Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
J Gerontol A Biol Sci Med Sci. 2021 Jan 18;76(2):277-285. doi: 10.1093/gerona/glaa141.
Muscle metrics derived from computed tomography (CT) are associated with adverse health events in older persons, but obtaining these metrics using current methods is not practical for large datasets. We developed a fully automated method for muscle measurement on CT images. This study aimed to determine the relationship between muscle measurements on CT with survival in a large multicenter trial of older adults.
The relationship between baseline paraspinous skeletal muscle area (SMA) and skeletal muscle density (SMD) and survival over 6 years was determined in 6,803 men and 4,558 women (baseline age: 60-69 years) in the National Lung Screening Trial (NLST). The automated machine learning pipeline selected appropriate CT series, chose a single image at T12, and segmented left paraspinous muscle, recording cross-sectional area and density. Associations between SMA and SMD with all-cause mortality were determined using sex-stratified Cox proportional hazards models, adjusted for age, race, height, weight, pack-years of smoking, and presence of diabetes, chronic lung disease, cardiovascular disease, and cancer at enrollment.
After a mean 6.44 ± 1.06 years of follow-up, 635 (9.33%) men and 265 (5.81%) women died. In men, higher SMA and SMD were associated with a lower risk of all-cause mortality, in fully adjusted models. A one-unit standard deviation increase was associated with a hazard ratio (HR) = 0.85 (95% confidence interval [CI] = 0.79, 0.91; p < .001) for SMA and HR = 0.91 (95% CI = 0.84, 0.98; p = .012) for SMD. In women, the associations did not reach significance.
Higher paraspinous SMA and SMD, automatically derived from CT exams, were associated with better survival in a large multicenter cohort of community-dwelling older men.
计算机断层扫描（CT）得出的肌肉指标与老年人的不良健康事件有关，但使用当前方法获取这些指标对于大型数据集来说并不实际。我们开发了一种全自动的 CT 图像肌肉测量方法。本研究旨在确定 CT 上的肌肉测量值与老年人大型多中心试验中生存的关系。
在国家肺癌筛查试验（NLST）中，确定了 6803 名男性和 4558 名女性（基线年龄：60-69 岁）基线时脊柱旁骨骼肌面积（SMA）和骨骼肌密度（SMD）与 6 年以上生存的关系。自动化机器学习管道选择合适的 CT 系列，在 T12 处选择单个图像，并对左侧脊柱旁肌肉进行分割，记录横截面积和密度。使用性别分层 Cox 比例风险模型确定 SMA 和 SMD 与全因死亡率之间的关联，调整因素包括年龄、种族、身高、体重、吸烟包年数以及糖尿病、慢性肺部疾病、心血管疾病和癌症的存在。
在平均 6.44 ± 1.06 年的随访后，635 名男性和 265 名女性死亡。在男性中，在完全调整的模型中，较高的 SMA 和 SMD 与较低的全因死亡率风险相关。与一个单位标准差的增加相关的危险比（HR）为 0.85（95%置信区间[CI] = 0.79，0.91；p <.001），SMA 为 0.91（95% CI = 0.84，0.98；p =.012）。在女性中，这些关联没有达到显著水平。
从 CT 检查中自动得出的较高脊柱旁 SMA 和 SMD 与大型多中心社区居住的老年男性队列中的生存更好相关。
