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通过胸部CT全自动身体成分分析提取的生物标志物与SARS-CoV-2感染结果的严重程度相关。

Biomarkers extracted by fully automated body composition analysis from chest CT correlate with SARS-CoV-2 outcome severity.

作者信息

Hosch René, Kattner Simone, Berger Marc Moritz, Brenner Thorsten, Haubold Johannes, Kleesiek Jens, Koitka Sven, Kroll Lennard, Kureishi Anisa, Flaschel Nils, Nensa Felix

机构信息

Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.

Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany.

出版信息

Sci Rep. 2022 Sep 30;12(1):16411. doi: 10.1038/s41598-022-20419-w.

DOI:10.1038/s41598-022-20419-w
PMID:36180519
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9524347/
Abstract

The complex process of manual biomarker extraction from body composition analysis (BCA) has far restricted the analysis of SARS-CoV-2 outcomes to small patient cohorts and a limited number of tissue types. We investigate the association of two BCA-based biomarkers with the development of severe SARS-CoV-2 infections for 918 patients (354 female, 564 male) regarding disease severity and mortality (186 deceased). Multiple tissues, such as muscle, bone, or adipose tissue are used and acquired with a deep-learning-based, fully-automated BCA from computed tomography images of the chest. The BCA features and markers were univariately analyzed with a Shapiro-Wilk and two-sided Mann-Whitney-U test. In a multivariate approach, obtained markers were adjusted by a defined set of laboratory parameters promoted by other studies. Subsequently, the relationship between the markers and two endpoints, namely severity and mortality, was investigated with regard to statistical significance. The univariate approach showed that the muscle volume was significant for female (p ≤ 0.001, p ≤ 0.0001) and male patients (p = 0.018, p ≤ 0.0001) regarding the severity and mortality endpoints. For male patients, the intra- and intermuscular adipose tissue (IMAT) (p ≤ 0.0001), epicardial adipose tissue (EAT) (p ≤ 0.001) and pericardial adipose tissue (PAT) (p ≤ 0.0001) were significant regarding the severity outcome. With the mortality outcome, muscle (p ≤ 0.0001), IMAT (p ≤ 0.001), EAT (p = 0.011) and PAT (p = 0.003) remained significant. For female patients, bone (p ≤ 0.001), IMAT (p = 0.032) and PAT (p = 0.047) were significant in univariate analyses regarding the severity and bone (p = 0.005) regarding the mortality. Furthermore, the defined sarcopenia marker (p ≤ 0.0001, for female and male) was significant for both endpoints. The cardiac marker was significant for severity (p = 0.014, p ≤ 0.0001) and for mortality (p ≤ 0.0001, p ≤ 0.0001) endpoint for both genders. The multivariate logistic regression showed that the sarcopenia marker was significant (p = 0.006, p = 0.002) for both endpoints (OR = 0.42, 95% CI: 0.23-0.78, OR = 0.34, 95% CI: 0.17-0.67). The cardiac marker showed significance (p = 0.018) only for the severity endpoint (OR = 1.42, 95% CI 1.06-1.90). The association between BCA-based sarcopenia and cardiac biomarkers and disease severity and mortality suggests that these biomarkers can contribute to the risk stratification of SARS-CoV-2 patients. Patients with a higher cardiac marker and a lower sarcopenia marker are at risk for a severe course or death. Whether those biomarkers hold similar importance for other pneumonia-related diseases requires further investigation.

摘要

从身体成分分析(BCA)中手动提取生物标志物的复杂过程,极大地限制了对新冠病毒感染结果的分析,只能针对小患者队列和有限数量的组织类型进行。我们调查了基于BCA的两种生物标志物与918例患者(354名女性,564名男性)严重新冠病毒感染发展之间的关联,涉及疾病严重程度和死亡率(186例死亡)。使用了多种组织,如肌肉、骨骼或脂肪组织,并通过基于深度学习的全自动BCA从胸部计算机断层扫描图像中获取。对BCA特征和标志物进行单变量分析,采用夏皮罗-威尔克检验和双侧曼-惠特尼-U检验。在多变量分析中,通过其他研究提出的一组特定实验室参数对获得的标志物进行调整。随后,研究了标志物与两个终点(即严重程度和死亡率)之间的关系,并分析其统计学意义。单变量分析表明,就严重程度和死亡率终点而言,肌肉体积对女性(p≤0.001,p≤0.0001)和男性患者(p = 0.018,p≤0.0001)具有显著意义。对于男性患者,肌内和肌间脂肪组织(IMAT)(p≤0.0001)、心外膜脂肪组织(EAT)(p≤0.001)和心包脂肪组织(PAT)(p≤0.0001)在严重程度结果方面具有显著意义。就死亡率结果而言,肌肉(p≤0.0001)、IMAT(p≤0.001)、EAT(p = 0.011)和PAT(p = 0.003)仍然具有显著意义。对于女性患者,在单变量分析中,骨骼(p≤0.001)、IMAT(p = 0.032)和PAT(p = 0.047)在严重程度方面具有显著意义,骨骼(p = 0.005)在死亡率方面具有显著意义。此外,定义的肌肉减少症标志物(女性和男性均为p≤0.0001)在两个终点上均具有显著意义。心脏标志物在严重程度(p = 0.014,p≤0.0001)和死亡率(p≤0.0001,p≤0.0001)终点上对两性均具有显著意义。多变量逻辑回归表明,肌肉减少症标志物在两个终点上均具有显著意义(p = 0.006,p = 0.002)(OR = 0.42,95%CI:0.23 - 0.78,OR = 0.34,95%CI:0.17 - 0.67)。心脏标志物仅在严重程度终点上具有显著意义(p = 0.018)(OR = 1.42,95%CI 1.06 - 1.90)。基于BCA的肌肉减少症和心脏生物标志物与疾病严重程度和死亡率之间的关联表明,这些生物标志物有助于对新冠病毒患者进行风险分层。心脏标志物较高且肌肉减少症标志物较低的患者有发生严重病程或死亡的风险。这些生物标志物对其他肺炎相关疾病是否具有类似重要性,需要进一步研究。

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