Predictive Value of Noninvasive Cardiac Function Monitoring Combined With GRACE Score for Short‐Term Outcomes in Patients With ST‐Segment Elevation Myocardial Infarction

Abstract

ABSTRACT

Objective

To investigate the correlation between non‐invasive cardiac function monitoring indexes and recent adverse prognosis in patients with STEMI. The hemodynamic indexes with high diagnostic value were selected to construct a new risk prediction model combined with GRACE scores, and the efficiency of the new prediction model was evaluated.

Methods

STEMI patients who met the inclusion and exclusion criteria were selected. All patients were followed for 6 months of major adverse cardiovascular events (MACE). The non‐invasive cardiac function monitoring indexes were analyzed by univariate and multivariate logistic regression. The ROC curve was used to evaluate the accuracy of non‐invasive cardiac function indexes predicting MACE. Then, a new risk prediction model was established and its prediction efficiency was evaluated by ROC curve.

Results

Patients were divided into MACE group (N = 69) and non‐MACE group (N = 173), stroke volume (SV), cardiac output (CO), cardiac index (CI), cardiac time intervals (CTI), early diastolic filling rate (EDFR), end‐diastolic volume (EDV), and systemic vascular resistance (SVR) were found to be significant predictors of recent MACE events in STEMI patients. Multivariate logistic regression analysis confirmed that indicators of noninvasive cardiac function were independent predictors. In addition, the combination of SV and CTI with the GRACE score has the potential to enhance the predictive accuracy for MACE.

Conclusion

Non‐invasive hemodynamic indicators SV, CO, CI, CTI, EDFR, EDV and SVR can not only independently predict the risk of recent MACE in patients with STEMI, but can also be used as joint indicators to significantly improve the predictive ability of GRACE score.

Introduction

Acute ST-segment elevation myocardial infarction (STEMI), as the most common cardiovascular critical disease, is a serious threat to human health due to its high morbidity and mortality rate (Zhang et al. 2022). The main pathological features of STEMI are the instability and vulnerability of vascular plaque, and percutaneous coronary intervention (PCI) is the most direct and effective treatment option for patients with STEMI (Biscaglia et al. 2023), However, patients may still face the risk of experiencing major adverse cardiovascular events (MACE) following the procedure. Extensive prospective clinical studies have substantiated the superior precision of the Global Registry of Acute Coronary Events (GRACE) risk score in evaluating the risk of myocardial infarction patients, leading to its widespread adoption in clinical settings (Ge et al. 2024) Building upon version 1.0, GRACE 2.0 broadens its applicability to encompass not only myocardial infarction patients but also those with unstable angina, offering the capability to forecast patient mortality at 1 and 3 years post-discharge. (Akyuz et al. 2016; Çınar et al. 2023) Although it plays a certain role in the evaluation of prognosis of patients with acute myocardial infarction, it mostly includes vital signs, blood tests, electrocardiogram and other indicators that indirectly reflect the cardiac injury situation. Therefore, it is particularly important to explore methods that more intuitively and safely assess the risk of MACE after PCI in STEMI patients.

Impedance Cardiography (ICG) is applied to noninvasive cardiac function monitoring to measure left ventricular contraction time and calculate stroke output, cardiac output, cardiac index and other cardiac function related parameters, which can provide data with similar accuracy to that of invasive hemodynamic monitoring, and can realize continuous dynamic monitoring of cardiac function indicators to detect circulatory function abnormalities in early stage. It is an important method to evaluate noninvasive cardiac function after myocardial infarction (Lewicki et al. 2021). The purpose of this study was to explore the predictive value of non-invasive cardiac function monitoring for short-term adverse prognosis in STEMI patients, and to construct a new risk prediction model by selecting hemodynamic indexes with high diagnostic value and combining GRACE scores, and to evaluate the evaluation efficiency of the new prediction model.

