These findings illustrate the variability of hematological indices among ACS patients, with evidence of inflammatory responses (elevated NLR and PLR) and platelet reactivity indices (MPV, PCT, PDW) contributing to disease severity.

Mortality and Major Adverse Cardiovascular Events (MACE)

During hospitalization, 30 patients (5.8%) died, while 486 patients (94.2%) survived and were discharged. Analysis of major adverse cardiovascular events (MACE) revealed that:

• Cardiogenic shock occurred in 108 patients (20.9%).
• Ventricular tachyarrhythmias developed in 18 patients (3.5%).
• Left ventricular failure (LVF) was documented in 56 patients (10.9%).
• Prolonged hospitalization (>5 days) was observed in 56 patients (10.9%).

These data highlight that although overall mortality was below 6%, morbidity in terms of complications remained significant, with cardiogenic shock being the most common adverse outcome.

Gender and Outcomes

When gender differences were analyzed, mortality occurred in 18 men (5.4%) and 12 women (6.5%), showing no statistically significant difference (p=0.626). Similarly, rates of ventricular tachyarrhythmia (3% in men vs. 4.3% in women), cardiogenic shock (21.8% vs. 19.5%), and LVF (10.9% vs. 10.8%) did not differ significantly. However, prolonged hospitalization was more common in men (13.3%) compared to women (6.5%), and this association was statistically significant (p=0.017).

Independent t-Test Analyses

When hematological indices were compared between mortality and survival groups, NLR and PLR were significantly higher among those who died. The mean NLR in the mortality group was 12.41 (±3.82) compared to 4.78 (±4.33) in survivors (p<0.001). Similarly, PLR was 446.06 (±290.56) in mortality versus 177.43 (±119.51) in survivors (p<0.001). In contrast, MPV, PCT, and PDW showed no significant differences.

Further analyses demonstrated that mortality was also associated with significantly higher neutrophil counts and lower lymphocyte counts (p<0.001). Prolonged PT and aPTT values were also strongly associated with mortality (p<0.001 for both), suggesting that coagulation derangements contributed to worse outcomes.

For morbidity outcomes:

• Ventricular tachyarrhythmia was significantly associated with elevated NLR and PLR (p<0.001 each), and lower MPV and PCT.
• Left ventricular failure showed strong associations with elevated NLR and PLR (p<0.001 each).
• Prolonged hospitalization demonstrated significant associations with NLR and PLR (p<0.001 each), but not with MPV, PCT, or PDW.
• Cardiogenic shock was strongly associated with both NLR and PLR (p<0.001).

Logistic Regression Analyses

On univariate logistic regression, both NLR and PLR emerged as strong predictors of mortality and morbidity. On multivariate regression, only NLR and PLR remained independently significant for mortality, ventricular tachyarrhythmia, LVF, cardiogenic shock, and prolonged hospitalization.

For mortality, every unit increase in NLR was associated with an odds ratio of 1.13 (95% CI: 1.04–1.22; p=0.002), while PLR had an odds ratio of 1.003 (95% CI: 1.001–1.006; p=0.003). For ventricular tachyarrhythmia, NLR again showed strong independent association (OR=1.22, p<0.001). For cardiogenic shock, both NLR and PLR remained significant, with NLR conferring an odds ratio of 1.15 (95% CI: 1.07–1.23). For LVF and prolonged hospitalization, NLR was consistently significant with odds ratios around 1.10–1.22 per unit increase.

These results highlight the robustness of NLR and PLR as predictors of adverse outcomes in ACS patients, while other indices such as MPV, PCT, and PDW did not retain independent predictive value in multivariate models.

ROC Curve Analyses

Receiver Operating Characteristic (ROC) curve analysis was performed for NLR and PLR in predicting mortality and major adverse outcomes.

