Triglyceride Glucol Index for Predicting the Value of No-Reflow in Patients Undergoing Emergency PCI for Acute Myocardial Infarction
Conditions: Acute Myocardial Infarction (AMI)
Sponsor: The Third Xiangya Hospital of Central South University

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Clinical Research Program 
 
 
Analysis of risk factors for 
microcirculatory obstruction after PCI in 
patients with acute cardiac myocardial 
infarction 
 
 
Source of funding: self-financing 
 
Affiliation: Xiangya Third Hospital, Central South University, 
China 
 
Principal Investigator: XXX 
 
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CONTENTS 
Summary of the research program ................................................................................. 3 
Body of the study ........................................................................................................... 6 
1. Background of the study ............................................................................................ 6 
2. research purpose ......................................................................................................... 9 
3. Research design ......................................................................................................... 9 
4. Research ................................................................................................................... 10 
5. Key evaluation indicators ........................................................................................ 14 
6. Security Considerations ........................................................................................... 14 
7. statistical analysis ..................................................................................................... 15 
8. quality management ................................................................................................. 15 
9. Description of the form of publication of research results ...................................... 16 
10. Ethics Statement ..................................................................................................... 16 
11. bibliography ........................................................................................................... 16 
 
 
 
 

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Summary of the research program 
Study Title: Analysis of Risk Factors for Microcirculatory Obstruction after PCI in 
Patients with Acute Myocardial Infarction 
 
     
STUDY OBJECTIVE: To analyze the risk factors for coronary microcirculatory 
obstruction in patients with acute myocardial infarction after emergency PCI and the 
relationship between triglyceride glucose index and coronary microcirculatory 
obstruction. 
     
 
STUDY DESIGN: This study is a single-center prospective observational cohort 
study to include 200 patients with AMI who attended the Third Xiangya Hospital of 
Central South University from June 2024 to August 2025 for emergency PCI. We 
will collect patients' baseline clinical characteristics, preoperative blood laboratory 
indices, and intraoperative coronary angiography imaging characteristics. The 
primary outcome event will be MVO diagnosed by CMR after postoperative 
coronary emergency PCI.MVO is defined as the inability to reperfuse the coronary 
microcirculation in a previously ischemic area despite epicardial vessel opening. On 
late contrast-enhanced imaging (LGE) scans of CMR, bright spots in the region of 
infarcted myocardium and dark areas within the bright spots are defined as MVO.We 
proposed to analyze the baseline characteristics and postoperative outcomes of the 
patients using descriptive statistics. Logistic regression and Lasso regression 
analyses were used to screen independent risk factors from potential predictors, and 
then multifactorial logistic regression was used to further analyze the effects of 
independent predictors on coronary microcirculatory obstruction and to calculate the 
associated statistical efficacy. 
The research process: 
This study consists of four stages: data collection, data preprocessing, 
correlation analysis, and interpretation. 

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(1) Data collection: 
Baseline clinical characteristics, preoperatively available biochemical markers, 
coronary angiographic imaging characteristics, and intravascular ultrasound imaging 
characteristics were collected from patients. All data were collected uniformly by 
trained investigators. 
(2) Data preprocessing: 
This includes data cleaning, data coding, data normalization, data 
dimensionality reduction, and variable screening. 
(3) correlation analysis 
We propose to use logistic regression and Lasso regression to identify i.e. 
analyze risk factors associated with coronary microcirculatory obstruction. 
(4) account for 
Explain the independent correlation between risk factors and coronary 
microcirculatory obstruction. 
Entry Criteria: 
(1) Age ≥ 18 years; 
(2) STEMI symptom onset <12 hours; 
(3) STEMI symptom onset time of 12-48 hours in the presence of persistent 
ischemic symptoms, hemodynamic instability, or life-threatening ventricular 
arrhythmias; 
(4) Very high risk group NSTEMI; 
(5) treated with emergency PCI; 
Voluntary signed informed consent. 
Exclusion Criteria: 
(1) Nonobstructive acute myocardial infarction; 
(2) Severe chronic kidney disease (defined as estimated glomerular filtration 
rate <20 mL/min per 1.73 m²); 
(3) CMR The image is not clear; 
(4) People who are pregnant or planning to become pregnant; 
(5) Failed emergency PCI. 
 

