Bai Miao-Na, Wang Ji-Xiang, Li Xiao-Wei, Wang Jing-Xian, Wang Yu-Hang, Liu Yin, Gao Jing
Graduate School, Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
Thoracic Clinical College, Tianjin Medical University, Tianjin, China.
Front Cardiovasc Med. 2025 Jun 25;12:1618038. doi: 10.3389/fcvm.2025.1618038. eCollection 2025.
The 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 mm, 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.
To identify the risk factors associated with OCT HRP in ACS and develop a risk prediction model for HRPs in ACS.
A 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 ( = 55) and OCT non-HRP ( = 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.
The most commonly observed HRP characteristic was lipid plague >180° (147 patients), followed by MLA < 3.5 mm (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/m, 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.
A 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.
CLIMA研究[光学相干断层扫描（OCT）冠状动脉斑块形态与临床结局的关系；NCT02883088]引入了高危斑块（HRP）的概念，并证明HRP与主要冠状动脉事件的高风险相关。HRP由四个同时出现的特征定义：最小管腔面积（MLA）<3.5 mm、纤维帽厚度（FCT）<75 μm、脂质弧圆周延伸>180°和巨噬细胞浸润。HRP形成的早期预测对于预防和治疗急性冠状动脉综合征（ACS）至关重要，但尚未有关于该主题的研究。
确定ACS中与OCT HRP相关的危险因素，并建立ACS中HRP的风险预测模型。
对2019年9月至2022年8月期间的ACS患者进行了一项前瞻性观察研究。共169例患者分为两组：OCT HRP（n = 55）组和OCT非HRP（n = 114）组。收集患者的临床资料、实验室检查结果和OCT特征。采用最小绝对收缩和选择算子（LASSO）回归筛选变量，多因素逻辑回归建立风险预测模型。绘制列线图，采用受试者工作特征曲线评估模型的区分度，并采用自助法进行内部验证。
最常观察到的HRP特征是脂质斑块>180°（147例患者），其次是MLA<3.5 mm（141例患者）、巨噬细胞（127例患者）和FCT<75 μm（64例患者）。使用LASSO回归模型筛选变量并建立HRP危险因素模型。列线图包括五个预测因子：年龄、BMI≥25 kg/m²、甘油三酯、低密度脂蛋白胆固醇和Log N末端脑钠肽前体。该模型具有高度区分度（曲线下面积0.780，95%置信区间0.705 - 0.855）且经过校准。校准曲线和决策曲线分析证明了该模型的临床实用性。
开发并验证了一种简单实用的列线图，可准确预测ACS患者的HRP，有望帮助临床医生诊断和预防斑块稳定性。
