Development and preliminary internal validation of a prediction model incorporating cardiac troponin I (cTnI), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and thyroid function for no-reflow during percutaneous coronary intervention in patients with acute coronary syndrome

Introduction

Coronary artery disease (CAD) is the leading cause of death in middle- and high-income countries, and acute coronary syndromes (ACS) is its most severe form. ACS is triggered by the rupture of coronary artery plaques, which leads to acute myocardial ischemia, thrombosis, and vascular stenosis or occlusion, and often results in critical conditions such as unstable angina, acute myocardial infarction (AMI), or sudden cardiac death. In recent years, with the acceleration of population aging in China, the incidence and number of patients with ACS have been continuously rising. Its characteristics of abrupt onset, rapid progression, and high mortality pose severe challenges to public health systems (1-3). Each year, there are approximately 5.8 million new cases of ischemic heart disease manifesting mainly as ACS worldwide, and around 4.1 million deaths are attributable to ACS annually (4). The incidence of ACS has been rising year by year in China. According to the China Cardiovascular Disease Report 2014, there were 2.5 million patients with myocardial infarction across the country. Cardiovascular diseases rank first among all causes of death for urban and rural residents. In 2013, the mortality rate of AMI was 66.62 per 100,000 population in rural areas and 51.45 per 100,000 population in urban area (5). ACS refers to a spectrum of cardiac disorders triggered by abrupt myocardial perfusion deficiency, covering three major clinical subtypes: ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina pectoris. Rest-related thoracic distress stands as the predominant initial manifestation in ACS patients, occurring in roughly 79% of male sufferers and 74% of female patients with this disease. Nevertheless, around 40% of men and 48% of women exhibit atypical complaints—dyspnea being the representative one—which may arise alone or, more frequently, coexist alongside chest tightness. For individuals admitted with suspected ACS, an urgent electrocardiogram (ECG) recording is mandatory within 10 minutes after hospital arrival; this examination serves as a critical tool to differentiate STEMI from non-ST-segment elevation ACS (NSTE-ACS). STEMI stems from total coronary vessel blockage and makes up about 30% of all ACS cases. NSTE-ACS, which constitutes the remaining 70% of ACS diagnoses, is defined by the absence of prominent ST-segment shifts on ECG and arises from partial or transient coronary obstruction. Its electrocardiographic manifestations are diverse: isolated ST depression accounts for 31% of cases, isolated T-wave inversion for 12%, concurrent ST depression plus T-wave inversion for 16%, while nearly 41% of patients display no obvious ECG abnormalities. If ECG findings point to STEMI, prompt revascularization via primary percutaneous coronary intervention (PCI) delivered within a 120-minute time window can lower all-cause mortality from 9% down to 7% (6). Currently recommended treatment regimens in guidelines (such as PCI) have significantly improved the prognosis of patients with ACS (7). Since its inception in 1977, PCI has been developed into a mainstream treatment technology for CAD. Compared with thrombolytic therapy, PCI for reperfusion treatment in patients with acute myocardial infarction can significantly improve clinical prognosis. However, clinical data show that 30–40% of patients undergoing emergency PCI experience the no-reflow phenomenon, which is defined as patency of the epicardial vessels on coronary angiography but a lack of adequate blood perfusion at the myocardial tissue level (8-10). It is currently believed that the main pathological mechanisms of no-reflow are distal thromboembolism, myocardial ischemia-reperfusion injury, and coronary microcirculatory dysfunction (11). As the no-reflow phenomenon is independently associated with increased patient mortality and the occurrence of malignant arrhythmias, it holds considerable clinical significance (12). Although studies have proposed predictive indicators such as the platelet-lymphocyte ratio (13), more reliable predictors are still needed in clinical practice for optimizing risk stratification.

