Predictive value of novel inflammatory markers combined with GRACE score for in-hospital outcome in patients with ST-segment elevation myocardial infarction: a retrospective observational study

Abstract

Objectives

To assess the prognostic efficacy of innovative inflammatory indicators, specifically the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR), in conjunction with the Global Registry of Acute Coronary Events (GRACE) score, for predicting adverse in-hospital outcomes among patients diagnosed with ST-segment elevation myocardial infarction (STEMI).

Design and setting

This study is a single-centre retrospective analysis of patients with STEMI treated at the Cardiology Department of Tianjin Third Central Hospital between 1 January 2018 and 31 December 2023. All data were sourced from the hospital’s medical record system.

Outcome measures

The integration of the GRACE score with NLR and PLR facilitated the creation of an innovative predictive model. The model’s predictive efficacy for in-hospital major adverse cardiovascular event (MACE) occurrence was assessed through receiver operating characteristic (ROC) curve analysis and multivariate logistic regression. Additionally, the Net Reclassification Improvement and Integrated Discrimination Improvement were computed to quantify enhancements in predictive value.

Results

Patients were stratified into the MACE (N=167) and the non-MACE group (N=1011) based on the incidence of MACE. A comparison of baseline characteristics between the two groups revealed 13 potential confounding variables. The NLR and PLR levels were converted into binary variables using ROC curve analysis. Univariate logistic regression indicated that both NLR and PLR were significant risk factors for MACE during hospitalisation. After adjusting for confounders, multivariate logistic regression confirmed NLR as an independent predictor of MACE risk in this cohort. Furthermore, both NLR and PLR augmented the predictive accuracy of the GRACE score, with their combined use offering a significant improvement in its predictive capacity.

Conclusions

The NLR possesses the capability to independently forecast the risk of MACE during the hospitalisation period for patients diagnosed with STEMI. The incorporation of the PLR and NLR with the GRACE score serves to augment its predictive precision.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• Data collection encompasses comprehensive information, furnishing a foundation for subsequent statistical analysis, enabling a more comprehensive assessment of the factors influencing patient hospitalisation outcomes.

• A stringent retrospective approach was used to evaluate the effectiveness of novel inflammatory markers in predicting hospital outcomes for patients diagnosed with ST-segment elevation myocardial infarction.

• This investigation constitutes a single-centre study with a limited sample size, and the regional constraints may impede the generalisability of the findings.

• This investigation did not undertake long-term follow-up to ascertain the prognosis; therefore, it is not feasible to ascertain the influence of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio on the long-term outcomes of patients.

• The dynamic monitoring of inflammatory indicators is insufficient, precluding the accurate reflection of the impact that alterations in these indicators have on patient outcomes throughout the progression of the disease.

Introduction

Acute myocardial infarction (AMI) poses a considerable public health concern due to its elevated incidence, complication rate and mortality. Prior research underscores the imperative for the early risk assessment of all individuals afflicted with AMI,1,3 which facilitates the customisation of treatment strategies with greater precision and enhances the prediction of adverse outcomes risk, thus providing guidance for both patient management and long-term rehabilitation planning. Although the Global Registry of Acute Coronary Events (GRACE) score^{4 5} continues to be widely used for risk stratification, its intricacies, encompassing a multitude of parameters, extended report generation duration and elevated costs constrain its widespread adoption. As a result, the identification of more rapid and cost-efficient haematological biomarkers for risk assessment has emerged as a pivotal area of research.