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Statistical methods: 
Analyses were performed using SPSS software 26.0 and R software. Continuous 
variables are expressed as SD ± mean or median (interquartile range). Categorical 
variables were expressed as percentages. To compare continuous variables between 
two groups, two-sample t-test was used and Wilcoxon rank sum test was used if the 
data did not conform to normal distribution. To compare the categorical variables of 
the two groups, the chi-square test was used. Univariate and multivariate logistic 
regression analyses were used to determine risk factors associated with MVO. Area 
under the curve (ROC), sensitivity, specificity, Jordon's index, and 95% confidence 
intervals (CIs) were calculated for subjects' work characteristics to assess the 
predictive value of the associated risk factors for MVO. All tests were two-tailed and 
statistical significance was defined as a P-value <0.05. 
 
 
 

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Body of the study 
1. Background of the study 
Despite significant advances in early diagnosis and reperfusion therapy, acute 
myocardial infarction (AMI) remains one of the leading causes of death and disability 
worldwide, with high mortality rates [1-3]. Primary Percutaneous Coronary Intervention 
(PPCI) is currently the treatment of choice for AMI, but myocardial reperfusion is not 
guaranteed even after successful revascularization of the offender, and according to 
previous studies, up to approximately 60% of AMI patients have myocardial 
reperfusion failure after PPCI ([4-3]). According to previous studies, up to about 60% 
of AMI patients have myocardial reperfusion failure after PPCI [4-5], which is 
characterized by failure to restore microcirculatory perfusion to the previously ischemic 
infarcted area, and is traditionally defined as no-reflow (NR) [6-7]. Microvascular 
obstruction (MVO) is a condition in which blood flow to the myocardial 
microcirculation is restricted despite the opening of epicardial vessels, resulting in 
inadequate myocardial perfusion [8]. Experimental studies have shown that NR 
manifests as a failure of microvascular perfusion, which is consistent with the features 
of MVO [9]. In addition, the equivalence of MVO and NR is further supported by the 
close correlation between perfusion defects (i.e., MVO) observed in imaging studies by 
cardiac magnetic resonance imaging (CMR) with gadolinium contrast agent and 
nonfluorescent regions (NR) observed using the dye thapsigargin S[10]. Thus, MVO can 
be used to refer to NR, indicating the extent of myocardial microcirculatory 
compromise.MVO can be recognized by specific signal changes in CMR scans, which 
are specified by the appearance of bright spots in areas of infarcted myocardium on 
Late Gadolinium Enhancement (LGE) scans, and the dark areas within the bright spots 
are referred to as MVO. The pathophysiological mechanisms of MVO are complex and 
often involve a variety of factors such as microvascular spasm, endothelial cell injury 
or dysfunction, thrombosis and inflammatory response, and reperfusion injury [5,11], and 
the exact mechanism remains controversial [12].MVO is significantly associated with 
poor prognosis in cardiovascular disease.Suzanne et al. demonstrated that, compared 
with traditional prognostic markers, MVO was the strongest predictor of the occurrence 
of death, nonfatal myocardial reinfarction, and congestive heart failure after ST-

