Abstract 19833: A New Therapeutic Strategy for Refractory Cardiac Arrest Including Prehospital Ecmo: A Comparison Study
Bibliographic record
Abstract
Introduction: ECMO is a treatment of refractory cardiac arrest (RCA). The survival rate depends directly on the low flow (LF) period. To reduce LF we have implemented a new RCA management strategy. This work aims to evaluate the impact of this strategy on survival. Methods: This is an observational retrospective single-center study. The ECMO team is composed of a doctor, a nurse and a paramedic.The ethical committee validated the study. During period 1 (11 / 2011-12 / 2014), the strategy associated an ECMO team alert after 10 min of ALS, followed by the implementation of ECMO either in-hospital or out of hospital depending on the ECMO team availability. During period 2 (01-06 / 2015), the strategy associated an ECMO Team alert at the departure of the BLS Ambulance, with a dedicated ECMO team 24/24 H, and a systematic assessment for ECMO implementation either in hospital or out of hospital according to logistical constraints. A specific training was given to dispatch centers, and all ALS units. Qualitative variables were compared using the χ2 and Fisher exact tests, whereas quantitative variables were compared by Wilcoxon tests. Results: The results are in Table 1. The characteristics are similar. The significant results when comparing periods 1 and 2 include an increased survival rate, a decreased LF. Conclusions: The survival rate of RCA treated with ECMO depends on the LF. Unfortunately even by performing a single scoop and run strategy, an important proportion of patients can not be rapidly put on ECMO. We have developped a LF reduction strategy involving the implementation of ECMO either in hospital or in the out of hospital setting. The optimization of this strategy by an earlier alert, a dedicated ECMO team and training has a major significant impact on survival. This increase of survival is probably correlated to the decrease of the LF, especially in the prehospital group. These results need to be confirmed by a RCT comparing in hospital and out of hospital ECMO implementation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".