Contemporary research suggests that inflammation plays a crucial role in the initiation and advancement of atherosclerosis.⁶ The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have garnered substantial interest due to their accessibility and capacity to promptly indicate systemic inflammatory status. Previous studies have substantiated a robust correlation between NLR, PLR and adverse prognosis among patients with AMI.⁷ Notably, the NLR functions as an autonomous prognostic indicator for the extent of coronary artery stenosis and for unfavourable outcomes subsequent to percutaneous coronary intervention (PCI).⁸ A recent meta-analysis has revealed a positive correlation between elevated NLR levels and both all-cause mortality and major adverse cardiovascular events (MACEs) in patients experiencing ST-segment elevation myocardial infarction (STEMI).⁹ Nevertheless, clinical data assessing the joint efficacy of NLR and PLR in early risk stratification among STEMI patients are scarce, and no research has been conducted to ascertain whether this combined methodology augments the predictive precision of the GRACE risk score for in-hospital MACE within this cohort.

Consequently, this study assessed the efficacy of initial NLR and PLR levels on admission as autonomous prognostic indicators for the risk of MACE among patients with STEMI during their hospital stay. A novel scoring system was devised by amalgamating NLR, PLR and the GRACE score. A retrospective analysis was undertaken to ascertain whether this innovative model could more effectively and precisely discern high-risk STEMI patients, thereby facilitating enhanced prediction of adverse cardiovascular events occurring during hospitalisation.

Methods

Study population

An investigation conducted retrospectively identified patients diagnosed with STEMI who underwent coronary angiography at the Department of Cardiology, Tianjin Third Central Hospital, from 1 January 2018 to 31 December 2023.

Inclusion criteria: Adult patients aged 18 years or older who fulfil the diagnostic criteria for STEMI as defined by the European Society of Cardiology (2017)¹⁰: Documentation of myocardial injury, characterised by elevated troponin levels surpassing the 99th percentile of the upper reference limit on at least one occasion; the presence of symptoms suggestive of myocardial ischaemia; and ST-segment elevation in at least two contiguous electrocardiographic leads (≥0.2 mV in leads V1–V3 and ≥0.1 mV in other leads). The diagnosis of STEMI was affirmed. All patients underwent coronary angiography within 2 hours of their admission and were administered a loading dose of dual antiplatelet therapy, comprising aspirin 300 mg and clopidogrel 300 mg, prior to the examination.

Exclusion criteria: Initially, this study excluded individuals who had undergone fibrinolytic therapy subsequent to experiencing myocardial infarction, as well as those afflicted with severe congenital heart disease, valvular heart disease and cardiomyopathy in conjunction with myocardial infarction. Second, individuals diagnosed with haematological disorders, systemic inflammatory conditions or autoimmune diseases, malignancies, significant renal and/or hepatic insufficiency, or who had experienced a severe infection within the preceding 30 days, those who had sustained trauma, undergone surgical procedures, received blood transfusions, or were administered glucocorticoids, or those undergoing chemoradiotherapy or immunotherapy were also excluded. Lastly, individuals who were lost to follow-up were excluded from the study. The exclusion criteria are depicted in figure 1.

Figure 1. Flow chart of included patients. STEMI, ST-segment elevation myocardial infarction.

This study endpoint encompassed in-hospital MACE,¹¹ defined as all-cause mortality, recurrent myocardial infarction, acute heart failure, cardiogenic shock, malignant arrhythmia and cardiac arrest.

All-cause mortality pertains to the occurrence of death from any cause during a patient’s hospitalisation.

Recurrent myocardial infarction is characterised by the recurrence of chest pain or the emergence of new electrocardiographic changes during hospitalisation, coupled with novel dynamic alterations in cardiac injury markers (elevations in CK-MB and TNI levels exceeding 20% above baseline).

Acute heart failure is delineated as the manifestation of new-onset symptoms of heart failure (such as shortness of breath) and signs (including pulmonary rales, peripheral oedema or jugular vein distension) during hospitalisation or the intensification of pre-existing symptoms and signs of heart failure (indicative of acute decompensated heart failure).

Cardiogenic shock is defined by the presence of sustained hypotension (systolic blood pressure (SBP) below 90 mm Hg for a duration exceeding 30 min) or the necessity for vasopressors or mechanical circulatory support in patients unresponsive to adequate fluid resuscitation, along with clinical indications of inadequate circulatory perfusion (such as changes in mental status/confusion, cold extremities, oliguria), and biochemical markers of tissue ischaemia and hypoxia, such as elevated blood lactate levels (exceeding 2 mmol/L).