Thrombosis and inflammatory response are the core links in the pathological mechanism of ACS. Fibrin monomer (FM) is generated via thrombin-mediated cleavage of fibrinogen under hypercoagulable conditions. As an early-appearing molecule in the coagulation cascade, FM sensitively reflects elevated coagulation activity and the initial formation of fibrin clots, making it a reliable marker for identifying prethrombotic states (14). von Willebrand factor (VWF), a key marker of endothelial injury, has attracted heightened research interest. As a multimeric glycoprotein primarily produced and released by vascular endothelial cells and megakaryocytes, VWF plays a central role in mediating platelet adhesion and regulating the coagulation cascade under physiological conditions (15). As a fibrin degradation product, D-dimer is a well-established biomarker of fibrinolytic activity and thrombus burden (16), and its high-level expression in patients with acute myocardial infarction is associated with an increased risk of cardiovascular death after PCI (17). A study by Erkol et al. also confirmed that there is a significant correlation between D-dimer levels and the no-reflow phenomenon (18). Studies have found that various metabolic abnormalities caused by abnormal thyroid hormone concentrations exert varying degrees of effect on cardiovascular diseases. After the occurrence of ACS, patients’ thyroid hormone concentrations fluctuate rapidly, affecting the degree of myocardial damage (19-21). Additionally, cardiac troponin I (cTnI) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) are established markers of myocardial injury and cardiac function (22,23).

Although individual indicators have been identified, the predictive efficacy of a single biomarker is limited. Thus far, no study has constructed a comprehensive prediction model integrating the coagulation indices, cTnI and NT-proBNP, and thyroid function for predicting no-reflow in patients with ACS undergoing PCI. Therefore, this study aimed to identify the independent risk factors for no-reflow, develop a multivariate prediction model, and validate the model’s performance to provide a scientific basis for clinical risk assessment and prevention of no-reflow among patients with ACS. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1485/rc).

Methods

General information

In the single-center, retrospective, prediction model development study, the development cohort included 136 patients with ACS admitted to the Department of Cardiology of The Second Hospital of Tianjin Medical University in 2024. The cohort included 85 males and 51 females, aged 40–91 years. We adopted the following inclusion standards: (I) all subjects fulfilled the global ACS diagnostic criteria issued by the European Society of Cardiology, (II) administration of PCI within 12 hours, and (III) completion of coronary angiography. In parallel, participants were ruled out if they had any of the following conditions: (I) presence of severe coronary thromboembolic issues including residual stenosis and coronary dissection after receiving bypass graft surgery or PCI treatment; (II) other heart diseases; (III) dysfunction of liver, kidney, and other organs; and (IV) malignant tumors, blood diseases, infectious diseases, and autoimmune diseases. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Medical Ethics Committee of The Second Hospital of Tianjin Medical University (approval No. KY2025K319), and all included participants signed an informed consent form.

Grouping

Patients were divided into a normal flow group (110 cases) and a no-reflow group (26 cases) according to the Thrombolysis In Myocardial Infarction (TIMI) flow during the operation. The blood flow was evaluated via TIMI score according to the following scheme: grade 0, no contrast agent passing through the stenosis; grade 1, contrast agent passing through the occlusion but stagnating at the occlusion and failing to fill the entire distal vascular bed of the occlusion; grade 2, contrast agent passing through the occlusion and filling the distal vascular bed of the occlusion, but exhibiting delayed filling and clearance; and grade 3, blood flow filling rapidly, with the filling and elimination of contrast agent in the distal vascular bed of the occlusion being as fast as that in the unaffected vessels. During PCI, grade 0 was defined as no-reflow, grades 1–2 as slow flow, and grade 3 as normal flow.

Detection methods

Peripheral venous blood (2 mL) was collected from patients in the fasting state into ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes, and after thorough mixing, routine blood tests were performed; another 2 mL of peripheral venous blood was collected into sodium citrate anticoagulant tubes, and after centrifugation, plasma obtained was taken for the detection of D-dimer, VWF, and FM levels. D-dimer and FM were measured using immunoturbidimetric assays, VWF was measured by immunoturbidimetric method, cTnI and NT‑proBNP by electrochemiluminescence immunoassay, and thyroid function by electrochemiluminescence immunoassay. 5 mL of peripheral venous blood was collected into tubes containing coagulants or gels, and after centrifugation, serum was obtained for the detection of biochemical indicators such as cTnI and NT-proBNP; finally, electrochemical luminescence was used for the detection of thyroid function items such as free thyroxine (FT4), free triiodothyronine (FT3) and thyroid-stimulating hormone (TSH).