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segment elevation myocardial infarction (STEMI) as well as CMR provided important 
information in assessing the long-term prognosis of patients with STEMI[12]. A study 
by Pei-Kun Hu and Fang-Fang Wang et al. also showed that MVO was an independent 
predictor of deterioration in left ventricular function and poor prognosis [17-18], and 
Durante et al. showed that MVO was highly significant in predicting the incidence of 
major adverse cardiovascular events and that the CMR provided a better prognostic 
stratification of patients [19]. Therefore, early identification and prevention of MVO is 
particularly important [20]. 
The detection rate of MVO varies widely, largely depending on the diagnostic 
method used, with different diagnostic methods and evaluation times resulting in 
reported rates of MVO ranging from 5% to 70% [13].Diagnostic methods for MVO 
include both invasive and noninvasive techniques. Invasive methods are mainly 
coronary angiography, which identifies microvascular obstruction by direct observation 
of blood flow. Noninvasive methods, on the other hand, include CMR, 
echocardiography, single photon emission computed tomography (SPECT), and 
positron emission tomography (PETCT), which indirectly diagnose MVO by 
evaluating myocardial blood flow (MBF) and function.Among them, CMR provides 
comprehensive information about cardiac structure, function, perfusion, and 
myocardial tissue characteristics, which helps to accurately diagnose MVO. Among 
them, CMR provides comprehensive information about cardiac structure, function, 
perfusion, and myocardial tissue properties, which helps to accurately assess the 
presence and extent of MVO and quantify myocardial fibrosis, and is considered to be 
the gold standard for identifying MVO [14-16]. 
Insulin resistance (IR), an impairment of insulin in stimulating glucose uptake, has 
been shown to be significantly associated with the pathogenesis, progression, and 
prognosis of coronary atherosclerosis [21-22], accelerating the development of both 
microvascular and macrovascular disease and leading to a poorer prognosis for patients 
with pre-existing cardiovascular disease [23-24]. High insulin-normal glucose (HIEG) 
clamp is the gold standard technique for the assessment of IR [25-26], but its widespread 
use is limited by the fact that it is time-consuming and complex, and is not suitable for 
large-scale studies.The homeostasis model assessment of IR (HOMA-IR) can be used 

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in large-scale or epidemiological studies [27], but its use is limited by the fact that it 
relies on the measurement of plasma insulin and is expensive. Triglyceride glucose 
index (TyG), which combines triglyceride (TG) and fasting glucose (FPG) levels, is a 
reproducible, reliable, and validated surrogate marker of IR [28-30].The TyG index has 
been associated with pathological processes such as atherosclerosis, arterial stiffness, 
and coronary calcification, which are all closely related to the development of 
cardiovascular disease. Ma J et al. demonstrated that elevated levels of TyG index were 
a strong independent predictor of freedom from recurrent flow in patients with T2DM 
combined with AMI, that the incidence of freedom from recurrent flow increased 
progressively with increasing tertile intervals of the TyG index, and that the addition of 
the TyG index to a baseline risk prediction model had incremental effect [33-34].Qu Z et 
al. also showed that the TyG index was a strong predictor of no-reflow phenomenon in 
patients with metabolic syndrome combined with STEMI undergoing PCI [35]. Previous 
studies have shown that increased IR is independently associated with offender vascular 
plaque vulnerability in patients with ACS, particularly speckled calcification [36-
37].Zhao X et al. demonstrated that the TyG index, in combination with offender 
vascular plaque characteristics, can be used in clinical practice to support the risk 
stratification of patients with STEMI and to predict a poorer prognosis [38].Yin D et al. 
demonstrated that higher TyG index levels were associated with a better prognosis 
inpatients with ACS [39]. TyG index levels were significantly associated with the 
prevalence of coronary artery speckle calcification, Minimum lumen area (MLA) ≤ 4.0 
mm², and Plaque burden (PB) > 70% in patients with ACS, which can be used as a 
clinical predictor of invasive potential for calcification patterns and plaque 
characteristics that may represent unstable plaques ([39]). biomarkers [39]. 
Studies have shown that insulin resistance leads to an increased inflammatory 
response, which in turn affects vascular endothelial function and normal microvascular 
function [40].IVUS recognizes plaques in coronary arteries, especially unstable plaques, 
which are more prone to hemodynamic changes and microvascular dysfunction [41]. 
Insulin resistance may exacerbate this process by mechanisms such as inflammatory 
response, abnormal lipid metabolism, and vascular endothelial dysfunction [40-43], but 
the exact mechanisms need to be further investigated and validated. However, there is 

