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系统免疫炎症指数与那不勒斯预后评分对行冠状动脉计算机断层扫描血管造影术患者冠状动脉严重程度的预测比较。

Comparison of Systemic Immune-Inflammation Index and Naples Prognostic Score for Prediction Coronary Artery Severity Patients Undergoing Coronary Computed Tomographic Angiography.

机构信息

Clinic of Cardiology, Cam and Sakura City Hospital, Istanbul, Turkey.

Clinic of Cardiology, Kartal Kosuyolu Training and Research Hospital, Istanbul, Turkey.

出版信息

Angiology. 2024 Jan;75(1):62-71. doi: 10.1177/00033197231170979. Epub 2023 Apr 15.

Abstract

This study compared the predictive power of the systemic immune-inflammation index (SII) and Naples prognostic score (NPS) in determining the severity of coronary artery disease (CAD). The study included 1138 patients who underwent coronary computed tomographic angiography (CCTA). The primary outcome was the evaluation of CAD severity, determined by the Coronary Artery Disease-Reporting and Data System (CAD-RADS) obtained from the CCTA scans. A basic statistical model including age, gender, chest pain, diabetes mellitus, hypertension, hyperlipidemia, and smoking was built, and categorical variables, NPS (Naples 3,4 vs 0,1,2) and SII, were added to the basic statistical model. The net benefits of the predictive parameters were determined by a decision curve analysis, and the association between CAD-RADS and NPS, SII was quantified by odds ratios (OR) and 95% confidence intervals (CI). The decision curve analysis showed that adding SII to the statistical model had a better full range of probability of clinical net benefit compared with the baseline model (OR: 5.77, 95% CI 4.15-8.02, < .001). However, adding the NPS ( = .11) to the model did not outperform the basic statistical model. In conclusion, the SII may have a net predictive effect on top of traditional risk factors.

摘要

本研究比较了全身免疫炎症指数(SII)和那不勒斯预后评分(NPS)在确定冠状动脉疾病(CAD)严重程度方面的预测能力。该研究纳入了 1138 名接受冠状动脉计算机断层扫描血管造影(CCTA)的患者。主要结局是通过 CCTA 扫描获得的冠状动脉疾病报告和数据系统(CAD-RADS)评估 CAD 严重程度。建立了一个包含年龄、性别、胸痛、糖尿病、高血压、高血脂和吸烟的基本统计模型,并向基本统计模型中添加了分类变量、NPS(那不勒斯 3、4 与 0、1、2)和 SII。通过决策曲线分析确定预测参数的净收益,通过比值比(OR)和 95%置信区间(CI)量化 CAD-RADS 与 NPS、SII 之间的关系。决策曲线分析表明,与基线模型相比,将 SII 添加到统计模型中具有更好的全范围临床净收益(OR:5.77,95%CI 4.15-8.02,<.001)。然而,在模型中添加 NPS(=.11)并没有优于基本统计模型。总之,SII 可能对传统危险因素具有净预测作用。

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