• For mortality, NLR demonstrated an AUC of 0.909 (95% CI: 0.868–0.949), while PLR had an AUC of 0.862 (95% CI: 0.79–0.934), both highly significant (p<0.05). Using a cutoff value of NLR=9.38, sensitivity was 87.5%, specificity 88.8%, positive predictive value (PPV) 34.1%, and negative predictive value (NPV) 99%, with an overall accuracy of 88.8%. For PLR with a cutoff of 201, sensitivity was 87.5%, specificity 69.8%, PPV 16.1%, NPV 98.8%, and accuracy 70.9%.
• For ventricular tachyarrhythmia, NLR achieved an AUC of 0.882 (95% CI: 0.838–0.927), while PLR achieved an AUC of 0.822 (95% CI: 0.724–0.920). At the chosen cutoffs, NLR had sensitivity of 77.8% and specificity of 86.3%, while PLR had sensitivity of 77.8% and specificity of 67.9%.
• For cardiogenic shock, NLR achieved an AUC of 0.839, while PLR had an AUC of 0.848. NLR demonstrated high specificity (95.1%) but moderate sensitivity (56.4%), while PLR showed higher sensitivity (83.6%) but lower specificity (79.8%).
• For left ventricular failure, NLR achieved an AUC of 0.774, while PLR achieved 0.743, both significant. NLR demonstrated specificity of 88.6% and sensitivity of 50%, while PLR had sensitivity of 73.3% and specificity of 71.5%.
• For prolonged hospitalization, NLR achieved an AUC of 0.847, while PLR achieved 0.821, both significant. At the cutoffs, NLR demonstrated sensitivity of 70% and specificity of 91.2%, whereas PLR demonstrated sensitivity of 83.3% and specificity of 72.8%. Collectively, the ROC analyses confirm that both NLR and PLR possess substantial discriminatory power in predicting mortality and morbidity, with NLR consistently outperforming PLR in terms of overall accuracy and predictive strength.

Summary of Findings

In summary, this study involving 516 ACS patients demonstrated that:

• Mortality occurred in 5.8%, while morbidity in the form of cardiogenic shock, ventricular tachyarrhythmia, LVF, and prolonged hospitalization was frequent.
• NLR and PLR consistently emerged as strong predictors of both mortality and morbidity across univariate, multivariate, and ROC analyses.
• Other hematological indices, including MPV, PCT, and PDW, did not show independent prognostic value in multivariate models.
• ROC analysis confirmed the superiority of NLR, with high sensitivity, specificity, and overall accuracy.

Table 1 presents the baseline demographic and admission vital characteristics of the study population (N = 516). The cohort was predominantly male (64.1%) with a mean age of 60.6 years, reflecting the established epidemiological profile of ACS as a disease of middle-aged and older men. The wide age range (25–92 years) also underscores the early onset of coronary artery disease in the Indian population. Admission vitals showed a moderate mean heart rate of 85 bpm and mean blood pressure values within a hypertensive range, while oxygen saturation was relatively preserved (mean 96%). These findings suggest that although most patients presented in hemodynamically stable condition, there was substantial heterogeneity with some presenting at extremes of vital parameters.

Table 2 outlines the clinical presentation at admission. A striking majority (80.8%) were Killip Class I, indicating absence of overt heart failure, while approximately 20% had evidence of decompensated heart failure (Killip II–IV). Troponin positivity was observed in 65.3% of the cohort, confirming myocardial injury in most patients. Electrocardiographic findings demonstrated STEMI as the most common presentation (53.3%), followed by unstable angina (37.8%) and NSTEMI (8.5%). These patterns are consistent with Indian ACS registries where STEMI predominates, reflecting late presentation and limited early diagnostic access in the population.

Table 3 summarizes hematological indices at presentation. Patients exhibited mean hemoglobin levels within the normal range, though extremes from 6.2 g/dL to 20 g/dL highlight both anemic and polycythemic subgroups. Elevated leukocyte and neutrophil counts, coupled with reduced lymphocyte counts, resulted in a high mean neutrophil-to-lymphocyte ratio (NLR = 5.22), suggestive of systemic inflammation. Similarly, platelet-to-lymphocyte ratio (PLR = 193.0) was elevated, reflecting inflammatory and thrombotic burden. Platelet indices such as MPV, PCT, and PDW were within conventional limits but showed wide variation, reinforcing their potential role as prognostic markers. Collectively, these values demonstrate that inflammatory hematological profiles are frequently deranged in ACS patients.

