Impact of prehospital extracorporeal cardiopulmonary resuscitation for out-of-hospital cardiac arrest on survival with good neurological function: a systematic review and meta-analysis
Bibliographic record
Abstract
Aim: Prehospital extracorporeal cardiopulmonary resuscitation (ECPR) has been proposed to reduce delays in ECPR delivery in refractory out-of-hospital cardiac arrests (OHCA) and improve outcomes. Our aim was to synthesize the literature on outcomes of prehospital ECPR in OHCA, focusing on low-flow times (emergency call to extracorporeal blood flow) and survival with good neurological function, comparing them to in-hospital ECPR when possible. Methods: -analysis of studies reporting outcomes in adult OHCA patients treated with prehospital ECPR. Searches spanned seven databases and relevant grey literature (last updated January 21, 2025). Eligible studies included ≥ 5 patients. The primary outcome was survival with good neurological function (CPC 1-2). Pooled estimates were calculated using random-effects models. Meta-regression assessed the association between low-flow time and survival. Comparative analyses with in-hospital ECPR were performed when possible. Results: Eight cohort studies involving 305 patients (84% male, mean age 57) were included. Survival with good neurological function was 25% (95%CI: 17-35%). Mean low-flow time was 59 min (95%CI: 46-72). Meta-regression showed a significant inverse association between low-flow time and good neurological outcomes (β = -0.0271, 95%CI: -0.0536 to -0.0006; p = 0.045). Compared to in-hospital ECPR, prehospital ECPR showed no significant difference in survival (RR 1.23, 95%CI: 0.35-4.38) but was associated with significantly shorter low-flow times (mean difference -30 min, 95%CI: -44 to -16). Conclusion: Prehospital ECPR is associated with a 25% rate of survival with good neurological function. Shorter low-flow times were associated with improved outcomes.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".