AUTHOR=Bai Miao-Na , Wang Ji-Xiang , Li Xiao-Wei , Wang Jing-Xian , Wang Yu-Hang , Liu Yin , Gao Jing 

TITLE=Nomogram for predicting the severity of high-risk plaques in acute coronary syndrome

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1618038

DOI=10.3389/fcvm.2025.1618038

ISSN=2297-055X

ABSTRACT=BackgroundThe CLIMA study [Relationship between Optical Coherence Tomography (OCT) Coronary Plaque Morphology and Clinical Outcome; NCT02883088] introduced the concept of high-risk plaque (HRP) and demonstrated that HRP was associated with a high risk of major coronary events. HRP is defined by four simultaneous characteristics: minimum lumen area (MLA) <3.5 mm2, fibrous cap thickness (FCT) <75 μm, lipid arc circumferential extension >180°, and macrophage infiltration. Early prediction of HRP formation is critical for preventing and treating acute coronary syndrome (ACS), but no studies have been conducted on this topic.PurposeTo identify the risk factors associated with OCT HRP in ACS and develop a risk prediction model for HRPs in ACS.MethodsA prospective observational study was conducted on patients with ACS between September 2019 and August 2022. A total of 169 patients were divided into two groups: OCT HRP (n = 55) and OCT non-HRP (n = 114) groups. Clinical data, laboratory results, and OCT characteristics of the patients were collected. Least absolute shrinkage and selection operator (LASSO) regression was used to screen variables, while multivariate logistic regression was used to create a risk prediction model. A nomogram was created, and the receiver operating characteristic curve was used to assess the model's discrimination, as well as the bootstrap method to internally validate it.ResultsThe most commonly observed HRP characteristic was lipid plague >180° (147 patients), followed by MLA < 3.5 mm2 (141 patients), macrophages (127 patients), and FCT < 75 μm (64 patients). The LASSO regression model was used to screen variables and develop an HRP risk factor model. The nomogram includes five predictors: age, BMI ≥ 25 kg/m2, triglycerides, low-density lipoprotein cholesterol, and Log N-terminal brain natriuretic peptide precursor. The model is highly differentiated (area under the curve 0.780, 95% confidence interval 0.705–855) and calibrated. The calibration curve and decision curve analysis demonstrated the model's clinical usefulness.ConclusionA simple and practical nomogram for predicting HRPs accurately in patients with ACS was developed and validated, and is expected to help clinicians diagnose and prevent plaque stability.