Table 4 depicts independent predictors of adverse outcomes on multivariable logistic regression. NLR and PLR consistently emerged as robust independent predictors of mortality, cardiogenic shock, left ventricular failure, and prolonged hospitalization, even after adjusting for confounders. Notably, NLR was a universal predictor across all adverse outcomes, while PLR retained significance in mortality, cardiogenic shock, and LVF. Interestingly, MPV was inversely associated with ventricular tachyarrhythmia, suggesting a potential protective link. These findings establish NLR and PLR as inexpensive, widely available biomarkers with strong prognostic significance in ACS.

Table 5 evaluates the discriminative ability of NLR and PLR using ROC analysis. Both indices showed strong predictive performance for mortality and morbidity, with NLR outperforming PLR in terms of AUC and overall accuracy across all outcomes. For mortality, NLR achieved an AUC of 0.909, with high sensitivity (87.5%) and specificity (88.8%). For ventricular tachyarrhythmia, cardiogenic shock, and prolonged hospitalization, NLR maintained higher accuracy compared to PLR. While PLR also demonstrated predictive value, its specificity and positive predictive values were lower, limiting clinical utility as a standalone marker. These results confirm that NLR, with its superior diagnostic accuracy, may serve as the most powerful hematological predictor of adverse in-hospital outcomes in ACS patients.

Figures 1–3 highlight the prognostic role of hematological markers in patients with acute coronary syndrome (N = 516). As shown in Figure 1, multivariable regression identified NLR as a consistent independent predictor of mortality, ventricular tachyarrhythmia, cardiogenic shock, left ventricular failure, and prolonged hospitalization, with PLR also retaining significance for several outcomes. Figure 2 demonstrates the superior discriminative ability of NLR compared to PLR across all endpoints, with AUC values exceeding 0.90 for mortality and above 0.80 for most morbidity outcomes. Figure 3 further illustrates the operating characteristics of NLR at the predefined cutoff of 9.38, revealing high sensitivity, specificity, and negative predictive values across outcomes, particularly for mortality and prolonged hospitalization. Collectively, these figures reinforce that NLR, more than PLR or platelet indices, provides robust, inexpensive, and clinically meaningful risk stratification in ACS

Cardiovascular disease remains one of the foremost causes of morbidity and mortality worldwide and is emerging as the leading cause of premature deaths in India. At the center of this epidemic is atherosclerotic heart disease, a condition driven by systemic and local inflammation, endothelial dysfunction, and thrombotic mechanisms. Acute coronary syndrome (ACS), the most severe clinical manifestation of atherosclerosis, is characterized by plaque rupture, platelet aggregation, leukocyte activation, and thrombus formation. Although sophisticated imaging, biochemical biomarkers, and risk scores are routinely employed to stratify patients, simple, inexpensive, and universally available hematological indices such as NLR, PLR, MPV, PDW, PCT, PT, and aPTT may provide equally valuable prognostic insights.

This study investigated the association of these parameters with in-hospital mortality and major adverse cardiovascular events (MACE) such as ventricular tachyarrhythmia, left ventricular failure (LVF), cardiogenic shock, prolonged hospitalization, and readmission in 516 ACS patients admitted to a tertiary care hospital in Eastern India. The results demonstrated that NLR and PLR emerged as the most powerful predictors, while MPV, PDW, and PCT showed limited prognostic utility. Furthermore, coagulation parameter PT was found to be significantly associated with mortality.

