Risk of Incident Bleeding After AKI: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: End-stage kidney disease (ESKD) causes bleeding diathesis; however, whether these findings are extrapolable to acute kidney injury (AKI) remains uncertain. We assessed whether AKI is associated with an increased risk of de novo bleeding. Methods: We conducted a one-year single-center retrospective cohort study, excluding readmissions, admissions less than 24 hours, ESKD or kidney transplant patients. The primary outcome was the development of incident bleeding analyzed by multivariate time-dependent Cox models and independently adjudicated by two investigators. Results: In 1,001 patients, bleeding occurred in 48% of AKI and 57% of non-AKI patients (p=0.007). To identify predictors of incident bleeding, we excluded patients who bled before ICU (n=488). In bleeding-free patients (n=513), we observed a trend toward higher risks of bleeding in AKI (22% vs. 16%,p=0.06), and a higher risk of bleeding in AKI-requiring dialysis (38% vs. 17%, p=0.01). Cirrhosis, AKI-requiring dialysis, anticoagulation, and coronary artery disease were associated with bleeding (HR 3.67, 95%CI:1.33-10.25; HR 2.82, 95%CI:1.26-6.32; HR 2.34, 95%CI:1.45-3.80; and HR 1.84, 95%CI:1.06-3.20, respectively), while SOFA score and sepsis had a protective association (HR 0.92 95%CI:0.84-0.99 and HR 0.55, 95%CI:0.34-0.91, respectively). Incident bleeding was not associated with mortality. Conclusions: AKI-requiring dialysis was associated with incident bleeding, independent of anticoagulant administration. Studies are needed to better understand how AKI affects coagulation and clinical outcomes. Funding: Government Support - Non-U.S.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".