Ultrasonographic Abdominal Visceral Fat Thickness as an Independent Predictive Factor of Mortality in COVID-19 Patients: A Prospective Cohort Study
Bibliographic record
Abstract
Background: Visceral fat has been associated with severe COVID-19 outcomes due to its pro-inflammatory effects. While computed tomography (CT) is the standard for measuring visceral fat, ultrasound (US) offers a non-invasive and accessible alternative. This study aimed to evaluate whether abdominal fat thickness measured via US predicts clinical outcomes in COVID-19 pneumonia. Method: This prospective cohort study included 83 hospitalized COVID-19 patients. Visceral fat thickness (VFT), subcutaneous fat thickness (SFT), and preperitoneal fat thickness (PFT) were measured with US. The primary outcome of interest was mortality. Multivariable logistic regression was used to analyze associations, adjusting for confounders such as age, sex, and comorbidities. Results: 83 patients with a median age of 62 (IQR: 49–73) years were included (53% male). Mortality was significantly higher in male (61.4% vs. 38.6%, p = 0.018); older age (73.5 vs. 59.5; p = 0.003); patients with lower median blood oxygen saturation (80% vs. 88%, p < 0.001), higher median levels of AST (66 vs. 39.5, p = 0.002), BUN (67 vs. 36.5, p < 0.001), and creatinine (1.6 vs. 1.2, p < 0.001); and patients under mechanical ventilation (p < 0.01). Based on multivariate logistic regression analysis the independent predictors of mortality were VFT (aOR: 1.025, 95% CI: 1.001–1.051, p = 0.047), old age (aOR: 1.064, 95% CI: 1.016–1.115, p = 0.008), and male sex (aOR: 4.430, 95% CI: 1.169–16.769, p = 0.029). In contrast, SFT had an aOR of 1.059 (95% CI: 0.966–1.161, p = 0.223), and PFT had an aOR of 1.016 (95% CI: 0.880–1.172, p = 0.830), neither of which was statistically significant. The area under the ROC curve of VFT in predicting mortality was 0.643. The optimal cutoff value for VFT, determined using the Youden Index, was 80.4. At this cutoff point, the sensitivity was 52.2% while the specificity was 75.0%. Conclusion: It seems that ultrasound-measured VFT is a potential predictor of mortality in hospitalized COVID-19 patients, offering a cost-effective and accessible tool for risk assessment. Further research is needed to confirm its broader applicability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".