NLR as a Prognostic Marker

The neutrophil-to-lymphocyte ratio (NLR) is a surrogate marker of systemic inflammation and immune imbalance. Elevated neutrophils indicate heightened inflammatory activity and oxidative stress, while reduced lymphocytes reflect poor adaptive immune response and physiologic stress. In our cohort, the mean NLR at admission was 5.22 (±4.67), higher than normal cut-offs, and significantly elevated among patients with mortality and morbidity. Mortality cases had a mean NLR of 12.41, more than double the NLR of survivors (4.78), with p<0.001.

These findings echo prior literature. Tamhane et al. (8) showed that NLR was predictive of both in-hospital and 6-month mortality in ACS patients undergoing PCI. Núñez et al. (39) demonstrated superiority of NLR over total leukocyte count in predicting mortality among STEMI patients, while Azab et al. (40) reported similar results in NSTEMI. Sawant et al. (41) suggested an NLR cut-off of 7.4 predicted short- and long-term survival in revascularized STEMI patients. Other investigators linked elevated NLR to stent thrombosis, no-reflow, and higher rates of MACE (42,43).

In our analysis, NLR was not only significantly associated with mortality but also with ventricular tachyarrhythmia (10.62 vs. 5.03), LVF (10.06 vs. 4.63), cardiogenic shock (9.8 vs. 4.0), and prolonged hospitalization (11.38 vs. 4.47). Univariate logistic regression showed strong associations with all outcomes, and multivariable regression confirmed that NLR independently predicted each adverse event. ROC analysis further demonstrated that NLR had excellent discriminative ability, with an AUC of 0.909 for mortality and >0.80 for all morbidities. At a cut-off of 9.38, NLR provided high sensitivity and specificity, especially for mortality prediction.

Thus, our study adds to the growing evidence that NLR is a robust, inexpensive, and universally available biomarker that could complement or even outperform traditional risk scores in certain settings.

PLR as a Prognostic Marker

The platelet-to-lymphocyte ratio (PLR) integrates thrombosis and immune dysregulation. Elevated platelet counts signal prothrombotic activity, while reduced lymphocytes denote systemic stress. In this study, mean PLR was 193.04, with significantly higher values in adverse outcome groups. Patients who died had a mean PLR of 446 compared to 177 in survivors (p<0.001). Similarly, PLR was significantly elevated in those who developed ventricular tachyarrhythmia (305 vs. 189), LVF (355 vs. 173), cardiogenic shock (336 vs. 155), and prolonged hospitalization (345 vs. 174).

Multivariate regression confirmed PLR as an independent predictor of mortality, LVF, and cardiogenic shock, although its association with ventricular tachyarrhythmia and prolonged hospitalization did not remain significant after adjustment. ROC analysis using a cut-off of 201 showed good discriminative ability (AUC >0.80 for most outcomes), though specificity and positive predictive values were lower than those of NLR.

These findings are consistent with prior reports. Keskin et al. (23) highlighted PLR as a predictor of in-hospital and long-term outcomes in STEMI, while Yildiz et al. (45) and Acet et al. (46) associated PLR with no-reflow, recurrent MI, and mortality. Azab et al. (47) also demonstrated the utility of PLR in NSTEMI patients. However, PLR is less specific, as it may be elevated in other inflammatory and non-cardiac conditions. Nonetheless, our findings suggest PLR retains independent prognostic significance in ACS, albeit weaker than NLR.

PDW, MPV, and PCT

Platelet indices reflect platelet reactivity and turnover. Platelet distribution width (PDW) has been linked to severity of coronary artery disease and in-hospital MACE. In our cohort, PDW did not show statistically significant associations with mortality or morbidity outcomes. Mean PDW values were nearly identical between outcome groups. Prior studies such as Rechcinski et al. (49) and Celik et al. (50) suggested PDW could predict prognosis post-MI, but our data did not corroborate these findings.

Mean platelet volume (MPV) has been implicated in impaired reperfusion, restenosis, and mortality after ACS (51–53). Wan et al. (54) and Niu et al. (55) showed that MPV enhanced the predictive accuracy of GRACE scores. However, in our study, MPV was not significantly different between adverse outcome groups, and regression analyses did not reveal independent associations.

