Abstract 13200: Changes in Thrombi-Inflammatory Biomarkers Associated With Therapeutic Heparin in Non-Critically Ill Patients Hospitalized With COVID-19: A Pre-Specified Secondary Analysis of the ACTIV4a and ATTACC Randomized Clinical Trial
Bibliographic record
Abstract
Background: SARS-CoV-2 infection potentiates thromboinflammation contributing to poor outcomes in COVID-19. In non-critically ill patients hospitalized for COVID-19, therapeutic-dose heparin improves clinical outcomes. We hypothesized therapeutic-dose heparin impacts thromboinflammatory biomarkers in patients hospitalized for COVID-19 infection. Methods: We conducted a pre-specified secondary analysis of a multi-platform open-label, randomized trial comparing therapeutic-dose versus usual-care thromboprophylaxis-dose heparin in non-critically ill patients hospitalized for COVID-19. Inflammatory markers were analyzed using Wilcoxon rank-sum test and compared based on treatment. Ordinal logistic regression models evaluated for relative D-Dimer change (measured at baseline, day 1, day 3). Odds ratio of a 3-level ordinal outcome (death, survival with organ support, or survival without organ support through 21 days) was determined for relative change. Results: Of 1510 patients, 528 had a D-Dimer on baseline and day 1, and 432 on baseline and day 3. Median age was 60 years (IQR: 50-69) with 41% female and 66% under-represented minorities. Compared to usual-care, therapeutic-dose heparin was associated with a greater drop in D-Dimer at days 1 and 3; other biomarkers were unaffected by treatment (Table 1). D-Dimer at baseline and day 1 had an OR 0.93 (95% CI: 0.88, 0.98) whereas baseline and day 3 had an OR 0.86 (95% CI: 0.78, 0.94) of the ordinal outcome translating in a higher odds of surviving without the need for organ support (adjusted for treatment, gender, and age). Conclusion: In this randomized controlled trial therapeutic-dose heparin was associated with an early reduction in D-Dimer and clinical improvement, suggesting thromboinflammatory mechanisms whereby treatment conferred a clinical benefit. Temporal changes in D-dimer could predict treatment response or may signal need for alternative therapies.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".