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The progress in medical advancements has led to significant improvements in the treatment outcomes of patients with cardiovascular diseases (CVD); however, globally, CVD-related deaths continue to represent a substantial proportion.1,2 Research findings indicate that in 2019, non-communicable diseases accounted for 74.3% of the total global mortality, totaling 42.03 million deaths. Among non-communicable diseases, CVD emerged as the leading cause of mortality, with CAD being the predominant form of coronary artery disease.2 The occurrence and development of CAD is mainly based on atherosclerosis, which mainly causes coronary artery stenosis and obstruction due to atherosclerosis and thrombosis, etc., resulting in patients’ morbidity.3 Acute myocardial infarction (AMI) is an acute and critical event in CAD, characterized by sudden onset, severe condition and high risk of death.4 Although current treatments, such as surgery and thrombectomy, have reduced patient mortality, the outcomes for AMI patients are still less than ideal. Therefore, for the diagnosis, treatment and prognosis of AMI patients, early detection of the patient’s condition is of great importance.
Atherosclerosis (AS) represents the foundation and primary risk factor for AMI.5,6 The accumulation of cholesterol within the inner wall of blood vessels, the formation of foam cells, and the sustained inflammatory response are pivotal processes in the genesis and progression of atherosclerosis.7,8 Neutrophils, monocytes, platelets, and lymphocytes have been identified as playing pathogenic roles in the inflammatory response, which is a key factor in the pathogenesis of AS.7,9 Conversely, HDL exerts a protective effect on the occurrence and development of AS and is inversely correlated with the risk of CAD.9,10 Consequently, a novel biomarker is constructed based on blood cell count and its subtypes (neutrophils, monocytes, platelets, and lymphocytes), HDL, and other hematological detection indicators, which fully reflects the inflammatory response of the body. This biomarker is advantageous in that it is convenient for clinical multidimensional diagnosis of patients and does not increase the economic burden of patients. Consequently, novel inflammatory markers, including the systemic inflammatory response index (SIRI), systemic inflammatory immune index (SII), monocyte-to-high-density lipoprotein ratio (MHR), and neutrophil-to-high-density lipoprotein ratio (NHR), have emerged as valuable tools in the investigation of cardiovascular diseases in recent years.11–13 SIRI and SII are novel and stable inflammatory markers that can reflect systemic inflammatory response and local immune response of the human body.14,15 SIRI mainly includes neutrophils, monocytes and lymphocytes, and has been identified as an independent risk factor for major adverse cardiovascular events (MACE) in patients with acute coronary syndrome following interventional therapy.16,17 SII encompasses three blood cell indices, namely neutrophils, monocytes, and platelets. Elevated SII levels have been linked to an increased risk of coronary heart disease.18,19 MHR mainly includes monocyte and HDL, which are considered as biomarkers of oxidative stress in inflammatory state, and independent predictors of AMI occurrence and major adverse cardiovascular events.20,21 NHR, which mainly includes neutrophils and HDL, has been widely reported as an emerging indicator of inflammation and oxidative stress, and has predictive value for long-term mortality and recurrent myocardial infarction in elderly patients with AMI.22
The occurrence and development of AMI are closely related to abnormal inflammatory cells and lipid metabolism. Studies have shown that novel inflammatory markers derived from blood cells, such as neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR), are associated with MACE in AMI patients.23,24 However, in this study, SIRI and SII contained more indicators related to the occurrence and development of AS, while MHR and NHR contained lipid-related indicators, which were more adequate than the single blood cell indicators of NLR and PLR. New inflammatory markers, including SIRI, SII, MHR and NHR, have been found to be closely associated with cardiovascular diseases. Nevertheless, the aforementioned four indicators have not yet been subjected to comprehensive investigation in the context of AMI. Therefore, the objective of this study is to observe the relationship between SIRI, SII, MHR, and NHR and AMI, as well as to analyze the relationship between them and the degree of coronary artery disease. The findings of this study will provide a reference for early clinical diagnosis and treatment of AMI and will also assist in the assessment of the degree of coronary artery disease in patients.