Method

Study Population

This study was approved by the Ethics Committee of Tianjin Third Central Hospital. A prospective study method was used to select adult patients who were admitted to the Cardiology Department of Tianjin Third Central Hospital from January 1, 2023, to March 31, 2024. Inclusion criteria: Adult patients aged 18 years or older who fulfill the diagnostic criteria for STEMI as defined by the European Society of Cardiology (2017) (Ibanez et al. 2018): evidence of myocardial injury, defined by elevated troponin levels exceeding the 99% of the upper limit of normal at least once; presence of symptoms indicative of myocardial ischemia; ST-segment elevation in at least two contiguous leads on the electrocardiogram (ST-segment elevation ≥ 0.2 mV in leads V1-V3 and ≥ 0.1 mV in other leads). Coronary angiography and non-invasive cardiac function monitoring were completed within 24 h of admission.

Exclusion criteria: Initially, this study omitted 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. Secondly, individuals diagnosed with hematological 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 omitted. Lastly, individuals who were lost to follow-up were excluded from the study. The exclusion criteria are shown in Figure 1.

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All patients were monitored for a period of 6 months post-discharge to evaluate the occurrence of major adverse cardiovascular events (MACE), encompassing all-cause mortality, recurrent myocardial infarction, acute heart failure, cardiogenic shock, malignant arrhythmia, and cardiac arrest.

Data Collection

According to strict inclusion and exclusion criteria, 242 STEMI patients were enrolled in this study and were divided into MACE group (all-cause mortality 5 cases, recurrent myocardial infarction 6 cases, acute heart failure 34 cases, cardiogenic shock 4 cases, malignant arrhythmia 15 cases, and cardiac arrest 5 cases, totally 69 cases) and non-MACE group (173 cases) according to the occurrence of MACE events during follow-up. The specific study procedures are shown in Figure 1.

Patient demographic data (age, sex, smoking history), pre-hospital cardiac arrest events, and vital signs at admission were extracted from the medical record system. Venous blood was collected within 2 h of admission to collect routine blood, myocardial enzymes, kidney function, electrocardiogram and other data. Fasting peripheral venous blood was collected from the morning of the second day of admission to collect blood lipids, blood sugar and other indicators. Echocardiography was performed within 24 h after admission, and key indicators such as left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDD) and left ventricular end-systolic diameter (LVESD) were recorded.

The GRACE risk score, as outlined by Keith et al. in 2003 (Watts 2013), 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. Data were systematically gathered by a trained clinical researcher using the in-hospital case system.

All patients underwent non-invasive cardiac function monitoring within 24 h of admission. Six monitoring points were selected (sternal Angle, left earlobe, left clavicle and sternocleidomastoid Angle, subxiphoid process and the middle line of the horizontal left and right axils of xiphoid process), and monitoring electrodes were affixed respectively, and wires of corresponding colors were used to connect the corresponding monitoring sites. Stroke volume (SV), cardiac output (CO), cardiac index (CI), contractility index (CTI), early diastolic filling ratio (EDFR), end-diastolic volume (EDV), systemic vascular resistance (SVR) and other indicators were monitored, and all indicators were measured three times and averaged. The ICON noninvasive cardiac output meter used was provided by PhysioFlow.

Statistical Analysis

Statistical analysis was performed using the SPSS 27.0 software. Missing values exceeding 10% in any variable were imputed using multiple interpolation methods. Normally distributed data were reported as mean ± standard deviation, with comparisons made using the independent sample t test. For non-normally distributed data, values were presented as median (P25, P75), and compared by the Mann–Whitney U test. Categorical data were expressed as frequencies (%), and the chi-square or Fisher's exact test was utilized for group comparisons.

Univariate and multivariate logistic regression analysis was performed for non-invasive cardiac function monitoring indicators. Selection of the adjusted variables for the multivariate model adhered to the 10 EPV (Events Per Variable) principle. Based on the p values in Table 1 and informed by clinical expertise, six variables were ultimately chosen: age, TnI, blood glucose, triglyceride, cholesterol, LVEF, and GRACE scores. The accuracy of the non-invasive cardiac function index in predicting MACE was evaluated using the ROC curve, and the results were presented as the area under the curve (AUC), and the optimal cutoff value was determined according to the Youden index.

