Magudia Kirti, Bridge Christopher P, Bay Camden P, Farah Subrina, Babic Ana, Fintelmann Florian J, Brais Lauren K, Andriole Katherine P, Wolpin Brian M, Rosenthal Michael H
Department of Radiology, Brigham and Women's Hospital, Boston, MA.
Present affiliation: Department of Radiology, Duke University School of Medicine, 2301 Erwin Rd, Durham, NC 27710.
AJR Am J Roentgenol. 2023 Feb;220(2):236-244. doi: 10.2214/AJR.22.27977. Epub 2022 Aug 31.
CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk. The aim of this study was to determine whether BC measurements obtained from routine CT scans by use of a fully automated deep learning algorithm could predict subsequent cardiovascular events independently from weight, BMI, and additional cardiovascular risk factors. This retrospective study included 9752 outpatients (5519 women and 4233 men; mean age, 53.2 years; 890 patients self-reported their race as Black and 8862 self-reported their race as White) who underwent routine abdominal CT at a single health system from January 2012 through December 2012 and who were given no major cardiovascular or oncologic diagnosis within 3 months of undergoing CT. Using publicly available code, fully automated deep learning BC analysis was performed at the L3 vertebral body level to determine three BC areas (skeletal muscle area [SMA], visceral fat area [VFA], and subcutaneous fat area [SFA]). Age-, sex-, and race-normalized reference curves were used to generate scores for the three BC areas. Subsequent myocardial infarction (MI) or stroke was determined from the electronic medical record. Multivariable-adjusted Cox proportional hazards models were used to determine hazard ratios (HRs) for MI or stroke within 5 years after CT for the three BC area scores, with adjustment for normalized weight, normalized BMI, and additional cardiovascular risk factors (smoking status, diabetes diagnosis, and systolic blood pressure). In multivariable models, age-, race-, and sex-normalized VFA was associated with subsequent MI risk (HR of highest quartile compared with lowest quartile, 1.31 [95% CI, 1.03-1.67], = .04 for overall effect) and stroke risk (HR of highest compared with lowest quartile, 1.46 [95% CI, 1.07-2.00], = .04 for overall effect). In multivariable models, normalized SMA, SFA, weight, and BMI were not associated with subsequent MI or stroke risk. VFA derived from fully automated and normalized analysis of abdominal CT examinations predicts subsequent MI or stroke in Black and White patients, independent of traditional weight metrics, and should be considered an adjunct to BMI in risk models. Fully automated and normalized BC analysis of abdominal CT has promise to augment traditional cardiovascular risk prediction models.
基于CT的身体成分（BC）测量在历史上资源消耗过大，难以进行广泛分析，并且在预测心血管风险方面缺乏与传统体重指标的有力比较。本研究的目的是确定使用全自动深度学习算法从常规CT扫描中获得的BC测量是否能够独立于体重、BMI和其他心血管风险因素预测随后的心血管事件。这项回顾性研究纳入了9752名门诊患者（5519名女性和4233名男性；平均年龄53.2岁；890名患者自我报告种族为黑人，8862名患者自我报告种族为白人），这些患者于2012年1月至2012年12月在单一医疗系统接受了常规腹部CT检查，且在接受CT检查后3个月内未被诊断出患有重大心血管疾病或肿瘤。使用公开可用的代码，在L3椎体水平进行全自动深度学习BC分析，以确定三个BC区域（骨骼肌面积[SMA]、内脏脂肪面积[VFA]和皮下脂肪面积[SFA]）。使用年龄、性别和种族标准化的参考曲线为三个BC区域生成分数。通过电子病历确定随后的心肌梗死（MI）或中风情况。多变量调整的Cox比例风险模型用于确定CT检查后5年内三个BC区域分数发生MI或中风的风险比（HR），并对标准化体重、标准化BMI和其他心血管风险因素（吸烟状况、糖尿病诊断和收缩压）进行调整。在多变量模型中，年龄、种族和性别标准化的VFA与随后的MI风险相关（最高四分位数与最低四分位数相比的HR为1.31[95%CI，1.03 - 1.67]，总体效应P = 0.04）和中风风险相关（最高四分位数与最低四分位数相比的HR为1.46[95%CI，1.07 - 2.00]，总体效应P = 0.04）。在多变量模型中，标准化的SMA、SFA、体重和BMI与随后的MI或中风风险无关。从腹部CT检查的全自动标准化分析中得出的VFA可预测黑人和白人患者随后的MI或中风，独立于传统体重指标，并且在风险模型中应被视为BMI的辅助指标。腹部CT的全自动标准化BC分析有望增强传统的心血管风险预测模型。