Materials and MethodsStudy Population and Data CollectionThis retrospective study of 916 specimens was obtained from Xi’an International Medical Centre (1 January 2022 to 31 December 2023), China. The control population consisted of 510 cases from the hospital’s health screening center. 406 patients in the AMI (acute myocardial infarction) group were from the hospital’s cardiovascular department. The sample size was estimated by using JMP® Trial 18.0.1 software (JMP Statistical Discovery LLC., USA). Considering a 95% (α=0.05) confidence interval, and a 90% (1-β) power to detect a difference of 0.25 units, the calculated sample size is 677. The total sample size of this study was 916, which met the requirement of 90% efficacy. The diagnosis of acute myocardial infarction is mainly based on the Fourth Universal Definition of Myocardial Infarction (2018) and the criteria recommended by the American College of Cardiology guideline.25,26 Inclusion criteria: AMI was diagnosed; first onset and symptoms within 12h; coronary angiography was performed in all patients. Exclusion criteria: prior coronary artery bypass grafting or coronary artery stenting; combined with valvular disease, congenital heart disease, pulmonary heart disease, cardiomyopathy, etc.; primary hepatic and renal dysfunction; suffering from severe infections, malignant tumors, rheumatic diseases, etc.; diseases of the blood system; have undergone bone marrow or organ transplantation; long-term use of anticoagulant drugs and other drugs that affect blood test results.
The clinical data of the study subjects were collected, including sex, age, blood routine, blood lipids and other indicators. The Gensini score of each AMI patient was calculated based on the coronary angiography results (the results were obtained from the patients’ hospital records). For details of the Gensini score, see a guide for Gensini score calculation.27 Patients in the AMI group were divided into mild group (Gensini score < 30), moderate group (30 ≤ Gensini score ≤ 60) and severe group (Gensini score > 60) according to the tertiles of Gensini score.
As this study was a retrospective analysis, informed consent from participants could not be obtained, and received an exemption from the hospital ethics committee. This study was approved by Xi’an International Medical Center Hospital Ethics Committee (GJYX-KY-2024-015), and its protocol abided by the principles of the Declaration of Helsinki.
Detection of Blood Markers and Calculation of the Inflammatory IndexMonocytes, lymphocytes, platelets, and neutrophils were measured using a standard automated haematology analyser (which can count and sort different types of cells in the blood) (SYSMEX-XN9000, Japan). Triglycerides, total cholesterol, HDL, LDL and blood glucose (glucose oxidase method) were measured using a blood biochemistry analyser (which allows blood samples to be taken and chemically analysed) (HITACHI-008AS, Japan). Left Ventricular Ejection Fractions (LVEF%) was measured by echocardiography.
The inflammatory index calculation: SII= neutrophil count (×109/L) ×platelet count (×109/L)/lymphocyte count (×109/L); SIRI =neutrophil count (×109/L) × monocyte count (×109/L)/lymphocyte count (×109/L); MHR =monocyte (×109/L)/high-density-lipoprotein cholesterol (mmol/L) ratio; NHR= neutrophil count (×109/L) /high-density-lipoprotein cholesterol (mmol/L) ratio.
Statistical AnalysisSPSS 22.0 and R 4.3.3 were used for statistical analysis of the study. Mean ± standard deviation (
) was used for normally distributed information, interquartile range [M (Q3–Q1)] for non-normally distributed information, and percentages for categorical variables. The independent samples t-test was used to compare normally distributed quantitative variables in the two samples. Non-normally distributed quantitative variables in the two samples were compared using two-sample non-parametric tests. One-way-ANOVA was used to compare normally distributed multiple samples and SNK test was used to compare two groups. Non-normally distributed samples were compared using non-parametric tests. Chi-squared test was used to compare rates of categorical variables. Correlations between the four inflammatory indices and Gensini scores were analysed using Spearman rank correlation. The outcome variable of this study was the degree of coronary artery lesions, which was divided into three grades: mild, moderate, and severe, so this study used multi-factor ordinal logistic regression to analyze the influencing factors of the degree of coronary artery lesions. The predictive value of the four inflammatory indices for AMI and the degree of coronary artery disease was analysed using ROC curves. A nomogram was used to generate a predictive model for the extent of AMI coronary lesions using the four inflammatory indices. The accuracy of the nomogram was assessed using calibration curves.
As shown in Table 1, the mean age of the control group was 59.19±7.25 years, and the mean age of the AMI group was 60.08±6.94 years. The proportion of males in the control and AMI groups was 65.69% and 64.29%, respectively. There was no significant difference in age and sex between the AMI group and the control group (P>0.05). There was no significant difference in PLT, TC, LDL and TG between AMI group and control group (P>0.05). Compared with the control group, the total amount of MONO and NEUT in the blood of the AMI group was significantly higher than that of the control group, and the total amount of LYMP was significantly lower than that of the control group (P<0.05). Blood HDL content was significantly lower in the AMI group than in the control group (P<0.05). The inflammation index SII, SIRI, MHR and NHR values of the AMI group were significantly higher than those of the control group (P<0.05).