TABLE 1 Clinical Baseline Indicators in Patients with ST-Segment Elevation Myocardial Infarction (STEMI).

Metric	Non-MACE N = 1 73	MACE N = 6 9	p
Demographic indicators
Age, year	63.7 ± 12. 9	68.3 ± 9.9	0.003
Male, n (%)	125 (72.3%)	53 (76.8%)	0.47
Smoking, n (%)	96 (55.5%)	44 (63.8%)	0.24
Clinical laboratory indicators
WBC(×109/L)	9.2 (7. 6, 12.1)	10. 2 (8. 0, 12.8)	0.216
Lymphocytes(×109/L)	1.3 (0.9, 1.8)	1.0 (0.7, 1.6)	0.110
Neutrophil (×109/L)	7.4 (5. 4, 9.4)	8.3 (5.7, 10.7)	0.203
Glu, mg/dL	6. 6 (5. 6, 8.6)	8. 8 (6.6, 10.7)	0.004
TG, mg/dL	1.5 (1.1,2.2)	1.2 (0.9,1.6)	0.001
TC, mg/dL	4.5 (3.8,5.2)	4.2 (3.5,5.0)	0.035
LDL-C, mg/dL	2.6 (2.2,3.2)	2.7 (2.2,3.3)	0.750
HDL-C, mg/dL	1.0 (0.9,1.2)	0.9 (0.8,1.1)	0.280
TNI, ng/ml	6.4 (0.8, 23.7)	15.5 (0.9, 30.0)	0.012
CK-MB, U/L	44.8 (7. 6, 60.0)	50.0 (1 2.5,80.0)	0.050
Echocardiographic index
LVEF, %	5 2.0 (4 7.0, 55.0)	48.0 (4 0.0, 52.0)	< 0.001
LVEDD(mm)	46 (42.0, 50.0)	45 (43.0, 51.0)	0.445
LVESD(mm)	31.4 (27.0, 34.3)	32.0 (28.0, 35.0)	0.263
Grace grade	146.0 (130.0, 156.0)	207.0 (198.0, 227.0)	< 0.001
Non-invasive cardiac function monitoring index
CO (l/min)	5.9 (5.3, 6.6)	4.6 (4.3, 5.2)	< 0.001
CI (l/min/m2)	3.9 (3.3, 4.6)	2.7 (2.4, 3.4)	< 0.001
SV (mL)	49.0 (45.0, 56.0)	38.0 (35.0, 44.0)	< 0.001
CTI	304.0 (258.2, 367.3)	90.1 (78.3, 128.6)	< 0.001
EDFR (%)	37.2 (33.3, 44.4)	50.4 (46.4, 68.9)	< 0.001
EDV (mL)	110.2 (98.9, 123.5)	165.4 (134.2,200.1)	< 0.001
SVR (dyn.s/cm5)	998.0 (888.0, 1078.0)	1345.0 (1046.0, 1556.0)	< 0.001

Indicators with high diagnostic value with sensitivity and specificity greater than 80% were selected to establish a logistic regression risk model, and a new risk prediction model was established in combination with GRACE risk score, and its prediction efficiency was evaluated by calculating C-index through ROC curve. Statistical significance was set at p < 0.05, with all tests being two-tailed.

Results

Baseline Characteristics

Patients were allocated to MACE and non-MACE groups based on MACEs. The baseline demographics, clinical laboratory tests, hemodynamic measures and GRACE scores of both groups are shown in Table 1.

Statistical analysis revealed significant differences between groups in age, blood glucose, triglyceride, total cholesterol, TNI, LVEF and GRACE score (p < 0.05). There were no significant differences in gender, smoking habits, white blood cells, neutrophils, lymphocytes, high and low density lipoprotein, creatine kinase isoenzymes, LVEDD and LVESD(p > 0.05).