Data collection

Decisions regarding the patient data to be collected were informed by clinical professional knowledge and previous research results (24). The patient data collected included age, gender, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG) level, number of stents implanted, hypertension, and smoking, among others.

Sample size justification

A total of 26 no-reflow events were observed, and 6 predictors were included in the final model, yielding an events-per-variable (EPV) of approximately 4.3. Although the EPV is below the ideal threshold for definitive model validation, this sample size is acceptable for an exploratory analysis identifying novel risk predictors, and the approach remains widely used in preliminary clinical prediction studies. No missing data were observed for the primary predictors and outcome variables in this study, and so no imputation methods were required.

Statistical analysis

All continuous predictors were kept as continuous variables in the regression. Candidate predictors were first screened by univariate logistic regression, and variables with P<0.05 were entered into multivariable stepwise forward logistic regression. No missing data were present for any study variables. Statistical analyses of all collected data were carried out via SPSS version 26.0 software (IBM Corporation, Armonk, New York, USA). Normally distributed continuous variables were presented as mean ± standard deviation, with independent sample t-tests adopted for intergroup comparisons. Continuous indicators that failed to follow a normal distribution were described using median and interquartile range, and intergroup differences were assessed through rank-sum tests. Categorical data were reported as case counts along with corresponding percentages, and chi-square tests were applied to compare differences across groups. Univariate logistic regression analysis was first performed to filter potential predictive factors linked to the no-reflow phenomenon. Variables with statistical significance were then incorporated into multivariate logistic regression to extract independent risk predictors. All confirmed independent risk factors were combined to build a predictive model, from which a corresponding regression formula was derived. Receiver operating characteristic (ROC) curves were plotted to compute the area under the curve (AUC) for evaluating the discriminative power of the model. Sensitivity, specificity, and accuracy were calculated based on the optimal cutoff value. Given the exploratory nature of this single‑center study, external validation in larger cohorts is warranted to confirm the model’s generalizability. Internal validation of the final model was performed using bootstrap resampling (1,000 replicates) to obtain optimism‑corrected performance estimates. P<0.05 was considered statistically significant.

Results

Participant flow and characteristics

A total of 152 patients with ACS were initially screened, among whom 16 were excluded after screening with the inclusion and exclusion criteria (6 with previous PCI history, 4 with liver dysfunction, 3 with malignant tumors, and 3 with incomplete data). Finally, 136 patients were included, with 110 in the normal flow group and 26 in the no-reflow group (Figure 1).

Figure 1 Flowchart of participant inclusion. PCI, percutaneous coronary intervention; TIMI, Thrombolysis in Myocardial Infarction.

Baseline characteristics

The proportion of hypertension in the no-reflow group was higher than that in the normal flow group (69.23% vs. 36.36%; P=0.005). There were no significant differences in gender, age, lipid levels (HDL-C, LDL-C, TC, and TG), number of stents implanted, or smoking status between the two groups (P>0.05) (Table 1).

Comparison of preoperative levels

Patients belonging to the no-reflow group had substantially greater preoperative values of D-dimer, FM, VWF, cTnI and NT-proBNP when compared to subjects in the normal reperflow group (all P<0.05), while FT3 concentration was significantly lower (P<0.05). There was no significant difference in TSH or FT4 levels between the two groups (P>0.05) (Table 2).

Univariate logistic regression analysis

A regression model was established, in which the occurrence of no-reflow during PCI in patients with ACS was the dependent variable, and hypertension, D-dimer, FM, VWF, cTnI, NT-proBNP, and FT3 levels were independent variables (Table 3). The regression results showed that high levels of D-dimer, FM, VWF, cTnI, and NT-proBNP and a low level of FT3 were all risk factors for no-reflow during PCI in patients with ACS (P<0.05) (Table 4).

Predictive value of preoperative D-dimer, FM, cTnI, NT-proBNP, and FT3 for no-reflow during PCI

In the ROC curve analysis of the prediction model, the AUC was 0.943 [95% confidence interval (CI): 0.901–0.985; P<0.001], which was higher than the AUC of the individual indicators (0.847–0.921). The optimal cutoff value (Youden index =0.775) produced a sensitivity of 88.46%, a specificity of 89.09%, and an accuracy of 88.97% (Table 5 and Figure 2).