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a lack of research on the relationship between TyG index and MVO. Based on the above 
background and the fact that TyG index can more comprehensively reflect insulin 
resistance and disorders of glucose and lipid metabolism, which are key factors leading 
to endothelial dysfunction and microcirculation injury, the scientific hypothesis of this 
study is that elevated TyG index is an independent risk factor for MVO in AMI patients 
after PPCI, and its predictive ability is better than that of traditional research. and its 
predictive power is superior to that of traditional single lipid or glucose indices. 
2. research purpose 
The aim of this study was to prospectively collect clinical data from patients with 
acute myocardial infarction, to understand the incidence of coronary, after emergency 
PCI in our center, to analyze the risk factors of coronary nil flow, and to pave the way 
for the development of a prediction model for coronary nil flow and further exploration 
of the pathogenesis of coronary nil flow.    
3. Research design 
3.1 Sample size calculation 
3.1.1 Sample size calculation according to overall outcome proportions:To ensure an 
accurate estimate of the overall outcome proportions at the 95% confidence level, we 
used the following formula to calculate the sample size([) (44) (]) : 
n = (1.96/δ)^2 * Φ(1-Φ) 
The parameters were set as follows: critical value of 95% confidence interval 
(constant) = 1.96, target error δ = 0.06, and expected proportion of results Φ = 0.5. 
Bringing the parameters into the above equation gives the minimum required sample 
size of 170. 
3.1.1 Sample size calculation according to prediction model accuracy:In order to ensure 
that the prediction model has a high prediction accuracy, we use the following 
formula to calculate the sample size[39] : 
n = exp[(-0.508 + 0.259ln(Φ) + 0.504ln(P)-ln(MAPE))/0.544] 
The parameters were set as follows: expected outcome proportion Φ = 0.5, number 
of predictor variable parameters P = 12, and target mean absolute prediction error 
MAPE = 0.1. The parameters were brought into the above equation to obtain the 
minimum required sample size of 191. 

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In summary, taking into account missing data and increasing the sample size by 
10-20%, this study proposes to prospectively collect data from approximately 300 
patients with acute myocardial infarction. 
3.2 flow chart 
 
4. Research 
4.1 Entry criteria: 
4.1.1 Entry Criteria: 
(1) Age ≥ 18 years; 
(2) STEMI symptom onset <12 hours; 
(3) STEMI symptom onset time of 12-48 hours in the presence of persistent 
ischemic symptoms, hemodynamic instability, or life-threatening ventricular 

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arrhythmias; 
(4) Very high risk group NSTEMI; 
(5) treated with emergency PCI; 
(6) Voluntary signed informed consent. 
4.1.2 Exclusion Criteria: 
(1) Nonobstructive acute myocardial infarction; 
(2) Severe chronic kidney disease (defined as estimated glomerular filtration rate 
<20 mL/min per 1.73 m²); 
(3) CMR The image is not clear; 
(4) People who are pregnant or planning to become pregnant; 
(5) Failed emergency PCI. 
4.2 Concluding events 
4.1.3 Main ending events 
MVO diagnosed by CMR after emergency PCI. 
4.1.4 Secondary ending events 
(1) MACE events during hospitalization (cardiac death, nonfatal reinfarction, 
nonfatal stroke, acute heart failure); 
(2) Cardiac death during hospitalization; 
(3) Nonfatal reinfarction during hospitalization; 
(4) Acute heart failure during hospitalization; 
(5) Cardiogenic shock during hospitalization; 
(6) All-cause mortality during hospitalization. 
4.3 research process  
The study was a prospective observational clinical study that did not involve 
clinical interventions, and all patients received the usual clinical regimen without supra-
guidelines or drug instructions for medication. 
4.3.1 Patient Screening 
This study intends to screen patients with acute myocardial infarction attending 
the Third Xiangya Hospital of Central South University from June 2024 to August 2025. 
It is expected to enroll a total of 300 patients who have been screened and qualified by 
the enrollment criteria and have signed the informed consent. 

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4.3.2 Data collection 
Based on the literature, expert opinion, and clinical experience, data were collected 
on baseline clinical characteristics at admission, pre-interventional blood laboratory 
markers, and emergency coronary angiography and intravascular ultrasound imaging 
characteristics, as shown in the table below. 
 