Table 1 Demographic Data and Clinical Characteristics of Control and AMI Patients [(
), M (Q3–Q1)]
406 patients with AMI were divided into three groups according to the Gensini score. The mean age of the three groups was 61.70±7.56, 63.39±9.23 and 61.64±3.46 years, respectively. The proportion of males in each group was 67.48%, 69.57% and 69.66%, respectively. There was no significant difference in age and sex between the three groups (P>0.05). There was no significant difference in the proportions of hypertension and diabetes among the three groups (P>0.05). There was no significant difference in heart rate, glucose, PLT, MONO and TG among the three groups (P>0.05). Comparison of smoking and Killip (II-IV) ratio among the three groups showed statistically significant differences, and with increasing Gensini score, smoking and Killip (II-IV) ratio showed an increasing trend (P<0.05). LVEF%, LYMP, NEUT, LDL, HDL and TC values of the three groups were compared and the differences were statistically significant (P<0.05). With increasing Gensini score, LVEF%, LYMP and HDL showed a decreasing trend, whereas NEUT, LDL and TC showed an increasing trend (P<0.05). The inflammatory indices SII, SIRI, MHR and NHR of the three groups were compared and the differences were statistically significant, and with the increase in Gensini score the four inflammatory indices showed an upward trend (P<0.05, Table 2).
Table 2 Comparison of Clinical Features of AMI Patients with Different Gensini Scores [(
), M (Q3–Q1)]
Spearman correlation analysis of SII, SIRI, MHR and NHR with Gensini scores in AMI patients showed a positive correlation (P<0.05) between SII, SIRI, MHR and NHR with Gensini scores in AMI patients. The rank correlation coefficients of SII, SIRI, MHR and NHR with Gensini scores were 0.3244, 0.2767, 0.2846 and 0.3146, respectively (Figure 1).
Figure 1 Correlation analysis of SII, SIRI, MHR and NHR with Gensini sore. (A) Correlation analysis of SII with Gensini sore; (B) Correlation analysis of SIRI with Gensini sore; (C) Correlation analysis of MHR with Gensini sore; (D) Correlation analysis of NHR with Gensini sore.
Abbreviations: SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; MHR, monocyte-to-high-density lipoprotein ratio; NHR, Neutrophil to-high-density lipoprotein ratio.
The Factors Influencing the Degree of Coronary Lesion in AMI Were Analyzed by Multi-Factor Ordinal Logistic RegressionThe results of the parallelism hypothesis test showed that P=0.249>0.1, which was consistent with proportional dominance, ie, the individual regression equations of each model were parallel and satisfied the prerequisites for ordered logistic regression analysis. The influential factors related to the degree of coronary lesion in AMI in univariate analysis were taken as independent variables, and the degree of coronary lesion in AMI was taken as dependent variable and included in the multi-factor ordered classification logistic regression model. The results showed that Killip grade and the new inflammation indicators SII, SIRI and MHR were independent risk factors for the degree of coronary lesion in AMI (P<0.05). LVEF% was a protective factor for the degree of coronary lesion in AMI (P<0.05). The goodness of fit test results (χ2=181.46, P=0.005) showed that the model had a good goodness of fit (Table 3).
Table 3 Multi-Factor Ordinal Logistic Regression for the Degree of Coronary Lesion in AMI
ROC Curve AnalysisROC Curve Analysis of SII, SIRI, MHR and NHR for AMIROC curve to assess the predictive value of the novel inflammatory indicators SII, SIRI, MHR and NHR for AMI, the results showed that the AUCs of SII, SIRI, MHR and NHR were 0.896, 0.972, 0.952 and 0.957, respectively (P<0.05); the optimal cutoffs were 557.22, 1.39, 0.53 and 4.66, respectively (P<0.05); and the combined detection of the four inflammation indicators of AUC was 0.984, sensitivity was 95.64% and specificity was 91.72% (Table 4, P<0.05). The value of applying the four inflammatory indicators to predict the occurrence of AMI was high (Figure 2, P<0.05).
Table 4 ROC Curve Analysis for AMI
Figure 2 ROC curve analysis for AMI.
Abbreviations: AUC, Area under the curve; SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; MHR, monocyte-to-high-density lipoprotein ratio; NHR, Neutrophil to-high-density lipoprotein ratio.
