Evaluation of clinical outcomes in patients treated with heparin or direct thrombin inhibitors during extracorporeal membrane oxygenation: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background The number of patients treated with extracorporeal membrane oxygenation (ECMO) devices is increasing. Anticoagulation therapy is crucial to prevent thrombosis during ECMO therapy. Predominantly, heparin has been used as primary anticoagulant but direct thrombin inhibitors (DTI) have been established as alternatives. The aim of this systematic review and meta-analysis was to evaluate clinical outcomes in patients treated with heparin compared to different DTI during ECMO. Methods A systematic search was conducted. Full scientific articles were sought for inclusion if heparin anticoagulation was compared to DTI (argatroban/bivalirudin) in ECMO patients. Risk of bias was assessed by Newcastle Ottawa scale. Primary endpoint was in-hospital mortality. Bleeding events, thrombotic events, hours of ECMO support, days of hospital stay, percentage of time within therapeutic range and time to therapeutic range were extracted from full texts as secondary endpoints. Results were presented as Forrest-plots. GRADE was used for confidence assessment in outcomes. Results Systematic search identified 4.385 records, thereof 18 retrospective studies for a total of 1942 patients, complied with the predefined eligibility criteria:15 studies investigated bivalirudin and 3 studies investigated argatroban versus heparin. Risk of bias was high for most studies. In-hospital mortality, major bleeding events and pump-related thrombosis were less frequent in DTI group as compared to heparin [mortality—OR 0.69, 95% CI 0.54–0.86; major bleeding—OR 0.48, 95% CI 0.29–0.81; pump thrombosis—OR 0.55, 95% CI 0.40–0.76]. Additionally, percentage of time within therapeutic range was higher for DTI [SMD 0.54, 95% CI 0.14–0.94]. GRADE approach revealed a very low level of certainty for each outcome. Conclusion In this meta-analysis, DTI and especially bivalirudin showed beneficial effects on clinical outcomes in ECMO patients as compared to heparin. However, due to the lack of randomized trials, certainty of evidence is low. Trial Registration This systematic review and meta-analysis was prospectively registered at PROSPERO data base (reference number CRD42021237252 ). Graphical Abstract
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".