Malignant arrhythmia encompasses the occurrence of ventricular tachycardia, ventricular fibrillation, high-degree or complete atrioventricular block and the onset of new atrial fibrillation.

Data collection

A total of 1178 eligible STEMI patients were ultimately included in the study. The selection process was depicted in figure 1.

Demographic data (age, gender and smoking history), medical history preceding admission (including AMI, PCI, coronary artery bypass surgery, hypertension, diabetes, hyperlipidaemia, atrial fibrillation, heart failure, renal insufficiency, ischaemic stroke, haemorrhagic stroke and chronic obstructive pulmonary disease (COPD)) and medication administration during hospitalisation (aspirin, P2Y12 inhibitors, β-blockers, statins, ACEI or angiotensin receptor blocker (ARB)) were extracted from the medical records. The patient underwent coronary angiography within 2 hours postadmission. Prior to the procedure, venous blood samples were obtained to enable the conduct of a blood routine, myocardial enzyme and renal function analyses. On the morning of the second day of hospitalisation, fasting peripheral venous blood was drawn for the purpose of lipid profile, blood glucose and additional pertinent tests. The NLR and PLR were referenced. Echocardiography was executed within a 24-hour period following admission, with the left ventricular ejection fraction (LVEF) duly noted.

The GRACE risk score, as outlined by Keith et al¹², is derived from eight variables: Killip grade, SBP, heart rate, age, serum creatinine, prehospital cardiac arrest, ST segment deviation and elevated myocardial enzymes. The final score is the cumulative total of the individual scores for each of these indicators.

All data originate from the hospital’s case management system, meticulously documented by the patient’s primary physician. This system provides a comprehensive and systematic compilation of sociodemographic data, medical details and in-hospital diagnostic and treatment specifics, all of which are legally binding to ensure their authenticity. A skilled full-time staff member is responsible for the extraction and entry of data, and the information undergoes regular scrutiny for quality assurance by an associate chief physician. This procedure includes both periodic data audits and quality control sessions to verify the accuracy and integrity of the data.

Patient and public involvement

During the investigative process, neither patients nor the public were engaged in the specific elements of the research’s design and execution. Concurrently, throughout the data processing and analysis phases, adherence to pertinent ethical and legal stipulations was rigorously observed, with stringent measures taken to maintain the confidentiality of patient personal information, thereby ensuring the protection of patient privacy.

Statistical analysis

The statistical analysis was conducted using Stata V.17.0 software. Variables with missing values exceeding 10% were subjected to multiple imputation techniques. Data exhibiting a normal distribution were reported as mean±SD, and comparisons were conducted using the independent samples t-test. In cases where the data were not normally distributed, values were reported as median (P25, P75), and comparisons were made using the Mann-Whitney U test. Categorical data were expressed as frequencies (%), and group comparisons were performed using the χ² test or Fisher’s exact test as appropriate.

Receiver operating characteristic (ROC) curves were used to evaluate the predictive precision of NLR and PLR for MACE, with outcomes conveyed through the area under the curve (AUC). Using the optimal threshold value determined by the Youden index, patients were categorised into low-value and high-value cohorts for subsequent statistical examination. Comparative analyses were conducted between these cohorts concerning baseline characteristics, laboratory test outcomes, in-hospital medication regimens and the occurrence of MACE.

Analyses involving univariate and multivariate logistic regression were performed on NLR and PLR. The selection of covariates for the multivariate model was guided by the 10 events per variable criterion. On examination of the p values presented in table 1 and drawing on clinical expertise, a total of 13 variables were selected for inclusion: age, Killip grade, triglyceride (TG), blood glucose levels, TnI, LVEF, SBP, diastolic blood pressure, heart rate, history of atrial fibrillation, history of chronic kidney disease, usage of ACEI/ARB, and usage of β-blockers.

