Effects of inflammation on thrombosis and outcomes in COVID-19: secondary analysis of the ATTACC/ACTIV-4a trial
Bibliographic record
Abstract
Background: Patients hospitalized for COVID-19 are at high risk of thrombotic complications and organ failure, and often exhibit severe inflammation, which may contribute to hypercoagulability. Objectives: To determine whether patients hospitalized for COVID-19 experience differing frequencies of thrombotic and organ failure complications and derive variable benefits from therapeutic-dose heparin dependent on the extent of systemic inflammation and whether observed benefit from therapeutic-dose anticoagulation varies depending on the degree of systemic inflammation. Methods: We analyzed data from 1346 patients hospitalized for COVID-19 enrolled in the ATTACC and ACTIV-4a platforms who were randomized to therapeutic-dose heparin or usual care for whom levels of C-reactive protein (CRP) were reported at baseline. Results: Increased CRP was associated with worse patient outcomes, including a >98% posterior probability of increased organ support requirement, hospital length of stay, risk of 28-day mortality, and incidence of major thrombotic events or death (patients with CRP 40-100 mg/L or ≥100 mg/L compared to patients with CRP <40 mg/L). Patients with CRP 40 to 100 mg/L experienced the greatest degree of benefit from treatment with therapeutic doses of unfractionated or low molecular weight heparin compared with usual-care prophylactic doses. This was most significant for an increase in organ support-free days (odds ratio: 1.63; 95% confidence interval, 1.09-2.40; 97.9% posterior probability of beneficial effect), with trends toward benefit for other evaluated outcomes. Conclusion: Moderately ill patients hospitalized for COVID-19 with CRP between 40 mg/L and 100 mg/L derived the greatest benefit from treatment with therapeutic-dose heparin.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".