A Comparison between Conventional and Extracorporeal Cardiopulmonary Resuscitation in Out-of-Hospital Cardiac Arrest: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
There is limited evidence comparing the use of extracorporeal cardiopulmonary resuscitation (ECPR) to CPR in the management of refractory out-of-hospital cardiac arrest (OHCA). We conducted a systematic review and meta-analysis to compare survival and neurologic outcomes associated with ECPR versus CPR in the management of OHCA. We searched PubMed, EMBASE, and Scopus to identify observational studies and randomized controlled trials comparing ECPR and CPR. We used the Newcastle−Ottawa Scale and Cochrane’s risk-of-bias tool to assess studies’ quality. We used random-effects models to compare outcomes between the pooled populations and moderator analysis to identify sources of heterogeneity and perform subgroup analysis. We identified 2088 articles and included 13, with 18,620 patients with OHCA. A total of 16,701 received CPR and 1919 received ECPR. Compared with CPR, ECPR was associated with higher odds of achieving favorable neurologic outcomes at 3 (OR 5, 95% CI 1.90−13.1, p < 0.01) and 6 months (OR 4.44, 95% CI 2.3−8.5, p < 0.01). We did not find a significant survival benefit or impact on neurologic outcomes at hospital discharge or 1 month following arrest. ECPR is a promising but resource-intensive intervention with the potential to improve long-term outcomes among patients with OHCA.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".