新型炎症-代谢标志物对冠心病患者急性冠脉综合征的预测效能评估

Evaluation of the predictive performance of novel inflammatory-metabolic markers for acute coronary syndrome in patients with coronary heart disease

摘要:

目的 探究新型炎症-代谢标志物在冠心病患者急性冠脉综合征（ACS）中的预测效能。方法 选取2022年6月—2025年6月东莞市黄江医院收治的308例冠心病患者纳入研究。所有患者接受冠状动脉造影检查，根据检查结果将患者分为稳定组（236例，稳定型冠心病）和不稳定组（72例，ACS），对比两组患者的临床资料及新型炎症-代谢标志物［淋巴细胞/高密度脂蛋白胆固醇（LHR）、全身炎症反应指数（SIRI）、中性粒细胞与淋巴细胞比值（NLR）］，分析影响患者发生ACS的危险因素，使用受试者工作特征（ROC）曲线分析LHR、SIRI、NLR对冠心病患者发生ACS的预测效能。结果 稳定组年龄、LHR、SIRI和NLR均低于不稳定组（P <0.05），高密度脂蛋白胆固醇、Gensini评分<20分占比均高于不稳定组（P <0.05）。多因素逐步Logistic回归分析结果显示：LHR高［O^R=2.784（95% CI：1.410，5.497）］、SIRI高［O^R=3.040（95% CI：1.478，6.254）］、NLR高［O^R=2.643（95% CI：1.474，4.740）］和Gensini评分高［O^R=2.020（95% CI：1.320，3.090）］均为冠心病患者发生ACS的危险因素（P <0.05）。经ROC曲线分析，LHR、SIRI、NLR、Gensini评分联合预测冠心病患者发生ACS的曲线下面积为0.858（95% CI：0.815，0.901），敏感性为86.11%（95% CI：0.781，0.941），特异性为70.34%（95% CI：0.645，0.762）。结论 LHR、SIRI、NLR是影响患者发生ACS的重要因素，可作为临床预测冠心病患者发生ACS的新型炎症-代谢标志物。

Abstract:

Objective To explore the predictive performance of novel inflammatory-metabolic markers for acute coronary syndrome (ACS) in patients with coronary heart disease.Methods A total of 308 patients with coronary heart disease admitted to Dongguan Huangjiang Hospital were enrolled between June 2022 and June 2025. All patients underwent coronary angiography and were divided into the stable group (236 cases, stable coronary heart disease) and the unstable group (72 cases, ACS) according to the angiographic findings. The clinical data and novel inflammatory-metabolic markers [lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR), systemic inflammatory response index (SIRI), neutrophil-to-lymphocyte ratio (NLR) ] in the two groups were compared. The risk factors for ACS were analyzed, and predictive value of LHR, SIRI and NLR in ACS was analyzed by receiver operating characteristic (ROC) curves.Results The stable group showed significantly lower age, LHR, SIRI, and NLR, and higher high-density lipoprotein cholesterol (HDL-C) levels and a higher proportion of Gensini score < 20 compared with the unstable group (all P < 0.05). Multivariable stepwise logistic regression analysis revealed that elevated LHR [O^R = 2.784 (95% CI: 1.410, 5.497) ], elevated SIRI [O^R = 3.040 (95% CI: 1.478, 6.254) ], elevated NLR [O^R = 2.643 (95% CI: 1.474, 4.740) ], and a higher Gensini score [O^R = 2.020 (95% CI: 1.320, 3.090) ] were independent risk factors for ACS in patients with coronary heart disease (all P < 0.05). ROC curve analysis showed that the combined model of LHR, SIRI, NLR, and Gensini score had the highest predictive performance for ACS, with an area under the curve of 0.858 (95% CI: 0.815, 0.901), sensitivity of 86.11% (95% CI: 0.781, 0.941), and specificity of 70.34% (95% CI: 0.645, 0.762).Conclusion LHR, SIRI, and NLR are important factors associated with the occurrence of ACS in patients with coronary heart disease and may serve as novel inflammatory-metabolic biomarkers for predicting ACS in clinical practice.