Table 1. Clinical indicators analysis of STEMI patients.

Characteristics	Non-MACEN=1011	MACEN=167	P value
Demographic
Age, year	64.5 (12.8)	67.9 (11.6)	0.001
Male, n (%)	718 (71%)	119 (71.3%)	0.95
Smoking, n (%)	523 (51.7%)	99 (59.3%)	0.07
Medical history, n (%)
MI	111 (11.0%)	16 (9.6%)	0.59
PCI	90 (8.9%)	15 (9.0%)	0.97
CABG	4 (0.4%)	1 (0.6%)	0.71
DM	300 (29.7%)	61 (36.5%)	0.075
HL	40 (4.0%)	10 (6.0%)	0.23
HTN	564 (55.8%)	101 (60.5%)	0.26
AF	29 (2.9%)	10 (6.0%)	0.037
HF	25 (2.5%)	4 (2.4%)	0.95
Renal dysfunction	24 (2.4%)	10 (6.0%)	0.01
Ischaemic stroke	160 (15.8%)	33 (19.8%)	0.2
Haemorrhagic stroke	17 (1.7%)	4 (2.4%)	0.52
COPD	11 (1.1%)	1 (0.6%)	0.56
Laboratory tests
eGFR, mL/min/1.73 m2	92.0 (72.0，106.0)	76.0 (54.0，98.0)	<0.001
LVEF, %	50.0 (46.0, 55.0)	48.0 (41.0, 52.0)	<0.001
Glu, mg/dL	6.7 (5.3, 9.1)	8.2 (5.8, 10.1)	<0.001
TG, mg/dL	126.7 (90.4,188.9)	114.3 (79.7,158.6)	0.005
TC, mg/dL	175.0 (151.0，206.0)	169.0 (144.0，204.0)	0.1
LDL-C, mg/dL	111.3 (89.7,132.2)	104.0 (88.5,131.4)	0.21
HDL-C, mg/dL	39.1 (33.6,44.8)	37.9 (32.5,44.5)	0.25
TNI,	6.2 (0.2, 25.7)	11.9 (0.5, 30.0)	0.007
CK-MB, U/L	55.3 (12.0,103.0)	57.5 (10.8,109.8)	0.64
Vital signs
Heart rate, bpm	78.0 (67.1, 89.0)	81.0 (68.0, 97.0)	0.009
Killip class >I, n (%)	644 (63.7%)	132 (79.0%)	<0.001
SP, mm Hg	129.0 (114.0, 140.0)	117.0 (99.0, 134.0)	<0.001
DP, mm Hg	74.0 (66.0, 83.0)	70.0 (60.0, 80.0)	<0.001
In-hospital medications, n (%)
Aspirin	942 (93.2%)	152 (91.0%)	0.32
p2y12	863 (85.4%)	146 (87.4%)	0.48
β-Blocker	549 (54.3%)	77 (46.1%)	0.049
Statin	927 (91.7%)	151 (90.4%)	0.58
ACEI/ARB	631 (62.4%)	86 (51.5%)	0.007
PCI	856 (84.7%)	151 (90.4%)	0.051
Grace score	160.0 (143.0, 179.0)	186.0 (165.0, 212.0)	<0.001

The data are shown as median (Q1, Q3) or n (%).

