Impact of sarcopenia and obesity on mortality in older adults with SARS-CoV-2 infection: automated deep learning body composition analysis in the NAPKON-SUEP cohort.

IF 5.4 2区 医学 Q1 INFECTIOUS DISEASES
Sabine Schluessel, Benedikt Mueller, Olivia Tausendfreund, Michaela Rippl, Linda Deissler, Sebastian Martini, Ralf Schmidmaier, Sophia Stoecklein, Michael Ingrisch, Sabine Blaschke, Gunnar Brandhorst, Peter Spieth, Kristin Lehnert, Peter Heuschmann, Susana M Nunes de Miranda, Michael Drey
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Abstract

Introduction: Severe respiratory infections pose a major challenge in clinical practice, especially in older adults. Body composition analysis could play a crucial role in risk assessment and therapeutic decision-making. This study investigates whether obesity or sarcopenia has a greater impact on mortality in patients with severe respiratory infections. The study focuses on the National Pandemic Cohort Network (NAPKON-SUEP) cohort, which includes patients over 60 years of age with confirmed severe COVID-19 pneumonia. An innovative approach was adopted, using pre-trained deep learning models for automated analysis of body composition based on routine thoracic CT scans.

Methods: The study included 157 hospitalized patients (mean age 70 ± 8 years, 41% women, mortality rate 39%) from the NAPKON-SUEP cohort at 57 study sites. A pre-trained deep learning model was used to analyze body composition (muscle, bone, fat, and intramuscular fat volumes) from thoracic CT images of the NAPKON-SUEP cohort. Binary logistic regression was performed to investigate the association between obesity, sarcopenia, and mortality.

Results: Non-survivors exhibited lower muscle volume (p = 0.043), higher intramuscular fat volume (p = 0.041), and a higher BMI (p = 0.031) compared to survivors. Among all body composition parameters, muscle volume adjusted to weight was the strongest predictor of mortality in the logistic regression model, even after adjusting for factors such as sex, age, diabetes, chronic lung disease and chronic kidney disease, (odds ratio = 0.516). In contrast, BMI did not show significant differences after adjustment for comorbidities.

Conclusion: This study identifies muscle volume derived from routine CT scans as a major predictor of survival in patients with severe respiratory infections. The results underscore the potential of AI supported CT-based body composition analysis for risk stratification and clinical decision making, not only for COVID-19 patients but also for all patients over 60 years of age with severe acute respiratory infections. The innovative application of pre-trained deep learning models opens up new possibilities for automated and standardized assessment in clinical practice.

肌少症和肥胖对SARS-CoV-2感染老年人死亡率的影响:NAPKON-SUEP队列中的自动深度学习体成分分析
严重呼吸道感染在临床实践中是一个重大挑战,特别是在老年人中。身体成分分析在风险评估和治疗决策中起着至关重要的作用。本研究探讨肥胖或肌肉减少症对严重呼吸道感染患者死亡率的影响更大。该研究的重点是国家大流行队列网络(NAPKON-SUEP)队列,其中包括60岁以上确诊的COVID-19严重肺炎患者。采用了一种创新的方法,使用预先训练的深度学习模型来自动分析基于常规胸部CT扫描的身体成分。方法:本研究纳入来自57个研究地点NAPKON-SUEP队列的157例住院患者(平均年龄70±8岁,女性41%,死亡率39%)。使用预训练的深度学习模型分析NAPKON-SUEP队列胸部CT图像中的身体组成(肌肉、骨骼、脂肪和肌内脂肪体积)。采用二元逻辑回归来调查肥胖、肌肉减少症和死亡率之间的关系。结果:与幸存者相比,非幸存者表现出较低的肌肉体积(p = 0.043),较高的肌内脂肪体积(p = 0.041)和较高的BMI (p = 0.031)。在所有身体组成参数中,在logistic回归模型中,即使在调整了性别、年龄、糖尿病、慢性肺部疾病和慢性肾脏疾病等因素后,体重调整后的肌肉体积是死亡率的最强预测因子(优势比= 0.516)。相比之下,在调整合并症后,BMI没有显着差异。结论:这项研究确定了常规CT扫描得出的肌肉体积是严重呼吸道感染患者生存的主要预测因素。这些结果强调了人工智能支持的基于ct的身体成分分析在风险分层和临床决策方面的潜力,不仅适用于COVID-19患者,而且适用于所有60岁以上的严重急性呼吸道感染患者。预训练深度学习模型的创新应用为临床实践中的自动化和标准化评估开辟了新的可能性。
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来源期刊
Infection
Infection 医学-传染病学
CiteScore
12.50
自引率
1.30%
发文量
224
审稿时长
6-12 weeks
期刊介绍: Infection is a journal dedicated to serving as a global forum for the presentation and discussion of clinically relevant information on infectious diseases. Its primary goal is to engage readers and contributors from various regions around the world in the exchange of knowledge about the etiology, pathogenesis, diagnosis, and treatment of infectious diseases, both in outpatient and inpatient settings. The journal covers a wide range of topics, including: Etiology: The study of the causes of infectious diseases. Pathogenesis: The process by which an infectious agent causes disease. Diagnosis: The methods and techniques used to identify infectious diseases. Treatment: The medical interventions and strategies employed to treat infectious diseases. Public Health: Issues of local, regional, or international significance related to infectious diseases, including prevention, control, and management strategies. Hospital Epidemiology: The study of the spread of infectious diseases within healthcare settings and the measures to prevent nosocomial infections. In addition to these, Infection also includes a specialized "Images" section, which focuses on high-quality visual content, such as images, photographs, and microscopic slides, accompanied by brief abstracts. This section is designed to highlight the clinical and diagnostic value of visual aids in the field of infectious diseases, as many conditions present with characteristic clinical signs that can be diagnosed through inspection, and imaging and microscopy are crucial for accurate diagnosis. The journal's comprehensive approach ensures that it remains a valuable resource for healthcare professionals and researchers in the field of infectious diseases.
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