目的 探讨视觉冠状动脉钙化积分(VCACS)预测冠心病的价值。方法 回顾性收集2018-07至2023-07在陆军军医大学第一附属医院接受胸部CT(1.25 mm层厚)及冠状动脉CT血管成像(CCTA)检查的患者数据,影像学资料均由富有经验的放射科医师进行分析,并将CCTA结果显示冠脉狭窄程度≥50%的患者纳入冠心病组。专业软件对心电图门控的心脏CT进行分析得到Agatston积分。采用Spearman相关性分析探讨在所有参与者、不同Agatston积分(0～10、11～100、101～400、＞400)以及不同年龄组(≤65岁,＞65岁)中Agatston积分与VCACS之间的关系。运用单因素logistic回归分析评估各变量与冠心病的关系,筛选出有统计学意义的变量后通过多因素logistic回归探讨总的VCACS(tVCACS)与冠心病之间的关联。同时构建XGBoost机器学习算法的预测模型,结合SHAP特征重要性分析,量化各个特征变量对冠心病的预测价值。结果 共纳入287例,年龄30～90岁,其中男性177例。冠心病组153例,占总数的53.3%。在所有参与者、不同Agatston积分组以及不同年龄组中,VCACS与Agatston积分在评估冠脉钙化程度方面均具有良好的一致性。冠心病组的tVCACS显著高于非冠心病组。多因素logistic回归分析结果表明tVCACS 2～5分组的OR=2.23(95%CI: 1.22～4.08)、tVCACS≥6分组的OR=20.26(95%CI: 5.76～71.26),提示随着tVCACS的增加,冠心病的风险显著升高。SHAP特征重要性分析亦表明,与其他因素相比,tVCACS对冠心病风险具有更高的预测价值。结论 通过1.25 mm层厚胸部CT获取的VCACS与Agatston积分之间存在较好的相关性,并在预测冠心病方面具有潜在的临床应用价值。
Abstract
Objective To explore the predictive value of visual coronary artery calcium score(VCACS) for coronary heart disease(CHD). Methods A retrospective collection of data was conducted on patients who underwent chest CT(with a slice thickness of 1.25 mm) and coronary CT angiography(CCTA) in Southwest Hospital of Army Medical University from July 2018 to July 2023. The imaging data were analyzed by experienced radiologists, and the patients with coronary artery stenosis ≥50% on CCTA were included in the CHD group. Agatston scores were derived from the analysis of ECG-gated cardiac CT using specialized software. Spearman's correlation analysis was employed to explore the relationship between Agatston scores and VCACS in all participants, different Agatston score ranges (0-10, 11-100, 101-400, >400) and age groups(≤65 years, >65 years). Univariate logistic regression analysis was used to assess the association between variables with CHD. After selecting statistically significant variables, multivariate logistic regression was employed to investigate the association between the total VCACS(tVCACS) subgroups and CHD. At the same time, the prediction model of XGBoost machine learning algorithm was constructed, and the significance analysis of SHAP features was combined to quantify the predictive value of each feature variable on CHD. Results A total of 287 participants, aged from 30 to 90 years, were included in the study, with 177 males. A total of 153 participants(53.3%) were classified into the CHD group. In the overall cohort, different Agatston score ranges and age groups, VCACS demonstrated good consistency with Agatston score in assessing the degree of coronary artery calcification. The tVCACS in CHD group was significantly higher than that in non-CHD group. Multivariate logistic regression analysis showed that the OR of tVCACS 2-5 group was 2.23(95% CI: 1.22-4.08), and the OR of the tVCACS≥6 group was 20.26(95% CI: 5.76-71.26), indicating a significantly increased risk of CHD with higher tVCACS. SHAP feature importance analysis also demonstrated that tVCACS had a higher predictive value for CHD risk than other factors. Conclusions VCACS obtained through 1.25 mm chest CT demonstrates a good correlation with Agatston score and has potential clinical application value in predicting CHD.
关键词
视觉冠状动脉钙化积分 /
Agatston积分 /
计算机断层扫描 /
冠状动脉计算机断层血管成像
Key words
visual coronary artery calcification score /
Agatston score /
computed tomography /
coronary artery computed tomography angiography
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