ACE-IACE inhibitorAFatrial fibrillationARBangiotensin receptor blockerCABGcoronary artery bypass graftingCK-MBcreatine kinase isoenzymeCOPDchronic obstructive pulmonary diseaseDMdiabetes mellitusDPdiastolic pressureeGFRestimated glomerular filtration rateGluglucoseHDL-Chigh-density lipoprotein cholesterolHFheart failureHLhyperlipidaemiaHTNhypertensionLDL-Clow-density lipoprotein cholesterolLVEFleft ventricular ejection fractionMImyocardial infarctionPCIpercutaneous coronary interventionSPsystolic pressureSTEMIST-segment elevation myocardial infarctionTCtotal cholesterolTGtriglycerideTNItroponin I

An investigation was conducted to ascertain the influence of the integration of NLR and PLR on the predictive precision of the GRACE risk score. Initially, a model, designated as A1, was formulated with the admission GRACE score as its exclusive predictor. Subsequent to the establishment of A1, model A2 was developed by incorporating the variable ‘NLR’, model A3 was created by including ‘PLR’, and model A4 was constructed by adding both ‘NLR’ and ‘PLR’ as variables. The predictive accuracy of these models was assessed using the C-index derived from the ROC curve. Additionally, the Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) metrics were employed to gauge enhancements in predictive efficacy. The threshold for statistical significance was established at p<0.05, with all analyses conducted as two-tailed tests.

Results

Baseline characteristics

Patients were assigned to the MACE and non-MACE groups in accordance with the occurrence of MACEs. Table 1 delineates a comparison of baseline demographics, medical histories, clinical laboratory parameters, vital signs, medication regimens, PCI treatments and GRACE scores between the two groups.

On conducting a statistical analysis, it was determined that there were considerable variations among the groups with respect to age, history of AF, renal insufficiency, LVEF, blood glucose, TG, eGFR, TNI, systolic pressure, diastolic pressure heart rate, utilisation of β-blockers, renin–angiotensin system inhibitors, Killip classification and the GRACE score (p<0.05). Conversely, no significant differences were observed in terms of gender, smoking practices, presence of hypertension, diabetes mellitus, hyperlipidaemia, myocardial infarction, coronary artery interventions such as stent placement or bypass surgery, heart failure, stroke, COPD, cholesterol levels, high-density lipoprotein, low-density lipoprotein, creatine kinase isoenzymes or administration of dual antiplatelet therapy, with a statistical significance of p>0.05.

The blood routine parameters for both cohorts are compiled in table 2. No substantial disparities were detected in the platelet or monocyte counts between the cohorts. Nevertheless, the cohort experiencing MACE demonstrated significantly elevated white blood cell, neutrophil, NLR and PLR in contrast to the cohort without MACE, whereas the lymphocyte count was markedly diminished (p<0.05).

Table 2. Analysis of blood routine indicators in STEMI patients.

Variables	Non-MACE group (N=1011)	MACE group (N=167)	P value
WCC (×109/L)	9.3 (7.4, 11.4)	10.6 (8.4, 13.8)	<0.001
Lymphocytes (×109/L)	1.3 (0.9，1.8)	1.1 (0.7，1.6)	<0.001
Neutrophil (×109/L)	7.3 (5.3，9.4)	8.6 (6.0，12.0)	<0.002
Monocyte (×109/L)	0.5 (0.4，0.6)	0.5 (0.4，0.7)	0.090
Platelet (×109/L)	218.0 (179.0，257.0)	210.0 (173.0，274.0)	0.700
NLR	5.6 (3.4, 8.8)	8.1 (4.4, 13.5)	<0.001
PLR	163.4 (118.8, 229.4)	196.7 (130.1, 292.0)	0.002

The data are shown as median （Q1, Q3）.

MACEmajor adverse cardiovascular eventNLRneutrophil to lymphocyte ratioPLRplatelet to lymphocyte ratioSTEMIST-segment elevation myocardial infarctionWCCwhite cell count

ROC curve to determine the optimal cut-off value

The ROC curve analysis determined the optimal threshold values for NLR and PLR, which were subsequently classified into low and high groups based on these thresholds. The NLR was categorised into low (NLR<7.45) and high (NLR≥7.45) groups, while PLR was categorised into low (PLR<189.15) and high (PLR≥189.15) groups.The analytical outcomes indicate that the Youden index for NLR is 0.257, with a sensitivity of 0.58 and a specificity of 0.68, while the AUC is 0.63. For PLR, the Youden index is 0.162, with a sensitivity of 0.55 and a specificity of 0.61 and the AUC is 0.58.