Collection 
point in time 
Collection of content 
Baseline clinical 
features 
Immediately 
after 
admission to 
the 
hospital, 
collection was 
performed by 
the 
researchers 
! 
 (a person's) age 
! 
 distinguishing 
between 
the 
sexes 
! 
 Killip classification 
! 
 Total ischemic time 
! 
 diabetes 
! 
 high blood pressure 
! 
 History of prior cardiovascular 
disease 
(including 
stroke, 
coronary 
heart 
disease, 
peripheral arterial disease) 
! 
 Systolic 
blood 
pressure 
<100mmHg 
! 
 angina pectoris without pre-
infarction 
Blood 
Laboratory 
Indicators 
Blood 
specimens 
were collected 
and sent for 
testing 
immediately 
after 
admission to 
the hospital 
! 
Inflammatory 
markers: 
high 
sensitivity C-reactive protein 
(hs-CRP), systemic immuno-
inflammatory index (neutrophil 
count 
x 
platelet 
count/lymphocyte 
count), 
platelet count, white blood cell 
count, interleukin-6 (IL-6) and 
interleukin-8 (IL-8). 
! 
Coagulation indices: D-dimer, 
Fibrinogen 
! 
hemoglobin 
! 
NT-proBNP 
! 
hypoglycemia 
Emergency 
coronary 
angiography 
imaging features 
Collection by 
trained 
researchers 
after 
emergency 
! 
Coronary 
flow 
parameters:: 
TIMI flow classification (to 
observe 
primary 
outcome), 
myocardial 
perfusion 
classification 

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PCI 
! 
Lesion characteristics: lesion 
length, 
lesion 
diameter, 
truncated occlusion 
! 
Thrombus TIMI classification 
! 
lesion location 
! 
collateral circulation 
Intravascular 
ultrasonographic 
features 
Collection by 
trained 
researchers 
after 
emergency 
PCI 
! 
Attenuation patches (maximum 
attenuation angle >180 degrees, 
attenuation length >5 mm) 
! 
plaque eccentricity 
! 
Lipid pool-like images 
! 
plaque rupture 
! 
Intraplaque thrombosis 
The data collection in this study will strictly follow the principles of 
representativeness, accuracy, completeness and safety to ensure that the constructed 
prediction model has good reliability and generalizability. The data collection process 
will be completed under the premise of not affecting the "door-to-door time" of 
emergency PCI and the emergency PCI process. We will collect baseline clinical data 
through consultation and physical examination; access to the electronic medical record 
system of Xiangya Third Hospital to obtain blood laboratory indexes; access to the 
SiChuang system of Xiangya Third Hospital to collect emergency coronary 
angiography imaging characteristics; and analyze intravascular ultrasound imaging 
characteristics and collect data through the Volcano S5 imaging system software. All 
participants underwent written notification of the study and their signed informed 
consent was obtained. We will strictly implement data quality control measures by 
using Epidata 3.1 and Excel software for double entry and cross-checking, which 
includes format consistency check, data range check, and logical validation of the data. 
We will perform missing value processing and outlier analysis to ensure the accuracy 
and completeness of the data. All data will be stored and managed safely in accordance 
with relevant laws and regulations, and the principle of patient privacy protection will 
be strictly observed. 
4.3.3 statistical analysis 
Analyses were performed using SPSS software 26.0 and R software. Continuous 
variables are expressed as SD ± mean or median (interquartile range). Categorical 
variables were expressed as percentages. To compare continuous variables between two 

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groups, two-sample t-test was used and Wilcoxon rank sum test was used if the data did 
not conform to normal distribution. To compare the categorical variables of the two 
groups, the chi-square test was used. Univariate and multivariate logistic regression 
analyses were used to determine risk factors associated with MVO. Area under the 
curve (ROC), sensitivity, specificity, Jordon's index, and 95% confidence intervals (CIs) 
were calculated for subjects' work characteristics to assess the predictive value of the 
associated risk factors for MVO. All tests were two-tailed and statistical significance 
was defined as a P-value <0.05. 
5. Key evaluation indicators  
(1) 
Dominance Ratio (OR): OR is the ratio of the probability of a disease 
occurring in a population exposed to a risk factor to the probability of a disease 
occurring in a non-exposed population. an OR>1 indicates that the risk factor 
increases the risk of a disease occurring, and an OR<1 indicates that the risk factor 
decreases the risk of a disease occurring. a larger OR value indicates a stronger 
correlation between the risk factor and the disease. 
(2) 
Adjusted Dominance Ratio (aOR): aOR is an OR calculated after 
controlling for the effects of confounders. aOR can more accurately reflect the true 
association between risk factors and disease. 
(3) 
Regression coefficient (β): the regression coefficient indicates the average 
extent to which a unit change in the independent variable affects the dependent 
variable. 
6. Security Considerations 
This was an observational clinical study, and all patients enrolled in the study 
received conventional clinical treatment without clinical intervention. 
The researcher has pre-studied the Declaration of Helsinki, Measures for Ethical 
Review of Biomedical Research Involving Human Beings, Measures for Ethical 
Review of Life Science and Medical Research Involving Human Beings , Data Security 
Law of the People's Republic of China , and other relevant laws and regulations, and 
has passed the examination of the Good Practice for Quality Management of Clinical 
Trials of Drugs (GCP). The safety considerations and countermeasures mainly involve 
the following points: 

