冠状动脉钙化评分和 19 种生物标志物对心血管事件的影响；DanRisk 子研究的 10 年跟踪调查

IF 1.4 Q3 PERIPHERAL VASCULAR DISEASE

Atherosclerosis plus Pub Date : 2024-09-24 DOI:10.1016/j.athplu.2024.09.003

Mie Schæffer , Jeppe Holm Rasmussen , Maise Høigaard Fredgart , Selma Hasific , Frederikke Nørregaard Jakobsen , Flemming Hald Steffensen , Jess Lambrechtsen , Niels Peter Rønnow Sand , Lars Melholt Rasmussen , Axel CP. Diederichsen

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引用次数: 0

摘要

建议采用 SCORE2 算法来估计心血管疾病（CVD）的风险。冠状动脉钙化（CAC）评分价格昂贵，但能改善风险预测。本研究旨在确定并比较 CAC 评分和 19 种生物标志物在风险预测中的附加值。方法在 2009-2010 年的多中心前瞻性队列中，从随机挑选的 1211 名中年男性和女性中收集了传统心血管（CV）风险因素、CAC 评分和多种生物标志物（包括血脂、钙磷代谢、肌钙蛋白、炎症、肾功能和踝肱指数（ABI））。有关心血管事件的 10 年随访数据通过丹麦健康登记处获得。心血管事件被定义为中风、心肌梗塞、心力衰竭住院、冠状动脉血运重建或心血管疾病导致的死亡。SCORE2、CAC-分数、生物标志物和心血管事件之间的关联采用cox比例危险率（HR）进行评估，并通过ROC曲线的AUC计算进行比较。最后，计算了净再分类改善率（NRI）。经风险因素调整后，CAC-分数与事件显著相关（调整后 HR 分别为 1.9 (95%CI:1.1; 3.3)、3.6 (95%CI:1.9; 6.8) 和 5. (95%CI:2.6; 10.3)，CAC-分数为 1-99、CAC-分数为 100-399 和 CAC-分数≥400。CRP最高四分位数的HR为2.3（95%CI：1.2；4.5），而其余生物标志物均未改善HR。对 SCORE2 进行调整后，CAC-评分提高了 AUC（AUCCAC：0.72，AUCSCORE2：0.67，p<0.01）。所选生物标志物（总胆固醇、低密度脂蛋白、磷酸盐、肌钙蛋白、CRP 和肌酐）的组合可在一定程度上改善 AUC（AUCBiomarkers + SCORE2：0.71，AUCSCORE2：0.67，p=0.06）。CAC评分的NRI为63%（p<0.0001）。

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Coronary artery calcification score and 19 biomarkers on cardiovascular events; a 10-year follow-up DanRisk substudy

Aim

The SCORE2 algorithm is recommended to estimate risk of cardiovascular disease (CVD). Coronary artery calcification (CAC) score is expensive but improves the risk prediction. This study aims to determine and compare the additive value of CAC-score and 19 biomarkers in risk prediction.

Methods

Traditional cardiovascular (CV) risk factors, CAC-score, and a wide range of biomarkers (including lipids, calcium-phosphate metabolism, troponin, inflammation, kidney function and ankle brachial index (ABI)) were collected from 1211 randomly selected middle-aged men and women in this multicenter prospective cohort in 2009–2010. 10-year follow-up data on CV-events were obtained via the Danish Health Registries. CV-event was defined as stroke, myocardial infarction, hospitalization for heart failure, coronary artery revascularization or death from CVD. The association between SCORE2, CAC-score, biomarkers, and CV-events was assessed using cox proportional hazard rates (HR) and compared using AUC-calculation of ROC-curves. Finally, net reclassification improvement (NRI) was calculated.

Results

92 participants had CV-events. Adjusted for risk factors, CAC-score was significantly associated with events (adjusted HR 1.9 (95%CI:1.1; 3.3), 3.6 (95%CI:1.9; 6.8), and 5. (95%CI:2.6; 10.3) for CAC-score 1–99, CAC-score 100–399 and CAC-score ≥400, respectively. HR for the highest quartile of CRP was 2.3 (95%CI:1.2; 4.5), while none of the remaining biomarkers improved HR. Adjusted for SCORE2, the CAC-score improved AUC (AUC_CAC: 0.72, AUC_SCORE2: 0.67, p<0.01). A combination of selected biomarkers (total cholesterol, low-density lipoprotein, phosphate, troponin, CRP, and creatinine) borderline improved AUC (AUC_{Biomarkers + SCORE2}: 0.71, AUC_SCORE2: 0.67, p=0.06). NRI for CAC score was 63 % (p<0.0001).

Conclusion

CAC-score improved prediction of CV-events, however the selected biomarkers did not.

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来源期刊

Atherosclerosis plus Cardiology and Cardiovascular Medicine

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

66 days