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Record W4417245981 · doi:10.1016/j.clnesp.2025.102878

Impact of muscle mass, muscle density and obesity on clinical outcomes in critically ill patients with COVID-19

2025· article· en· W4417245981 on OpenAlexafffund
Paulo César Ribeiro, Eduardo Leite Vieira Costa, Davi dos Santos Romão, Naiara Lima Matos, Montserrat Montes Ibarra, Carla M. Prado

Bibliographic record

VenueClinical Nutrition ESPEN · 2025
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsCritically illObesityCritical illnessSevere obesityMuscle strength

Abstract

fetched live from OpenAlex

OBJECTIVE: Obesity is generally recognized as an independent risk factor for poor outcomes in Coronavirus Disease 2019 (COVID-19); however, some studies report a paradoxical protective effect. Findings may be mediated by low muscle mass, a relevant predictor of poor clinical outcomes in critically ill patients. We aimed to investigate the association of body mass index (BMI), baseline low muscle mass index (SMI) and low muscle density (SMD) with clinical outcomes in critically ill patients with COVID-19. DESIGN: This retrospective cohort study was conducted at a tertiary care center in São Paulo, Brazil. We included all consecutive patients admitted to the intensive care unit (ICU) from March 1st, 2020, to May 31st, 2021, with a confirmed diagnosis of COVID-19 and who had a measured SMI and SMD by thoracic computed tomography (CT) at admission. SMI and SMD were assessed from a transverse image at the level of the 12th thoracic vertebra (T12), and BMI at admission was calculated. The association between coprimary exposures BMI, low SMI, low SMD and hospital mortality was assessed through multivariable analysis accounting for confounding factors such as age, sex, Simplified Acute Physiology Score 3 (SAPS 3) and comorbidities. RESULTS: A total of 962 patients were included; 63.7 (±15.3) years; 75.8 % males. SMI was assessed in all patients; however, 33 with contrast CTs were excluded from the SMD analysis. The prevalence of low SMI was 21.6 % (208/962). The prevalence of low SMD was 22.7 % (211/929). A total of 391 (40.6 %) patients were classified as overweight, and 393 (40.8 %) as having obesity. Hospital mortality was 14.3 %, increasing to 26.2 % for patients aged ≥65 years. We found no significant association between BMI, SMI or SMD and hospital mortality. Patients with low SMI were more likely to undergo extracorporeal membrane oxygenation (P = 0.045), required longer duration of mechanical ventilation (MV) (p < 0.001), and had prolonged ICU and hospital stays (p < 0.001). Low SMD was independently associated only with ICU readmission (p = 0.034) and longer hospital stay (p < 0.001). Patients with a higher BMI were more likely to be intubated and placed under MV (p < 0.001). Higher BMI was also associated with longer ICU (p = 0.001) and hospital stays (p = 0.049). Patients with obesity and low SMI had longer ICU (p = 0.033), and hospital (p < 0.001) stays, and extended MV duration compared to those with obesity and normal SMI (p = 0.003). CONCLUSIONS: BMI, low SMI, and low SMD were not associated with in-hospital mortality in this cohort. However, these parameters were important predictors of morbidity, including longer ICU and hospital stays, greater need for mechanical ventilation, and ICU readmissions. These findings highlight the importance of assessing body composition parameters in critically ill patients with COVID-19.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.088
GPT teacher head0.476
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes2
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