Significance of Hypoalbuminemia in the Development of Thromboembolic Complications in Severe Cases of SARS-CoV-2 Coronavirus Infection
Bibliographic record
Abstract
Background: The course of coronavirus disease 2019 (COVID-19) is associated with the progression of a wide range of complications, among which thrombosis and thromboembolism are of particular importance. The significance of hypoalbuminemia in the development of thromboembolic complications (TECs) in patients with a severe course of COVID-19 is currently under active discussion. The objective of our study was to evaluate the significance of hypoalbuminemia in the development of TECs in patients with severe SARS-CoV-2 coronavirus infection. Methods: In a single-center observational retrospective study, case histories of 1,634 patients with a verified diagnosis of SARS-CoV-2 coronavirus infection were analyzed. Patients were divided into two groups according to the presence of TECs: 127 patients with venous TECs constituted the main group and 1,507 patients, in whom the course of COVID-19 was not complicated by the development of TECs, constituted the comparison group. Results: The patients with TECs were older, and the prevalence of arterial hypertension, coronary heart disease, chronic heart failure, chronic kidney disease, and diabetes mellitus was higher than that in the comparison group. A single-factor regression analysis showed that a decrease in albumin levels of less than 35 g/L is associated with an eightfold increase in the risk of developing TECs in patients with severe SARS-CoV-2 coronavirus infection (area under the curve (AUC): 0.815, odds ratio (OR): 8.5389, 95% confidence interval (CI): 4.5637 - 15.977, P < 0.001). The sensitivity of the method was 76.34%, and the specificity was 72.58%. Conclusion: The study revealed that hypoalbuminemia is a predictor of development of TECs in severe cases of SARS-CoV-2 coronavirus infection.
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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 | 0.005 |
| 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) | 0.001 | 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".