ROC Curve Analysis of SII, SIRI, MHR and NHR for Degree of Coronary Lesions in AMIThe predictive value of the novel inflammatory markers SII, SIRI, MHR and NHR for the degree of AMI was evaluated using ROC curves with the degree of coronary artery disease as the outcome variable. The results of ROC analysis showed that the AUCs of SII, SIRI, MHR and NHR in the mild group were 0.673, 0.642, 0.677 and 0.661, respectively, and the AUCs of the combined test in the mild group were 0.738, with a sensitivity of 78.45% and a specificity of 59.44% (P<0.05). The AUC of SII, SIRI, MHR and NHR for the severe group were 0.650, 0.627, 0.609 and 0.638 respectively (P<0.05); the AUC of the four inflammation indicators for the severe group was 0.694 (P<0.05). The AUC of the four inflammation indicators for the severe group was 0.694, the sensitivity was 46.94% and the specificity was 87.43% (P<0.05); SII, SIRI, MHR and NHR had no predictive value for the moderate group (P>0.05). The four inflammatory indices had some predictive value for the mild group and the severe group in the degree of coronary lesions in AMI (Table 5 and Figure 3, P<0.05).
Table 5 ROC Curve Analysis for Degree of Coronary Lesions in AMI
Figure 3 ROC curve for degree of coronary lesions in AMI. (A) Mild group; (B) Moderate group; (C) Severe group.
Abbreviations: AUC, Area under the curve; SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; MHR, monocyte-to-high-density lipoprotein ratio; NHR, Neutrophil to-high-density lipoprotein ratio.
Risk Prediction of AMI Coronary Lesion DegreeThe SII, SIRI, MHR and NHR in the risk factor model for degree of AMI coronary lesions were output as a nomogram (Figure 4) by R Studio software, which allowed prediction of the degree of AMI coronary lesions. The inflammation indices SII, SIRI and MHR had a high predictive value for the extent of coronary artery disease in AMI, whereas NHR had a low predictive value. The accuracy of the nomogram in predicting the degree of AMI coronary lesions was assessed using calibration curves (Figure 5). The predictive ability of the model was assessed by repeated sampling of the original data 500 times using the bootstrap internal validation method, which showed a mean absolute error of 0.013, and the calibration curve converged to the ideal curve (mean absolute error <0.05). Each variable in the nomogram has a corresponding individual score, and the sum of all individual scores, projected onto the total score axis, is the predicted probability of risk. As shown in Figure 4, the mean absolute error of the calibration curve was less than 0.05, indicating that the nomogram constructed in this study predicted the risk of individual AMI coronary lesion extent with a good fit to the actual observed individual risket al the accuracy of the nomogram was good.
Figure 4 Nomogram for predicting the risk of AMI from coronary lesions. Y=1 represents the group is mild group; Y=2 represents the group is moderate group; Y=3 represents the group is severe group.
Abbreviations: SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; MHR, monocyte-to-high-density lipoprotein ratio; NHR, Neutrophil to-high-density lipoprotein ratio.
Figure 5 Calibration curves for predicting the extent of coronary lesions in AMI using a nomogram. B=500 repetitions, boot, mean absolute error=0.013, n=406. The mean absolute error is less than 0.05, indicating that the predicted probability of the degree of coronary lesions in AMI in the model is in good agreement with the actual probability.
