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.

Background: Coronary artery disease is a leading global cause of death, and acute coronary syndrome (ACS) is its most critical acute form. Percutaneous coronary intervention (PCI) is the standard reperfusion treatment for this condition, but no‑reflow occurs in 30-40% of emergency PCI cases, and this phenomenon is associated with higher mortality and poor prognosis. Coagulation, cardiac, and thyroid function markers, reflecting thrombus burden, myocardial injury, and vascular dysfunction, are closely correlated with no‑reflow, but single biomarkers have limited predictive value. This study aimed to screen independent no-reflow risk factors, build and validate a multivariate model for clinical risk assessment and prevention in ACS patients. Methods: This single‑center retrospective study enrolled a total of 136 patients with ACS admitted to The Second Hospital of Tianjin Medical University in 2024 as the development cohort. Patients were divided into a normal blood flow group (110 cases) and a no-reflow group (26 cases). Preoperative levels of D-dimer, fibrin monomer (FM), von Willebrand factor (VWF), N-terminal pro-B-type natriuretic peptide (NT-proBNP), cardiac troponin I (cTnI) and thyroid function indices were measured. We analyzed intergroup differences and constructed a multivariate logistic regression model to screen independent risk factors for no-reflow. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of both individual indicators and the established model, while bootstrap resampling combined with calibration analysis was adopted for internal validation. Results: The proportion of hypertension and the levels of D-dimer, FM, VWF, cTnI, and NT-proBNP were higher in the no-reflow group compared with the normal reflow group (P<0.05), while FT3 concentration was lower (P<0.05). Multivariate logistic regression showed that high levels of D-dimer, FM, VWF, cTnI, and NT-proBNP, along with a low level of FT3, were independent risk factors for no-reflow (P<0.05). ROC curve analysis indicated that the prediction model (incorporating all six independent risk factors) had a diagnostic value for no-reflow. Individual indicators also demonstrated good predictive performance. Conclusions: The preliminary prediction model based on preoperative D-dimer, FM, VWF, cTnI, NT-proBNP, and FT3 levels shows promising predictive performance for no‑reflow, but these findings are exploratory. It should not yet be considered a definitive clinical tool, but may inform future risk‑stratification research.