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
Affiliations (13)
Affiliations (13)
- Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany. [email protected].
- Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
- Department of Radiology, Clinical Data Science, LMU University Hospital, LMU Munich, Munich, Germany.
- Emergency Department, University Medical Center Goettingen, Göttingen, Germany.
- University Medicine Oldenburg, University Institute for Clinical Chemistry and Laboratory Medicine, Oldenburg, Germany.
- Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany.
- DZHK (German Center for Cardiovascular Research), University Medicine Greifswald, Greifswald, Germany.
- Institute of Medical Data Science, University Hospital Würzburg, Würzburg, Germany.
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.
- Faculty of Medicine, Institute for Digital Medicine and Clinical Data Science, Goethe University Frankfurt, Frankfurt, Germany.
- Department I for Internal Medicine, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany.
Abstract
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. 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. 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. 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.