DiscussionAMI is a form of irreversible myocardial injury that is primarily caused by acute coronary artery infarction. This results in ischemia and hypoxia of distal blood vessels and myocardium, which in turn causes persistent severe retrosternal pain.4 The continuous ischemia and hypoxia of myocardial tissue can lead to a series of inflammatory reactions, including the infiltration of a large number of inflammatory cells, the release of a large number of inflammatory factors, and the further aggravation of myocardial damage.28 The results of this study demonstrated that the number of monocytes and neutrophils in the blood of AMI patients was significantly increased in comparison to healthy subjects, while the number of lymphocytes and the level of HDL were significantly decreased. This finding is consistent with previous research.7,9,29 It is postulated that AMI patients may exhibit an overt inflammatory response, accompanied by a deficiency in anti-inflammatory substances such as HDL.29
The pathogenesis of AMI is influenced by the actions of immune cells and inflammatory processes. Studies have demonstrated that the number of white blood cells and the level of C-reactive protein in patients with coronary heart disease are significantly elevated.30 These factors are associated with the severity of coronary heart disease, the stability of atherosclerotic plaques, and patient mortality.30 Additionally, higher levels of inflammatory markers are predictive of a higher risk of cardiovascular disease.31 In patients presenting with chest pain, it is of the utmost importance to assess the severity of coronary artery disease in order to determine the optimal treatment and stratified management of associated disease risk.32 White blood cells and their classification subgroups (neutrophils, monocytes, lymphocytes, etc) are accessible clinical test results that can be used to assess the level of inflammation in patients with coronary artery disease,33 and elevated WBC levels are independent predictors of mortality in AMI patients.34 Neutrophils represent a significant subgroup of white blood cells and have been demonstrated to play a pivotal role in the body’s inflammatory response.35 Atherosclerosis studies have demonstrated that neutrophils can induce smooth muscle cell dissolution and death, thereby exacerbating inflammation.36 Furthermore, a considerable number of neutrophils are present in coronary artery diseases,37 and elevated myeloperoxidase levels of neutrophils may also contribute to the development of coronary atherosclerosis.38 Monocytes, a unique subset of white blood cells, infiltrate endothelial cells and differentiate into macrophages. These macrophages then phagocytose lipids, forming foam cells. This process results in the release of pro-inflammatory factors, reactive oxygen species, and proteolytic enzymes, which further exacerbate atherosclerotic inflammation.39,40 Studies have demonstrated a correlation between the number of monocytes in the peripheral blood and the progression of coronary plaques following an AMI. The peak value of these monocytes is the most reliable predictor of plaque progression.41 Lymphocytes are also an indispensable part of chronic inflammation in atherosclerosis. They can infiltrate into ischemic myocaroma and induce monocyte infiltration by secreting interleukin-10. Decreased peripheral blood count is positively correlated with MACE and poor prognosis of patients.42,43 The platelet content is a crucial indicator in conventional blood cell counts, exhibiting a dual role in atherosclerosis. On the one hand, it accelerates plaque formation through adhesion to the inner wall of blood vessels.44 On the other hand, it promotes inflammation and thrombosis.44,45 Furthermore, it is evident that aberrant lipid metabolism plays a pivotal role in the etiology of AMI. A significant proportion of patients with coronary heart disease are found to be primarily affected by abnormal blood lipid levels, among these, HDL is observed to play an antagonistic role in atherosclerosis, as evidenced by studies.46 All of the aforementioned indicators play a significant role in the diagnosis of CAD, yet their individual contributions are not without limitations. In recent years, the concept of new inflammatory markers based on blood cell subsets and lipids has provided a new and more comprehensive research direction for medical researchers. SIRI and SII are primarily based on blood cell subsets, while MHR and NHR are based on blood cell subsets and lipids. The results of this study demonstrated that the levels of SIRI, SII, MHR, and NHR in patients with AMI were significantly higher than those in healthy subjects. The results indicated that AMI patients were in a state of obvious inflammation, which was consistent with previous reports.14,20,22 The results of the ROC curve analysis demonstrated that the AUC values for SII, SIRI, MHR, and NHR were 0.896, 0.972, 0.952, and 0.957, respectively (P < 0.05). The optimal critical values were determined to be 557.22, 1.39, 0.53, and 4.66, respectively. The area under the curve (AUC) of the four inflammatory indexes was 0.984, with a sensitivity of 95.64% and a specificity of 91.72%. It is proposed that SII, SIRI, MHR and NHR may be inflammatory markers of AMI and have high predictive value for the occurrence of AMI. In particular, the prediction ability of the four combined tests is obviously better than that of the four independent tests.
The Gensini score is an efficacious index for the evaluation of the extent of coronary artery disease.47 The use of varying weight coefficients based on the severity of coronary artery branches allows for a more objective and comprehensive reflection of the disease’s severity.47,48 Accordingly, in this study, AMI patients were divided into three groups based on their Gensini scores: mild, moderate, and severe. The study then observed and compared the differences in general clinical data and SIRI, SII, MHR, and NHR between the three groups. The results demonstrated a downward trend in LVEF%, LYMP and HDL with increasing Gensini score, while NEUT, LDL and TC exhibited an upward trend. Concomitantly, the levels of SII, SIRI, MHR, and NHR exhibited an upward trend. It can be postulated that as the severity of coronary artery disease increases, the prevalence of abnormal blood lipid metabolism in AMI patients becomes more pronounced, accompanied by elevated levels of inflammation.
