Extracorporeal cardiopulmonary resuscitation versus conventional CPR in cardiac arrest: an updated meta-analysis and trial sequential analysis
Bibliographic record
Abstract
BACKGROUND: Extracorporeal cardiopulmonary resuscitation (ECPR) may reduce mortality and improve neurological outcomes in patients with cardiac arrest. We updated our existing meta-analysis and trial sequential analysis to further evaluate ECPR compared to conventional CPR (CCPR). METHODS: We searched three international databases from 1 January 2000 through 1 November 2023, for randomised controlled trials or propensity score matched studies (PSMs) comparing ECPR to CCPR in both out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA). We conducted an updated random-effects meta-analysis, with the primary outcome being in-hospital mortality. Secondary outcomes included short- and long-term favourable neurological outcome and survival (30 days-1 year). We also conducted a trial sequential analysis to evaluate the required information size in the meta-analysis to detect a clinically relevant reduction in mortality. RESULTS: We included 13 studies with 14 pairwise comparisons (6336 ECPR and 7712 CCPR) in our updated meta-analysis. ECPR was associated with greater precision in reducing overall in-hospital mortality (OR 0.63, 95% CI 0.50-0.79, high certainty), to which the trial sequential analysis was concordant. The addition of recent studies revealed a newly significant decrease in mortality in OHCA (OR 0.62, 95% CI 0.45-0.84). Re-analysis of relevant secondary outcomes reaffirmed our initial findings of favourable short-term neurological outcomes and survival up to 30 days. Estimates for long-term neurological outcome and 90-day-1-year survival remained unchanged. CONCLUSIONS: We found that ECPR reduces in-hospital mortality, improves neurological outcome, and 30-day survival. We additionally found a newly significant benefit in OHCA, suggesting that ECPR may be considered in both IHCA and 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 | 0.041 | 0.083 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.052 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".