Cardiac stress biomarkers after red blood cell transfusion in patients at risk for transfusion‐associated circulatory overload: a prospective observational study
Bibliographic record
Abstract
BACKGROUND: Transfusion-associated circulatory overload (TACO) is a leading cause of serious reactions. In regard to TACO, little is known regarding biomarkers as a predictor, their most informative timing, or thresholds of significance or differentiation from other reactions. STUDY DESIGN AND METHODS: In this study of inpatients at risk for TACO (age ≥ 50 years) receiving 1 red blood cell unit, cardiac biomarkers, brain natriuretic peptide (BNP), N-terminal pro-BNP (NT-proBNP), and high-sensitivity troponin were measured at baseline, 6 to 12 hours (except troponin) posttransfusion, and 18 to 24 hours posttransfusion. Primary outcome was a critical increase in biomarkers (>1.5-fold increase and supranormal) at 18 to 24 hours. RESULTS: Fifty-one patients were analyzed; 29% had cardiovascular disease, 73% had one or more cardiac risk factors, and 50% took cardiac or antihypertensive therapies. Although eight (16%) developed an increase in systolic pressure of at least 30 mmHg and four (8%) reported dyspnea and/or cough, none had TACO. At baseline, BNP level was more than 100 ng/L in 59% and NT-proBNP was more than 300 pg/mL in 83%. A total of 25% had a BNP critical increase, 33% had a NT-proBNP critical increase, and 2% had a troponin critical increase at 18 to 24 hours. Overall, 38% had at least one biomarker critical increase and NT-proBNP/BNP concordance was 84%. An increase in the NT-proBNP (>1.5-fold increase and >300 pg/mL) at 18 to 24 hours was the commonest biomarker change. CONCLUSIONS: An increase of the NT-proBNP at 18 to 24 hours may be the preferred surrogate marker for identifying a patient experiencing physiologic difficulty in handling the volume challenge. Larger studies are needed to clarify the risk of TACO for a given pretransfusion biomarker profile and the correlation between TACO and increase in biomarkers after transfusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".