Prognosis impact of multiple novel lymphocyte-based inflammatory indices in patients with initially diagnosed coronary artery disease

IF 3.1 4区医学 Q3 IMMUNOLOGY

Immunity, Inflammation and Disease Pub Date : 2024-09-27 DOI:10.1002/iid3.1340

Yi Gao, Geng Bai, Yuqing Li, Bo Yu, Ziqiang Guo, Xiaolin Chen, Tong Liu, Guangping Li

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Abstract

Background

This study aimed to evaluate six novel lymphocyte-based inflammatory markers (neutrophil-lymphocyte ratio, monocyte-lymphocyte ratio, platelet-lymphocyte ratio [PLR], systemic immune inflammation index [SII], systemic inflammatory response index, and systemic immune inflammation response index [SIIRI]) in patients with newly diagnosed coronary artery disease [CAD].

Methods

A total of 959 patients newly diagnosed with CAD and underwent diagnostic coronary angiography were enrolled in this study and followed for major adverse cardiovascular events (MACEs), including cardiovascular death, nonfatal myocardial infarction, and nonfatal stroke. The best cutoff value was used to compare the six indicators. Cox risk regression analysis evaluated the relationship between novel lymphocyte-based inflammatory markers and MACEs in newly diagnosed CAD patients.

Results

During a mean follow-up period of 33.3 ± 9.9 months, 229 (23.9%) MACEs were identified. Multivariate Cox regression analysis showed that only SIIRI (hazard ratio [HR]: 5.853; 95% confidence interval [CI]: 4.092–8.371; p < .001) and PLR (HR: 1.725; 95% CI: 1.214–2.452; p = .002) were independent predictors of MACEs. Nevertheless, following the adjustment for covariates, only the SIIRI was found to be a significant predictor MACEs and its corresponding specific endpoint occurrences. The predictive ability of the model was improved when six different inflammatory markers were added to the basic model established by traditional risk factors, namely, the C-index increased, and the SIIRI increased most significantly (AUC: 0.778; 95% CI: 0.743–0.812; p < .001). However, among the six novel inflammatory markers, only SIIRI had improved net reclassification improvement (NRI) and integrated discrimination improvement (IDI) (NRI: 0.187; 95% CI: 0.115–0.259, p < .001. IDI: 0.135; 95% CI: 0.111–0.159, p < .001), which was superior to the basic model established by traditional risk factors.

Conclusions

SIIRI is independent predictor of MACEs in newly diagnosed CAD patients. SIIRI was superior to other measures in predicting MACEs. The combination of SIIRI and traditional risk factors can more accurately predict MACEs.

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基于淋巴细胞的多种新型炎症指数对初诊冠心病患者预后的影响

背景本研究旨在评估新诊断冠状动脉疾病（CAD）患者的六种基于淋巴细胞的新型炎症指标（中性粒细胞-淋巴细胞比值、单核细胞-淋巴细胞比值、血小板-淋巴细胞比值 [PLR]、全身免疫炎症指数 [SII]、全身炎症反应指数和全身免疫炎症反应指数 [SIIRI]）。方法共有 959 名新确诊为 CAD 并接受冠状动脉造影诊断的患者参与了这项研究，并对其主要不良心血管事件（MACE）进行了随访，包括心血管死亡、非致死性心肌梗死和非致死性卒中。比较六项指标时采用了最佳临界值。Cox 风险回归分析评估了新诊断的 CAD 患者中基于淋巴细胞的新型炎症指标与 MACEs 之间的关系。结果在平均 33.3 ± 9.9 个月的随访期间，共发现 229 例（23.9%）MACE。多变量 Cox 回归分析表明，只有 SIIRI（危险比 [HR]：5.853；95% 置信度 [HR]：5.8535.853；95% 置信区间 [CI]：4.092-8.371; p < .001）和 PLR（HR：1.725; 95% CI：1.214-2.452; p = .002）是 MACEs 的独立预测因子。然而，在对协变量进行调整后，发现只有 SIIRI 可显著预测 MACEs 及其相应的特定终点发生率。当在传统风险因素建立的基本模型中加入六种不同的炎症标志物时，模型的预测能力得到了提高，即C指数增加，SIIRI增加最为显著（AUC：0.778；95% CI：0.743-0.812；p <.001）。然而，在六种新型炎症标记物中，只有 SIIRI 的净再分类改进（NRI）和综合判别改进（IDI）有所改善（NRI：0.187；95% CI：0.115-0.259，p <；.001。IDI：0.135；95% CI：0.111-0.159，p <.001），优于由传统风险因素建立的基本模型。结论 SIIRI 是新诊断的 CAD 患者 MACEs 的独立预测指标。在预测 MACE 方面，SIIRI 优于其他指标。SIIRI 和传统风险因素的结合可以更准确地预测 MACE。

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来源期刊

Immunity, Inflammation and Disease Medicine-Immunology and Allergy

CiteScore

3.60

自引率

0.00%

发文量

146

审稿时长

8 weeks

期刊介绍： Immunity, Inflammation and Disease is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research across the broad field of immunology. Immunity, Inflammation and Disease gives rapid consideration to papers in all areas of clinical and basic research. The journal is indexed in Medline and the Science Citation Index Expanded (part of Web of Science), among others. It welcomes original work that enhances the understanding of immunology in areas including: • cellular and molecular immunology • clinical immunology • allergy • immunochemistry • immunogenetics • immune signalling • immune development • imaging • mathematical modelling • autoimmunity • transplantation immunology • cancer immunology