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(1) Risks to privacy and confidentiality: When collecting sensitive personal 
information, it is proposed that measures be taken to anonymize the data to ensure its 
security and confidentiality. 
(2) Data quality risk: Data loss, incorrect recording, incomplete data, etc. may 
occur during data collection, which may affect the accuracy of the study results. Strict 
data collection and quality control processes should be established, and data should be 
verified and validated on a regular basis. 
(3) Risk of analytical bias: During data analysis, researchers may unconsciously 
introduce analytical bias that affects the objectivity of the findings. A rigorous data 
analysis plan should be developed, appropriate statistical methods should be used, and 
sensitivity analyses should be conducted to mitigate the effects of analytic bias. 
(4) Risk of misinterpretation of findings: Findings may be misunderstood or over-
interpreted. The limitations of the findings and the scope of application of the findings 
should be clearly stated in the study report. 
7. statistical analysis 
7.1 Statistical methods: 
Analyses were performed using SPSS software 26.0 and R software. Continuous 
variables are expressed as SD ± mean or median (interquartile range). Categorical 
variables were expressed as percentages. To compare continuous variables between two 
groups, two-sample t-test was used and Wilcoxon rank sum test was used if the data did 
not conform to normal distribution. To compare the categorical variables of the two 
groups, the chi-square test was used. Univariate and multivariate logistic regression 
analyses were used to determine risk factors associated with MVO. Area under the 
curve (ROC), sensitivity, specificity, Jordon's index, and 95% confidence intervals (CIs) 
were calculated for subjects' work characteristics to assess the predictive value of the 
associated risk factors for MVO. All tests were two-tailed and statistical significance 
was defined as a P-value <0.05. 
7.2 Missing value handling: 
Missing values were filled in, using plurality for categorical variables and median 
for continuous variables. 
7.3 Sensitivity analysis: 

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Sensitivity analyses were performed for multiple missing value filling methods, 
including plurality filling and 5-fold multiple filling. Age, sex, and acute infarction type 
subgroups were analyzed. 
7.4 Statistical software: 
Data for this study will be collected and entered by Excel (Microsoft Office home 
and student 2019) and Epidata 3.8 and analyzed by IBM SPSS Statistics version 26. 
8. quality management 
The investigator should ensure that the data are true, accurate, complete and 
traceable, and should ensure the integrity of the basic clinical research documents 
during their retention to avoid intentional or unintentional alteration or loss. 
 
9. Description of the form of publication of research results 
Proposed academic paper. 
10. Ethics Statement 
Clinical research will be conducted in accordance with the Declaration of Helsinki 
of the World Medical Assembly, the Measures for Ethical Review of Biomedical 
Research Involving Human Beings, the Measures for Ethical Review of Life Science 
and Medical Research Involving Human Beings, and the Measures for the 
Administration of Healthcare Institutions Carrying Out Researcher-Initiated Clinical 
Research, and other relevant regulations. The clinical research will be implemented 
only after the Ethics Committee approves the study protocol before the study begins. 
The privacy and data confidentiality of the subjects will be protected during the study. 
I promise to comply with the regulations related to research norms and integrity. 
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[5] Galli M, Niccoli G, De Maria G, et al. Coronary microvascular obstruction
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