CT-Defined Pectoralis Muscle Density Predicts 30-Day Mortality in Hospitalized Patients with COVID-19: A Nationwide Multicenter Study.

IF 3.8 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Academic Radiology Pub Date : 2024-12-14 DOI:10.1016/j.acra.2024.11.054
Andreas Michael Bucher, Julius Behrend, Constantin Ehrengut, Lukas Müller, Tilman Emrich, Dominik Schramm, Alena Akinina, Roman Kloeckner, Malte Sieren, Lennart Berkel, Christiane Kuhl, Marwin-Jonathan Sähn, Matthias A Fink, Dorottya Móré, Bohdan Melekh, Hakan Kardas, Felix G Meinel, Hanna Schön, Norman Kornemann, Diane Miriam Renz, Nora Lubina, Claudia Wollny, Marcus Both, Joe Watkinson, Sophia Stöcklein, Andreas Mittermeier, Gizem Abaci, Matthias May, Lisa Siegler, Tobias Penzkofer, Maximilian Lindholz, Miriam Balzer, Moon-Sung Kim, Christian Römer, Niklas Wrede, Sophie Götz, Julia Breckow, Jan Borggrefe, Hans Jonas Meyer, Alexey Surov
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Abstract

Rationale and objectives: The prognostic role of computed tomography (CT)-defined skeletal muscle features in COVID-19 is still under investigation. The aim of the present study was to evaluate the prognostic role of CT-defined skeletal muscle area and density in patients with COVID-19 in a multicenter setting.

Materials and methods: This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the COVID-19 pandemic). The acquired sample included 1379 patients, 389 (28.2%) women and 990 (71.8%) men. In each case, chest CT was analyzed and pectoralis muscle area and density were calculated. Data were analyzed by means of descriptive statistics. Group differences were calculated using the Mann-Whitney-U test and Fisher's exact test. Univariable and multivariable logistic regression analyses were performed.

Results: The 30-day mortality was 17.9%. Using median values as thresholds, low pectoralis muscle density (LPMD) was a strong and independent predictor of 30-day mortality, HR=2.97, 95%-CI: 1.52-5.80, p=0.001. Also in male patients, LPMD predicted independently 30-day mortality, HR=2.96, 95%-CI: 1.42-6.18, p=0.004. In female patients, the analyzed pectoralis muscle parameters did not predict 30-day mortality. For patients under 60 years of age, LPMD was strongly associated with 30-day mortality, HR=2.72, 95%-CI: 1.17;6.30, p=0.019. For patients over 60 years of age, pectoralis muscle parameters could not predict 30-day mortality.

Conclusion: In male patients with COVID-19, low pectoralis muscle density is strongly associated with 30-day mortality and can be used for risk stratification. In female patients with COVID-19, pectoralis muscle parameters cannot predict 30-day mortality.

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理由和目的:计算机断层扫描(CT)定义的骨骼肌特征在COVID-19中的预后作用仍在研究中。本研究的目的是在多中心环境下评估 CT 定义的骨骼肌面积和密度在 COVID-19 患者中的预后作用:这项回顾性研究是德国多中心项目 RACOON(COVID-19 大流行病放射学合作网络)的一部分。获得的样本包括 1379 名患者,其中女性 389 人(28.2%),男性 990 人(71.8%)。对每个病例都进行了胸部 CT 分析,并计算了胸肌面积和密度。数据采用描述性统计方法进行分析。组间差异采用 Mann-Whitney-U 检验和费雪精确检验进行计算。进行了单变量和多变量逻辑回归分析:结果:30 天死亡率为 17.9%。以中位数值为阈值,胸肌密度低(LPMD)是30天死亡率的一个强有力的独立预测因素,HR=2.97,95%-CI:1.52-5.80,P=0.001。在男性患者中,LPMD 也是预测 30 天死亡率的独立指标,HR=2.96,95%-CI:1.42-6.18,p=0.004。在女性患者中,分析的胸肌参数并不能预测 30 天的死亡率。对于 60 岁以下的患者,LPMD 与 30 天死亡率密切相关,HR=2.72,95%-CI:1.17;6.30,P=0.019。对于 60 岁以上的患者,胸肌参数不能预测 30 天死亡率:结论:在 COVID-19 男性患者中,低胸肌密度与 30 天死亡率密切相关,可用于风险分层。在 COVID-19 女性患者中,胸肌参数不能预测 30 天死亡率。
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来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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