Logistic regression analysis of MACEs in STEMI patients

Initially, univariate logistic regression was used to evaluate the association between NLR, PLR and the incidence of MACE during hospitalisation. Subsequently, NLR and PLR were considered as categorical variables, and multivariate logistic regression was implemented to account for potential confounding factors.

On employing univariate logistic regression, it was ascertained that both the NLR and the PLR were identified as risk factors for MACE during hospitalisation. Subsequent multivariate analysis, which accounted for potential confounding variables, corroborated the NLR as an independent predictor of MACE risk. Conversely, no significant correlation was discerned between PLR and the incidence of MACE within this particular cohort. The comprehensive findings are delineated in table 3.

Table 3. Univariate and multivariate logistic regression analysis.

Categories	Crude model		Adjust model
	OR and 95% CI	P value	OR and 95% CI	P value
Low NLR index	Ref.		Ref.
High NLR index	0.971 (0.638 to 1.303)	<0.001	1.767 (1.223 to 2.551)	0.002
Low PLR index	Ref.		Ref.
High PLR index	0.002 (0.001 to 0.003)	0.003	1.001 (1.000 to 1.002)	0.137

Note:Covariates were described in the part of ‘Sstatistical Aanalysis’.

NLRneutrophil to lymphocyte ratioPLRplatelet-lymphocyte ratio

Logistic regression risk prediction model

The novel composite index was formulated by amalgamating both the NLR and PLR, with the GRACE score, derived from a multiple logistic regression analysis, to evaluate its effectiveness in forecasting in-hospital risk for MACE. The analysis indicated that the integration of NLR and PLR augmented the predictive precision of the GRACE score, with their combined application resulting in a 0.008 increase in the C index (p<0.05). Although the individual addition of NLR and PLR to the GRACE score improved its NRI and IDI, the concurrent utilisation of both inflammatory markers yielded a more significant enhancement in predictive efficacy. The comprehensive outcomes are detailed in table 4.

Table 4. Impact of NLR and PLR on GRACE score prediction performance.

	AUC (95% CI)	P value	NRI	P value	IDI	P value
GRACE score	0.731 (0.690 to 0.772)	0.021	Ref.		Ref.
GRACE score+NLR	0.737 (0.696 to 0.777)	0.020	0.595	<0.001	0.090	<0.001
GRACE score+PLR	0.732 (0.691 to 0.772)	0.021	0.636	<0.001	0.087	<0.001
GRACE score+NLR+PLR	0.739 (0.697 to 0.779)	0.021	0.661	<0.001	0.096	<0.001

AUCarea under curveGRACEGlobal Registry of Acute Coronary EventsIDIintegrated discrimination improvementNLRneutrophil-to-lymphocyte ratioNRINet Reclassification ImprovementPLRplatelet-to-lymphocyte ratio

It is concluded that the NLR independently forecasts the likelihood of major adverse cardiovascular events (MACE) occurring during hospitalisation among the studied patients. The integration of both PLR and NLR with the GRACE score augmented its predictive precision for the risk of in-hospital MACE within this patient population.

Discussion

AMI poses a significant threat to global health. Epidemiological studies have confirmed that a multitude of factors influence the prognosis of patients who have experienced myocardial infarction. These factors include the age of the patient, pre-existing medical conditions, the location and extent of the myocardial infarction, and the therapeutic approach selected,¹³ and the interval between the onset of myocardial infarction and the establishment of vascular patency.¹⁴ The foundational analysis of this study indicated that elderly patients diagnosed with atrial fibrillation, fundamental renal insufficiency, disorders of glucose and lipid metabolism, and diminished ejection fraction exhibited an elevated risk for MACE. Within this cohort, advanced age, renal insufficiency and disruptions in glucose and lipid metabolism could exacerbate the severity of arteriosclerosis and microcirculatory dysfunction.¹⁵ Consequently, the heart’s compensatory mechanisms are compromised. A diminished ejection fraction indicates a significant impairment in cardiac function, which may be linked to critical factors such as the infarct’s location, the infarct’s size and the extended duration of vascular patency. These factors are closely associated with the onset of an adverse prognosis, corroborating the results of previous studies.^{16 17}

