Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).
Department of Cardiac Sciences, University of Calgary, Alberta, Canada (R.J.J.M.).
Circ Cardiovasc Imaging. 2024 Oct;17(10):e016958. doi: 10.1161/CIRCIMAGING.124.016958. Epub 2024 Sep 30.
Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We determined per-patient age- and sex-specific distributions of deep learning-based plaque measurements and further evaluated their risk prediction for myocardial infarction in external samples.
In this international, multicenter study of 2803 patients, a previously validated deep learning system was used to quantify coronary plaque from computed tomography angiography. Age- and sex-specific distributions of coronary plaque volume were determined from 956 patients undergoing computed tomography angiography for stable coronary artery disease from 5 cohorts. Multicenter external samples were used to evaluate associations between coronary plaque percentiles and myocardial infarction.
Quantitative deep learning plaque volumes increased with age and were higher in male patients. In the combined external sample (n=1847), patients in the ≥75th percentile of total plaque volume (unadjusted hazard ratio, 2.65 [95% CI, 1.47-4.78]; =0.001) were at increased risk of myocardial infarction compared with patients below the 50th percentile. Similar relationships were seen for most plaque volumes and persisted in multivariable analyses adjusting for clinical characteristics, coronary artery calcium, stenosis, and plaque volume, with adjusted hazard ratios ranging from 2.38 to 2.50 for patients in the ≥75th percentile of total plaque volume.
Per-patient age- and sex-specific distributions for deep learning-based coronary plaque volumes are strongly predictive of myocardial infarction, with the highest risk seen in patients with coronary plaque volumes in the ≥75th percentile.
冠状动脉 CT 血管造影的斑块定量分析已成为心血管风险的有价值预测指标。深度学习可提供冠状动脉 CT 血管造影的冠状动脉斑块的自动定量分析。我们确定了基于深度学习的斑块测量值的患者特异性年龄和性别分布，并在外部样本中进一步评估了它们对心肌梗死的风险预测。
在这项国际多中心研究中，对 2803 例患者进行了研究，使用了经过验证的深度学习系统来对 CT 血管造影中的冠状动脉斑块进行定量分析。从 5 个队列的 956 例稳定型冠状动脉疾病患者的 CT 血管造影中确定了冠状动脉斑块体积的年龄和性别特异性分布。使用多中心外部样本评估了冠状动脉斑块百分位数与心肌梗死之间的关系。
定量深度学习斑块体积随年龄增加而增加，且男性患者更高。在合并的外部样本（n=1847）中，与第 50 百分位以下的患者相比，总斑块体积≥75 百分位的患者（未经调整的危险比，2.65 [95% CI，1.47-4.78]；=0.001）发生心肌梗死的风险增加。对于大多数斑块体积，也存在类似的关系，并且在调整了临床特征、冠状动脉钙、狭窄和斑块体积的多变量分析中仍然存在，总斑块体积≥75 百分位的患者的调整后危险比范围为 2.38 至 2.50。
基于深度学习的冠状动脉斑块体积的患者特异性年龄和性别分布与心肌梗死强烈相关，总斑块体积≥75 百分位的患者风险最高。