According to the summary in Table 1, the early diastolic filling ratio (EDFR), end-diastolic volume (EDV), systemic vascular resistance (SVR) levels were significantly higher than the non-MACE group, while stroke volume (SV), cardiac output (CO), cardiac index (CI), and contractility index (CTI) were significantly lower than those in the non-MACE group (p < 0.05).

Logistic Regression Analysis of MACE Events in STEMI Patients

First, univariate logistic regression was applied to evaluate the association of noninvasive cardiac function monitoring indicators with the risk of recent MACE in patients with STEMI. Then, following the 10 EPV (Events Per Variable) principle, referring to the P-values in Table 1, and combined with clinical experience, we finally determined six correction variables: age, TnI, blood glucose, triglycerides, cholesterol, LVEF, and GRACE score. Subsequently, we used multivariate logistic regression to further evaluate the predictive value of noninvasive cardiac function monitoring indicators for MACE events after excluding confounding factors.

The results of univariate logistic regression analysis confirmed that the non-invasive cardiac function monitoring indicators SV, CO, CI, CTI, EDFR, EDV, and SVR were all risk factors for recent MACE events in patients with STEMI. A multivariate logistic regression analysis revealed that, after adjusting for confounding variables, noninvasive cardiac function monitoring indicators were identified as independent predictors of the risk of recent MACE in patients with STEMI. The results are shown in Table 2.

TABLE 2 Univariate and multivariate logistic regression analysis.

Categories	Crude model	Adjust model
OR and 95% CI	p	OR and 95% CI	p
SV	0.701 (0. 632–0.777)	< 0.001	0.688 (0. 610–0.777)	< 0.001
CO	0.118 (0. 067–0.209)	< 0.001	0.115 (0. 061–0.218)	< 0.001
CI	0.147 (0. 088–0.246)	< 0.001	0.104 (0. 054–0.200)	< 0.001
CTI	0.948 (0. 930–0.966)	< 0.001	0.929 (0. 891–0.968)	< 0.001
EDFR	1.135 (1.095–1.177)	< 0.001	1.149 (1.101–1.199)	< 0.001
EDV	1.057 (1.041–1.073)	< 0.001	1.057 (1.039–1.076)	< 0.001
SVR	1.008 (1.001–1.010)	< 0.001	1.005 (1.003–1.006)	< 0.001

ROC Curve Determines the Optimal Cutoff Value

The optimal cut-off value, sensitivity and specificity of the non-invasive cardiac function monitoring indexes were determined from the ROC curve. The findings are detailed in Table 3.

TABLE 3 Impact of non-invasive cardiac function monitoring indexes on GRACE score prediction performance.

	AUC(95% CI)	p	The best cut-off value	Sensitivity	Specificity
SV	0.893 (0.852–0.934)	< 0.001	5.7	0.971	0. 899
CO	0.902 (0.865–0.938)	< 0.001	44.5	0.768	0.792
CI	0.890 (0.849–0.930)	< 0.001	3.7	0.942	0.740
CTI	0.990 (0.982–0.998)	< 0.001	184	0.986	0.931
EDFR	0.861 (0.812–0.910)	< 0.001	45.2	0.841	0.792
EDV	0.868 (0.815–0.921)	< 0.001	135.0	0.710	0.896
SVR	0.793 (0.719–0.866)	< 0.001	1246.5	0.623	0.948

Logistic Regression Risk Prediction Model

We chose SV and CTI indexes, both of which had sensitivity and specificity above 80%, and converted them into categorical variables using the optimal cutoff value. By integrating these two indicators with the GRACE score, we developed a novel risk prediction model. Based on the results of multiple logistic regression analyses, we evaluated the efficacy of this new model in predicting the short-term risk of MACE in STEMI patients. The results show that SV and CTI can improve the predictive efficiency of GRACE scores, and the results are shown in Table 4.

TABLE 4 Effect of non-invasive cardiac function monitoring indexes on the prediction efficacy of GRACE score.

