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Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study

机译:独立的左心室形态测量图谱显示与心血管危险因素的一致关系:英国生物库研究

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摘要

Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric atlases enable precise quantification of shape and function, but there has been no objective comparison of different atlases in the same cohort. We compared two independent LV atlases using MRI scans of 4547 UK Biobank participants: (i) a volume atlas derived by automatic non-rigid registration of image volumes to a common template, and (ii) a surface atlas derived from manually drawn epicardial and endocardial surface contours. The strength of associations between atlas principal components and cardiovascular risk factors (smoking, diabetes, high blood pressure, high cholesterol and angina) were quantified with logistic regression models and five-fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) metrics. Both atlases exhibited similar principal components, showed similar relationships with risk factors, and had stronger associations (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors (DeLong p < 0.05). Morphometric variations associated with each risk factor could be quantified and visualized and were similar between atlases. UK Biobank LV shape atlases are robust to construction method and show stronger relationships with cardiovascular risk factors than mass and volume.
机译:左心室(LV)的质量和体积是临床和临床前疾病进程的重要指标。但是,当前忽略了现代成像检查中存在的许多形状信息。形态计量图谱可以精确量化形状和功能,但是在同一队列中尚无客观比较不同的图谱的方法。我们使用4547个英国生物库参与者的MRI扫描比较了两个独立的左心室图谱:(i)通过将图像体积自动非刚性配准到通用模板而获得的体积图谱,以及(ii)手动绘制的心外膜和心内膜所得到的表面图谱表面轮廓。地图集主要成分与心血管危险因素(吸烟,糖尿病,高血压,高胆固醇和心绞痛)之间的关联强度通过逻辑回归模型和五重交叉验证(使用ROC曲线下的面积(AUC)和Akaike进行量化)进行量化信息标准(AIC)指标。对于所有危险因素,这两个地图集均显示出与主要模型相似的主成分,与危险因素具有相似的关系,并且与基于LV质量和体积的参考模型相比,具有更强的关联性(较高的AUC和较低的AIC)(DeLong p <0.05)。与每个危险因素相关的形态变化可以量化和可视化,并且在地图集之间相似。 UK Biobank LV形状地图集对构建方法很可靠,并且与心血管危险因素的关系比质量和体积更牢固。

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