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.
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
{"title":"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.","authors":"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","doi":"10.1007/s15010-025-02555-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":13600,"journal":{"name":"Infection","volume":" ","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infection","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s15010-025-02555-3","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
引用次数: 0
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.
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
