Predicting coronary artery severity in patients undergoing coronary computed tomographic angiography: Insights from pan-immune inflammation value and atherogenic index of plasma

被引:1
作者
Demirtola, Ayse Irem [1 ]
Erdol, Mehmet Akif [1 ]
Mammadli, Anar [2 ]
Ertem, Ahmet Goktug [1 ]
Yayla, Cagri [1 ]
Akcay, Adnan Burak [1 ]
机构
[1] Univ Hlth Sci, Ankara City Hosp, Bilkent, Ankara, Turkiye
[2] Bayindir Sogutozu Hosp Ankara, Ankara, Turkiye
关键词
Pan-immune inflammation value (PIV); Atherogenic index of plasma (AIP); Coronary computed tomographic angiography (CCTA); CAD-RADS; Predictive capability; MYOCARDIAL-INFARCTION; CT ANGIOGRAPHY; OUTCOMES;
D O I
10.1016/j.numecd.2024.05.015
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background and aims: Coronary computed tomographic angiography (CCTA) is pivotal in diagnosing coronary artery disease (CAD). We explored the link between CAD severity and two biomarkers, Pan-Immune Inflammation Value (PIV) and Atherogenic Index of Plasma (AIP), in stable CAD patients. Methods and results: A retrospective observational study of 409 CCTA patients with stable angina pectoris. Logistic regression identified predictors of severe CAD, stratified by CAD-RADS score. Receiver Operating Characteristic (ROC) curves evaluated predictive performance. PIV and AIP were significant predictors of severe CAD (PIV: OR 1.002, 95% CI: 1.000-1.004, p < 0.021; AIP: OR 0.963, 95% CI: 0.934-0.993, p < 0.04). AUC values for predicting severe CAD were 0.563 (p < 0.001) for PIV and 0.625 (p < 0.05) for AIP. Combined with age, AUC improved to 0.662 (p < 0.02). Conclusions: PIV and AIP were associated with severe CAD, with AIP demonstrating superior predictive capability. Incorporating AIP into risk assessment could enhance CAD prediction, offering a cost-effective and accessible method for identifying individuals at high risk of coronary atherosclerosis. (c) 2024 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:2289 / 2297
页数:9
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