Previous studies have demonstrated that SII is a risk factor for CHD and its severity, and that it is positively correlated with the severity of stable CHD patients using the SYNTAX score.19,49 In a study conducted by Jin et al, 85,000 respondents were observed for a period of 10 years, the results demonstrated that in patients under the age of 60, an increased SIRI was positively correlated with a higher incidence of acute coronary syndrome.50 In addition, MHR and NHR are also associated with the risk of cardiovascular disease.51,52 The results of the multi-factor ordered logistic regression analysis indicated that Killip grade, SII, SIRI, and MHR were independent risk factors for the degree of coronary artery disease in AMI. NHR has not been demonstrated to be an independent risk factor for the degree of coronary artery disease in AMI, potentially due to the fact that neutrophils are not a significant factor in the formation of atherosclerosis, while monocytes are a major cell in the formation of AS and a predictor of the occurrence of coronary artery disease.53,54 ROC was employed to assess the predictive value of novel inflammatory markers on AMI lesion severity. The results demonstrated that the AUC of SII, SIRI, MHR, and NHR in the mild group were 0.673, 0.642, 0.677, and 0.661, respectively. Additionally, the AUC of the combined detection of the four indicators in the mild group was 0.738. The sensitivity and specificity were 78.45% and 59.44%, respectively. The AUC of SII, SIRI, MHR, and NHR in the severe group were 0.650, 0.627, 0.609, and 0.638, respectively (P < 0.05). The AUC of the four inflammatory indexes was 0.694, with a sensitivity of 46.94% and a specificity of 87.43% (P < 0.05). The SII, SIRI, MHR, and NHR did not demonstrate predictive value for the moderate group. Furthermore, Spearman correlation analysis demonstrated a positive correlation between the levels of SII, SIRI, MHR, and NHR in AMI patients and Gensini scores. It is proposed that SII, SIRI, MHR, and NHR may be useful in predicting the extent of coronary artery disease in AMI patients. The higher the SII, SIRI, MHR, and NHR, the more severe the coronary artery disease in AMI patients may be. This can provide a reference for clinicians to judge the condition of patients as early as possible.
The nomogram model, which is based on the core diagnostic indicators, can be utilized for the purpose of patient risk assessment and the early diagnosis of disease. The nomogram model has been utilized in the context of a diverse array of diseases, including pregnancy-induced hypertension,55 early-stage diabetic nephropathy,56 and coronary artery disease.57 In this study, the risk factor model of AMI coronary artery disease degree was output into a column graph by R Studio software, which enabled the degree of AMI coronary artery disease to be predicted. The SII, SIRI, and MHR were found to be effective in predicting the degree of coronary artery disease in AMI, while the NHR was found to be less effective in this regard. Calibration curves were employed to assess the precision of the nomogram in forecasting the severity of coronary artery disease in AMI. A re-sampling method was employed to conduct 500 iterations of the original data set, thereby enabling an assessment of the model’s predictive capabilities. The results demonstrated that the mean absolute error was 0.013, and the calibration curve converged to the ideal curve (mean absolute error <0.05). Each variable in the column chart is assigned a corresponding individual score, and the sum of all individual scores is projected onto the total score axis, which represents the predicted risk probability. The mean absolute error of the calibration curve was less than 0.05, indicating that the nomogram constructed in this study for predicting the risk of coronary artery lesions in individuals with AMI is an accurate representation of the actual observed individual risk.
In conclusion, the elevated levels of novel inflammatory markers SIRI, SII, MHR, and NHR in AMI patients are positively correlated with the degree of coronary artery disease, which is an independent risk factor for the severity of coronary artery disease in AMI patients and has predictive value to a certain extent. This provides a reference for the early clinical diagnosis and treatment of AMI, as well as for the assessment of the degree of coronary artery disease. Concurrently, the nomogram model proposed in this study exhibits satisfactory predictive capacity, thereby offering a reference for early clinical estimation of the degree of coronary artery disease in AMI patients. It should be noted that this study is not without limitations. The four inflammatory indicators demonstrated no predictive value in the moderate group, which may be attributed to differences in sample size, study design, overall selection, statistical methods, and outcome measurement. In addition, this study did not include SYNTAX score and did not distinguish NSTEMI from STEMI, which limited the comprehensiveness of the conclusions of this study. In the future, we will also expand the sample size and improve the research methods in order to provide a more comprehensive reference for clinical diagnosis and treatment of AMI. Furthermore, the retrospective nature of the study and single center study limit the persuasiveness of the results. Additionally, the dynamic changes of various indicators could not be comprehensively observed.
Author ContributionsHui Sun and Haiying Liu share first authorship. All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
FundingNo funding was received.
DisclosureThe authors declare that they have no conflict of interest.
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