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Analysis of risk factors in high-risk plaque in acute coronary syndrome based on LASSO regression

医学 急性冠脉综合征 Lasso(编程语言) 内科学 心脏病学 回归 统计 心肌梗塞 数学 万维网 计算机科学
作者
Jing Gao,M.N Bai,J X Wang,X W Li,J X Wang,Yuli Wang,Yu Liu
出处
期刊:European Heart Journal [Oxford University Press]
卷期号:45 (Supplement_1)
标识
DOI:10.1093/eurheartj/ehae666.1612
摘要

Abstract Background The CLIMA Study (Relationship between OCT Coronary Plaque Morphology and Clinical Outcome) put forward the concept of High-risk plaque (HRP) and demonstrated that HRP were correlated with the high risk of major coronary events[1, 2]. HRP were defined as the presence of four simultaneous characteristics: minimum lumen area (MLA) <3.5mm2, fibrous cap thickness (FCT) <75 μm, lipid arc circumferential extension >180°, and the presence of macrophage infiltration. Early prediction of HRP formation are of great significance for the prevention and treatment of Acute Coronary Syndrome (ACS), but there are no relevant studies. Purpose To identify the risk factors related to OCT HRP in ACS and establish risk prediction model for HRPs in ACS. Methods A prospective observational cohort study was carried out on the patients with ACS from September 2019 to August 2022. A total of 169 patients were divided into OCT HRP (n=55) and OCT non-HRP (n=114) groups. Clinical information, laboratory examination and the OCT characteristics of the patients were collected. LASSO regression was used to screen variables, and multivariate logistic regression was used to establish risk prediction model. A nomogram was formulated, use the ROC curve to evaluate the discrimination of the model and the Bootstrap method to internally validate the model. Results The most frequently observed HRP characteristic was Lipid plague>180°(147 patients),followed by MLA<3.5mm2(141 patients), macrophages(127 patients), FCT<75μm(64 patients). The LASSO regression model was used to screen variables and construct an HRP risk factor model. This final model included age, BMI≥25, TC, LDL-C and Log NT-proBNP. The model is well differentiated (AUC 0.780, 95%CI 0.705-0.855) and calibrated. Decision Curve Analysis shows that the model has good net benefits. Conclusion This prediction model can accurately predict the risk of OCT HRP in patients with ACS, and is expected to assist clinicians in diagnosis and prevention of plaque stability.

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