Ultrafiltration reduces blood transfusions following cardiac surgery: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Although used routinely in pediatric patients, ultrafiltration techniques that reverse hemodilution are infrequently used in adults. Data from small, unblinded clinical trials suggest that the use of ultrafiltration can reduce inflammatory mediators, improve cardiac function, and reduce hemodilution. We conducted a meta-analysis of randomized trials to evaluate the effects of ultrafiltration on blood transfusions and blood loss following adult cardiac surgery. METHODS: Medline, EMBASE, and Cochrane databases were searched and randomized controlled trials evaluating modified and/or conventional ultrafiltration, meeting pre-determined selection criteria, were obtained. Quality evaluation and data extraction were performed by two independent observers blinded to study source. Random effects models were used to determine pooled effect estimates and sources of heterogeneity were explored using meta-regression. RESULTS: One hundred and thirty two studies were screened and 10 randomized trials evaluating 1004 patients (control, n = 495; ultrafiltration, n = 509) were identified of which only two were double-blinded. The use of ultrafiltration was associated with a reduction in postoperative blood transfusions (weighted mean difference [95% CI] of -0.73 units [-1.16, -0.31]; p = 0.001). This reduction was greater in studies evaluating modified ultrafiltration. Use of ultrafiltration was also associated with reduced postoperative bleeding (-70 ml, [-118, -21]; p = 0.005), which was driven primarily by trials evaluating modified rather than conventional ultrafiltration. CONCLUSIONS: Use of ultrafiltration is associated with a significant reduction in postoperative blood transfusions as well as reduced bleeding in adults undergoing cardiac surgery. The efficacy and cost-effectiveness of ultrafiltration as a blood conservations strategy should be evaluated in a large, randomized, double-blinded study.
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 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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.052 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".