The association of venous thromboembolism with blood transfusion in kidney transplant patients
Bibliographic record
Abstract
BACKGROUND: Red blood cell transfusion (RBCT) is common after kidney transplantation and could have pro-thrombotic effects predisposing to venous thromboembolism (VTE). The risks for developing of VTE after RBCT in kidney transplant patients are unknown. STUDY DESIGN AND METHODS: This was a retrospective cohort study of adult kidney transplant recipients from 2002 to 2018. The exposure of interest was receipt of RBCT after transplant. Cox proportional hazards models were used to calculate hazard ratios (HR) for the outcomes of venous thromboembolism [VTE] (deep venous thrombosis [DVT] or pulmonary embolism [PE]) using RBCT as a time-varying, cumulative exposure. RESULTS: Out of 1258 kidney transplants recipients, 468 (37%) were transfused during the study period. Seventy-nine study participants (6.3%) developed VTE, 72 DVT (5.7%), and 22 PE (1.8%). For the receipt of 1, 2, 3-5, and >5 RBCT, compared to individuals never transfused, the number of events and adjusted HR (95%CI) for VTE were 6 (6.2%) HR 1.57 (0.69-3.58), 9 (7.6%) HR 2.54 (1.30-4.96), 15 (11.9%) HR 2.73 (1.38-5.41), and 23 (18.1%) HR 5.77 (2.99-11.14) respectively; for DVT, it was 6 (6.2%) HR 1.94 (0.84-4.48), 9 (7.6%) HR 2.92 (1.44-5.94), 14 (11.1%) HR 3.29 (1.63-6.65), and 21 (16.5%) HR 6.97 (3.53-13.76), respectively. For PE, among transfused individuals, there were 14 events (3.0%) and the HR was 2.40 (1.02-5.61). CONCLUSION: The risks for developing VTE, DVT, and PE were significantly increased in kidney transplant patients receiving RBCT after transplant. Receipt of RBCT should prompt considerations for judicious monitoring and assessment for thrombosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".