Effect of therapeutic-dose heparin on severe acute kidney injury and death in noncritically ill patients hospitalized for COVID-19: a prespecified secondary analysis of the ACTIV4a and ATTACC randomized trial
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) in patients with COVID-19 is partly mediated by thromboinflammation. In noncritically ill patients with COVID-19, therapeutic-dose anticoagulation with heparin increased the probability of survival to hospital discharge with reduced use of cardiovascular or respiratory organ support. Objectives: We investigated whether therapeutic-dose heparin reduces the incidence of AKI or death in noncritically ill patients hospitalized for COVID-19. Methods: We report a prespecified secondary analysis of the ACTIV4a and ATTACC open-label, multiplatform randomized trial of therapeutic-dose heparin vs usual-care pharmacologic thromboprophylaxis on the incidence of severe AKI (≥2-fold increase in serum creatinine or initiation of kidney replacement therapy (KDIGO stage 2 or 3) or all-cause mortality in noncritically ill patients hospitalized for COVID-19. Bayesian statistical models were adjusted for age, sex, D-dimer, enrollment period, country, site, and platform. Results: Among 1922 enrolled, 23 were excluded due to pre-existing end stage kidney disease and 205 were missing baseline or follow-up creatinine measurements. Severe AKI or death occurred in 4.4% participants assigned to therapeutic-dose heparin and 5.5% assigned to thromboprophylaxis (adjusted relative risk [aRR]: 0.72; 95% credible interval (CrI): 0.47, 1.10); the posterior probability of superiority for therapeutic-dose heparin (relative risk < 1.0) was 93.6%. Therapeutic-dose heparin was associated with a 97.7% probability of superiority to reduce the composite of stage 3 AKI or death (3.1% vs 4.6%; aRR: 0.64; 95% CrI: 0.40, 0.99) compared to thromboprophylaxis. Conclusion: Therapeutic-dose heparin was associated with a high probability of superiority to reduce the incidence of in-hospital severe AKI or death in patients hospitalized for COVID-19.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".