Inflammation Biomarker-Driven Vertical Visualization Model for Predicting Long-Term Prognosis in Unstable Angina...

Angina, a prevalent manifestation of coronary artery disease, is primarily associated with inflammation, an established contributor to the pathogenesis of atherosclerosis and acute coronary syndromes (ACS). Various inflammatory markers are employed in clinical practice to predict patient prognosis and optimize clinical decision-making in the management of ACS. This study investigated the prognostic significance of integrating commonly used, easily repeatable inflammatory biomarkers within a multimodal preoperative prediction model in patients presenting with unstable Angina Pectoris (UAP) and intermediate coronary lesions.

This retrospective analysis included patients diagnosed with UAP and intermediate coronary lesions (50%-70% stenosis) who underwent coronary angiography at our hospital between January 2019 and June 2021. The assessed outcome was the occurrence of major adverse cardiac and cerebrovascular events (MACCEs). The Boruta algorithm was applied to identify potential risk factors and develop a prognostic multimodal model.

A total of 773 patients were enrolled and divided into a training cohort (n=463) and validation cohort (n=310). A nomogram was constructed to predict the probability of MACCE-free survival based on five clinical features: diabetes mellitus, current smoking, history of myocardial infarction, neutrophil-to-lymphocyte ratio, and fasting blood glucose. In the training cohort, the area under the curve values for the nomogram at 24, 32, and 40 months were 0.669, 0.707, and 0.718, respectively, while those in the validation cohort were 0.613, 0.612 and 0.630, respectively. The model demonstrated good calibration in both cohorts with predicted outcomes aligning well with actual results at all time points up to 40 months. Furthermore, decision curve analysis showed significant clinical utility of the model across the specified time intervals.

The developed preoperative prognostic model visually illustrates the association among inflammation, blood glucose level, established risk factors, and long-term MACCEs in UAP patients with intermediate coronary lesions.