Pericardial Fat Tissue as a Predictor of the Severity of Acute Coronavirus Infection COVID-19
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) and obesity remain pressing global health concerns. Identifying predictors of severe disease is of particular importance. Pericardial fat tissue (PFT) is a known source of metainflammation due to its secretion of adipocytokines and inflammatory mediators. Moreover, cytokine storm plays a major role in COVID-19-related mortality. This study aimed to investigate the association between preexisting PFT volume and inflammatory markers in patients with COVID-19. Methods: The study included 290 hospitalized patients with confirmed COVID-19 infection. Based on PFT volume (above or below 3.45 cm3), patients were divided into two groups: with and without pericardial obesity (PO), consisting of 132 and 158 individuals, respectively. Clinical, laboratory, and imaging data were analyzed. Statistical analysis was performed in Statistica 12.0. Results: Significant intergroup differences were observed in the PO group for the following variables: male sex (P < 0.001), body mass index (BMI) (P < 0.001), obesity (P < 0.001), and history of diabetes mellitus (P = 0.003). No significant differences were found in lung computed tomography (CT) severity scores. However, patients with PO showed significantly lower oxygen saturation (SpO2) levels (P = 0.014) and a higher frequency of SpO2 ≤ 93% (P = 0.012). Ferritin levels were significantly higher in the PO group (median 440 (274.00 - 552.70) vs. 292.55 (156.00 - 521.50), P = 0.010). Linear correlation analysis revealed a positive association between PFT volume and age, BMI, glucose, ferritin, C-reactive protein, and D-dimer levels, and a negative correlation with oxygen saturation. Multivariate logistic regression confirmed an independent association between PFT volume and SpO2 ≤ 93%. Receiver operating characteristic (ROC) analysis identified a threshold PFT volume of 3.45 cm3 for predicting increased risk of severe COVID-19 (SpO2 ≤ 93%), with sensitivity of 66.7%, specificity of 65.0%, and area under the curve (AUC) of 0.710 (95% confidence interval (CI): 0.551 - 0.868, P < 0.001). Conclusions: Our data suggest that a PFT volume greater than 3.45 cm3 is a potential predictor of severe 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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".