The innate immunity driven by neutrophils and the adaptive immunity mediated by lymphocytes are both essential components in the development of atherosclerosis and the rupture of plaques. Prior research has identified a positive correlation between heightened neutrophil counts and adverse outcomes in patients experiencing AMI,¹⁸ providing substantial contributions to the stratification of risk in myocardial infarction.¹⁹ Lymphocytes, which possess proinflammatory and proatherosclerotic properties, experience apoptosis during AMI. The liberation of proinflammatory cytokines in response to stress exacerbates the reduction in their numbers. A diminished lymphocyte count has been associated with a heightened risk of adverse outcomes among patients suffering from myocardial infarction.²⁰ The NLR, which amalgamates the contributions of neutrophils and lymphocytes, functions as a composite indicator of both innate and adaptive immune responses. This ratio offers a more comprehensive and precise evaluation of the patient’s inflammatory and stress status. In recent years, the NLR has been incorporated into the Naples Score (NS),²¹ a metric that synthesises the extent of inflammation and the nutritional status of patients. It is frequently employed to predict the long-term prognosis of individuals diagnosed with cancer. Numerous contemporary studies have corroborated the efficacy of the NS in stratifying risk for long-term mortality among patients with STEMI undergoing primary PCI, as well as in forecasting long-term mortality in patients with pulmonary embolism.²² The research outcomes suggest that the NLR acts as an independent predictor of MACE during hospitalisation and augments the predictive power of the GRACE score when used as an auxiliary marker. Given its superior practicality, extensive applicability and minimal cost of assessment, the NLR can function as an auxiliary to conventional risk factors in clinical practice, facilitating risk evaluation and personalised rehabilitation strategies for patients with STEMI, especially within primary healthcare environments. This highlights its considerable significance in hospital contexts, corroborating the results of numerous domestic and international studies.^{23 24}

Contemporary research has confirmed the pivotal role of platelets in the initiation and advancement of atherosclerosis. The interplay between platelets and fibrin constitutes the bedrock of coronary thrombosis.²⁵ Additionally, platelets that have been activated stimulate the adhesion and migration of monocytes through the induction of inflammatory factor secretion by endothelial cells and leucocytes. This process augments the recruitment of circulating leukocytes to areas of vascular endothelial damage, thereby initiating and accelerating the progression of atherosclerosis. The interplay among platelets, leucocytes and endothelial cells plays a significant role in the development of unstable plaque.²⁶ There exists a strong correlation between elevated platelet levels and the incidence of coronary artery restenosis as well as stent thrombosis.²⁷ Furthermore, thrombocyte count has been associated with both immediate and prolonged mortality among patients experiencing STEMI.²⁸ The PLR, a composite index that integrates platelet and lymphocyte counts, provides a more accurate representation of platelet activation and the prothrombotic condition.

Previous research has demonstrated a direct correlation between increased PLR and an unfavourable prognosis in patients with STEMI.²⁹ Additionally, an elevated PLR in individuals subjected to primary PCI subsequent to myocardial infarction has been identified as an independent predictor of no-reflow phenomenon, elevated SYNTAX score, suboptimal myocardial reperfusion and adverse in-hospital events.^{7 30 31} A prospective investigation conducted by Lee et al indicated that an elevated PLR was predictive of long-term all-cause mortality in individuals with high-risk coronary artery disease subsequent to coronary angiography.³² Nevertheless, alternative research indicates that the predictive efficacy of PLR for unfavourable cardiovascular outcomes in patients who have experienced myocardial infarction is less robust than that of NLR, thereby suggesting its avoidance as the sole prognostic indicator in such instances.^{33 34} The current investigation substantiated that although elevated PLR levels were associated with in-hospital MACEs in patients experiencing myocardial infarction, they did not constitute an independent risk factor.