	AUC(95% CI)	p
GRACE score	0.831 (0.790–0.872)	0.019
GRACE score + SV + CTI	0.839 (0.797–0.879)	0.008

In conclusion, the non-invasive cardiac function monitoring indicators SV, CO, CI, CTI, EDFR, EDV, and SVR were independent predictors of the risk of recent MACE in patients with STEMI. Combining SV and CTI based on the GRACE score can further improve the predictive ability of the score to predict the risk of recent MACE in STEMI patients.

Discussion

Clinical experience and previous research[6]indicate that despite successful PCI treatment, the risk of severe complications and death remains high in patients with STEMI. Hemodynamic monitoring can reflect the patient's cardiac ejection function and myocardial contractility intuitively and accurately, playing a pivotal role in the early risk assessment of patients with myocardial infarction. The invasive Swan-Ganz catheter is the “gold standard” for hemodynamic monitoring. However, due to the invasive method, it is easy to produce various complications, which limits its application in patients with acute myocardial infarction. Thoracic bioimpedance (ICG) for noninvasive cardiac function monitoring offers data with comparable accuracy to that of invasive hemodynamic monitoring. It enables real-time assessment of the cardiac function of STEMI patients during invasive treatments and throughout their rehabilitation, aiding physicians in the early identification of high-risk individuals through dynamic alterations in cardiac function. This method is extensively utilized in the clinical evaluation of myocardial infarction patients and in the development of rehabilitation protocols. The findings of this study are the first to indicate that noninvasive cardiac function monitoring indicators can independently forecast the risk of MACE in STEMI patients, a discovery of significant practical importance in clinical practice.

The GRACE score is a scoring system that integrates multiple indicators including the classification of myocardial infarction, vital signs, electrocardiographic changes, and laboratory tests (Hautamäki et al. 2024). It is currently widely used for risk stratification and prognosis evaluation in patients with myocardial infarction. However, it lacks indicators for directly assessing cardiac function and hemodynamic changes after myocardial infarction. This study, for the first time, added two hemodynamic indicators with high diagnostic value to the GRACE score to make up for these defects. The results showed that after adding stroke volume and myocardial contractility index, the new scoring system had higher predictive efficiency and could more accurately stratify risk and evaluate prognosis for STEMI patients. This discovery is innovative, not only improving the GRACE scoring system but also further expanding the clinical application of non-invasive cardiac function monitoring.

However, this study is a single-center study with a small sample size that has some limitations. Firstly, the study's limitation lies in its brief follow-up duration, which merely suffices for assessing near-term outcomes among STEMI patients. Secondly, the follow-up patients failed to undergo a second review of cardiac displacement monitoring, resulting in an absence of dynamic change data crucial for indicator analysis.

Conclusion

Non-invasive hemodynamic indicators, such as SV, CO, CI, CTI, EDFR, EDV, and SVR, can not only independently predict the risk of recent MACE in STEMI patients, but also serve as combination indicators, significantly enhancing the predictive capacity of the GRACE score. By combining SV, CTI and the GRACE score, the newly constructed risk prediction model offers an improved assessment of the risk of recent MACE in STEMI patients. This safe and fast evaluation method can help clinicians to accurately and quickly identify high-risk STEMI patients and improve the prognosis of STEMI patients.

Author Contributions

Jiayan Xin has made significant contributions to the conception and design, analysis, and interpretation of data. Meng Ning participated in drafting the article and writing the manuscript. Chong Zhang is responsible for data collation, summary, revision, and statistical analysis. Yingwu Liu contributed to the review and revision of the manuscript, agreeing to take responsibility for all aspects of the work to ensure that issues related to the accuracy or completeness of the work were properly investigated and resolved. All authors read and approved the final manuscript.

Acknowledgments

The authors have nothing to report.

Ethics Statement

This study was approved by the Ethics Committee of Tianjin Third Central Hospital. Patients who participated in the study provided written informed consent prior to the interview and measurements, as stated in the Declaration of Helsinki.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

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