Optimizing extracorporeal cardiopulmonary resuscitation delivery for out-of-hospital cardiac arrest: a Monte Carlo simulation study
Bibliographic record
Abstract
BACKGROUND: Extracorporeal cardiopulmonary resuscitation (ECPR) can improve outcomes in refractory out-of-hospital cardiac arrest (OHCA), but access is limited by geographic and system constraints. We aimed to compare the potential impact of different ECPR delivery strategies in an urban setting using simulation modeling. METHODS: We performed a Monte Carlo simulation using historical OHCA data (2015-2019) from Montreal's sole EMS. Each of 2000 iterations simulated 1240 annual OHCA cases using geospatial heatmaps. Patients meeting ECPR criteria (witnessed arrest, bystander CPR, age ≤ 70 years old) were included. We tested in-hospital models (2-, 3-, and 4-hospital), a rendezvous model, and two prehospital strategies: hospital-based deployment and an optimally located mobile team. Transport times were estimated using a machine learning model trained on real operational data. Outcomes included survival with favorable neurological outcome, proportion of patients achieving flow recovery at 60 min and low-flow time. RESULTS: On average, 255 patients were included per iteration. With in-hospital ECPR delivery, increasing from 2 to 4 hospitals modestly improved CPC 1-2 survival (25.3% vs 28.0%), flow recovery at 60 min (69.2% vs 75.1%), and low-flow interval (-2.4 min. Rendezvous yielded 28.8% CPC 1-2 survival, 77.3% flow-recovery at 60 min and -2.9 min low-flow time. Prehospital strategies had the greatest impact, improving CPC 1-2 survival (39.5% and 42.0%), flow-recovery at 60 min (99.7% and 100%), and low-flow time (-7.8 and -12 min) for hospital-based and optimally placed teams respectively. CONCLUSION: In this simulation, prehospital ECPR strategies showed the potential to increase survival, improve flow recovery at 60 min, and reduce low-flow times in urban 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".