Empirical evidence derived from clinical practice has indicated that a multitude of scoring systems are adept at evaluating the prognosis of patients suffering from myocardial infarction. Notably, the Thrombolysis in Myocardial Infarction (TIMI Score), Intermountain Risk Score (IMRS score)³⁵ and GRACE score are commonly used. The TIMI score is distinguished by its simplicity and ease of application, facilitating the swift identification of high-risk patients presenting with acute chest pain,³⁶ whereas the IMRS score demonstrates a more pronounced predictive efficacy in patients with myocardial infarction complicated by shock.³⁷ Notwithstanding the commendable predictive capabilities of these scoring systems for forecasting short-term and long-term mortality risks in patients experiencing myocardial infarction, extensive prospective clinical studies have demonstrated that the GRACE risk score exhibits superior precision and a more comprehensive scope of applicability in evaluating the prognosis of myocardial infarction patients. ³⁸ The recently revised GRACE 2.0 has significantly broadened the scope of application for this scoring system. Following the update, the GRACE score is now relevant not only to individuals suffering from myocardial infarction but also to those experiencing unstable angina pectoris. Its evaluative capabilities extend beyond short-term prognosis, and its value in assessing the long-term prognosis of patients with myocardial infarction has been recognised. ³⁹ This investigation further evaluated the impact of NLR, PLR and their combined effect on the predictive precision of the GRACE score through the application of multiple logistic regression models. The findings indicate that the integration of these inflammatory markers augments the predictive capability of the GRACE score, facilitating a more precise identification of in-hospital cardiovascular event risk among STEMI patients. Routine blood tests provide clinicians with a straightforward approach for preliminary risk stratification of STEMI patients, thereby assisting in the optimisation of treatment and rehabilitation strategies.

It is imperative to acknowledge the limitations inherent in this study. Primarily, it was conducted as a retrospective analysis, which inherently imposes certain constraints. Due to the limitations of the data available, the study did not include a comparative evaluation of the fluctuations in GRACE scores before and after PCI. Furthermore, intraoperative prognostic factors such as the time elapsed from symptom onset to the reopening of the vessel, the volume of contrast media used, and the precise surgical techniques employed during PCI were not incorporated into the analysis. Second, the temporal interval between the onset of myocardial infarction and hospital admission was not stringently regulated. Although blood samples were procured within 2 hours of admission, there exists a discrepancy between the timing of blood collection and the onset of symptoms. Third, inflammatory markers exhibit dynamic fluctuations as the disease progresses, and this study lacked data pertaining to these dynamic alterations. Finally, the study did not undertake long-term follow-up, thereby limiting the capacity to evaluate the impact of the inflammatory markers on long-term patient outcomes. To enhance the understanding of the correlation between these inflammatory markers and long-term prognosis in STEMI patients, multicentre prospective studies are indicated.

Conclusions

The NLR possesses the capability to autonomously predict the risk of in-hospital mortality from any cause, recurrence of myocardial infarction, acute heart failure, cardiogenic shock, malignant arrhythmia and cardiac arrest among patients diagnosed with STEMI. Furthermore, it serves as a composite metric that significantly augments the predictive precision of the GRACE score. The amalgamation of NLR and PLR with the GRACE score establishes a novel scoring system that provides enhanced risk stratification for in-hospital MACE. As a non-invasive and readily accessible biomarker, it offers significant insight for clinicians in evaluating the short-term prognosis of patients, enabling primary care providers to more effectively identify high-risk STEMI cases and enhance patient outcomes.

Data availability statement

Data are available on reasonable request.