Figure 2 ROC curves for predicting the no-reflow phenomenon during PCI. AUC, area under the curve; cTnI, cardiac troponin I; FM, fibrin monomer; FT3, free triiodothyronine; NT-proBNP, N-terminal pro-B-type natriuretic peptide; PCI, percutaneous coronary intervention; ROC, receiver operating characteristic; VWF, von Willebrand factor.

Discussion

This study developed and validated a multivariate prediction model for no-reflow during PCI in patients with ACS incorporating six independent risk factors: D-dimer, FM, VWF, cTnI, NT-proBNP, and FT3. The model showed excellent discriminative ability (AUC =0.943) and good calibration, supporting its use as a reliable tool for clinical risk assessment.

D-dimer, FM, and VWF are critical biomarkers for evaluating thrombosis and endothelial function. Increased D-dimer concentrations signify elevated thrombus burden and augmented fibrinolytic activity (16), and studies have demonstrated a correlation between D-dimer and no-reflow (18). FM serves as an early marker of coagulation activation, and elevated FM levels indicate the formation of fibrin clots, which increase the risk of coronary microcirculatory embolism (14). As a key biomarker reflecting endothelial injury, VWF enhances platelet adhesion and aggregation, ultimately leading to microcirculatory disturbance (15). These three indicators collectively reflect the pathological processes of thrombosis and endothelial injury, which are core mechanisms of no-reflow.

cTnI is a specific marker of myocardial cell injury, and its elevation indicates severe myocardial damage caused by microcirculatory disorders (22). NT-proBNP serves as a reliable indicator of ventricular wall stress and cardiac function. Elevated NT-proBNP levels are associated with increased cardiac workload resulting from insufficient myocardial perfusion (23). Both indicators are closely linked to the severity of ACS and the risk of no-reflow, making them important components of the prediction model.

Low FT3 levels (low T3 syndrome) are common in patients with ACS under stress (19). FT3 regulates myocardial metabolism, vascular tone, and endothelial function; its reduction leads to weakened myocardial contractility, decreased cardiac output, and impaired vascular relaxation, further aggravating myocardial ischemia and no-reflow (25). In our study, a low FT3 level was an independent risk factor for no-reflow, highlighting the importance of evaluating thyroid function in patients with ACS.

The prediction model developed in this study demonstrated substantial clinical utility. Preoperative assessment of these six indicators can enable clinicians to identify patients at high risk of no-reflow, thereby facilitating the implementation of individualized preventive measures. For high-risk individuals, more aggressive interventional strategies can be adopted to reduce the incidence of no-reflow. Given that this phenomenon is closely associated with poor prognosis, this model may also serve as a valuable tool in predicting short-term adverse outcomes.

Several limitations of this study should be acknowledged. First, the relatively small sample size (136 patients, with only 26 no-reflow events) and single-center design are significant limitations. The EPV was approximately 4.3, which increases the risk of overfitting; therefore, the reported AUC should be interpreted cautiously, and external validation is essential before clinical application. Future multicenter, large-sample prospective studies are therefore warranted to further validate its performance. Second, we only evaluated the occurrence of no-reflow during PCI, and thus long-term follow-up is needed to assess the model’s predictive value for long-term prognosis. Third, potential unmeasured confounders—including diabetes mellitus, medication history, and genetic factors—were not incorporated into the model, which may reduce its predictive accuracy.

Future work should (I) include an expanded sample size and a multicenter, prospective design to validate and update the model; (II) examine the addition of other potential predictors (e.g., inflammatory markers and genetic polymorphisms) to improve model performance; (III) develop a user-friendly risk calculator (e.g., web-based or mobile application) to facilitate clinical use; and (IV) assess interventions to verify whether targeted management of high-risk patients based on the model can reduce the incidence of no-reflow.

Conclusions

High D-dimer, FM, VWF, cTnI, and NT-proBNP levels, along with a low FT3 level, were independent risk factors for no-reflow, and the model based on these six indicators performed well. However, given the exploratory nature of this single-center study and the lack of external validation, this model should not yet be used to guide clinical practice; future multicenter studies are needed to confirm its utility.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1485/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1485/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1485/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1485/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Medical Ethics Committee of The Second Hospital of Tianjin Medical University (approval No. KY2025K319) and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Gray)