Plateletcrit (PCT), reflecting the total platelet mass, also did not demonstrate significant prognostic value in our analysis. Although previous research suggested higher PCT levels might be linked with worse outcomes (35), our cohort did not support this.

These discrepancies may relate to population differences, timing of blood sampling, and variations in ACS management.

PT and aPTT as Coagulation Markers

Beyond hematological indices, coagulation parameters also provide insight into ACS prognosis. In our study, prolonged PT was strongly associated with mortality, consistent with international literature linking coagulation abnormalities to worse outcomes. Patients with prolonged PT (>18.5) had significantly higher mortality risk. This aligns with observations from Fuhrmann et al., Fei et al., and Hannan et al., who noted that coagulation derangements, hepatic dysfunction, and consumptive coagulopathy contribute to adverse outcomes in critically ill and ACS patients. aPTT also showed significance in univariate analysis but did not retain independent predictive value.

Comparison with Existing Risk Scores

Traditional scoring systems such as the GRACE risk score are widely used to predict short-term and long-term mortality in ACS (9,10). However, GRACE is primarily designed around clinical and electrocardiographic features and does not account for inflammation or hematological derangements. Our findings suggest that integration of NLR and PLR into GRACE or similar models may improve their predictive accuracy, a notion supported by Zhou et al. (13), Acet et al. (46), and Wan et al. (54).

Strengths of the Study

1. Large sample size (N = 516): Adequately powered to detect associations across multiple outcomes.
2. Comprehensive evaluation: Examined a spectrum of hematological and coagulation markers rather than focusing on a single parameter.
3. Standardized methodology: Blood samples analyzed using automated Sysmex technology ensured reliability and reproducibility.
4. Clinically relevant outcomes: Mortality and MACE were clearly defined and included common in-hospital complications.
5. First regional evidence: Provides much-needed Indian data where resource-constraints make low-cost predictors particularly valuable.

Limitations

1. Single-center study: Findings may not be generalizable to other populations or healthcare settings.
2. In-hospital outcomes only: Long-term prognostic value was not assessed; follow-up studies are required.
3. Potential confounders: Co-existing infections, inflammatory conditions, or medications could have influenced hematological parameters despite our exclusion criteria.
4. No serial measurements: Only admission values were considered; dynamic changes in NLR and PLR may provide even better prognostic insights.
5. Absence of comparison with high-sensitivity troponins: Although troponin was assessed, advanced biomarkers were not evaluated due to resource limitations.

Recommendations

1. Integration into risk models: NLR and PLR should be considered for incorporation into existing ACS risk scores (e.g., GRACE) to enhance predictive accuracy.
2. Routine reporting: Hospitals should routinely calculate and report NLR and PLR from CBC data to assist clinicians in early risk stratification.
3. Serial monitoring: Future research should explore changes in NLR and PLR over time as potential dynamic predictors.
4. Multicenter validation: Large multicentric Indian studies are needed to validate our findings and establish context-specific cut-offs.
5. Public health implication: In resource-limited settings where troponin assays may not be available, hematological indices can serve as practical, affordable surrogates to identify high-risk ACS patients early.

This study demonstrates that among hematological and coagulation parameters, NLR and PLR are the most powerful predictors of in-hospital mortality and morbidity in ACS patients, with NLR consistently outperforming PLR across outcomes. PT prolongation also predicted mortality. Other platelet indices such as MPV, PDW, and PCT showed limited prognostic value. ROC analyses confirmed that NLR, with a cut-off of 9.38, had excellent discriminative power with high sensitivity, specificity, and negative predictive values.

In summary, NLR, PLR, and PT provide inexpensive, universally available, and clinically meaningful information for early risk stratification in ACS. Their inclusion into routine practice could guide timely interventions, especially in resource-constrained settings. Future multicenter longitudinal studies are required to validate these findings and explore integration with established clinical risk scores.

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