The effect of percutaneous coronary intervention after extracorporeal cardiopulmonary resuscitation on survival for out of hospital cardiac arrest: a causal inference analysis
Bibliographic record
Abstract
Percutaneous coronary intervention (PCI) improves survival in acute coronary syndromes and has been used in recent randomized trials of extracorporeal cardiopulmonary resuscitation (ECPR). However, the role of PCI during ECPR for out-of-hospital cardiac arrest (OHCA) remains uncertain. We analyzed adult patients with OHCA from the Extracorporeal Life Support Organization (ELSO) Registry from January 2020 to December 2022 who underwent ECPR at high-volume centers. Patients were stratified by PCI receipt. We applied propensity-score weighting to balance covariates predicting the probability of receipt of PCI including year, age, sex, race, quantitative burden of comorbidities, CPR duration prior to ECMO flow start, initial cardiac arrest rhythm, and center-level case volume. The primary outcome was survival to hospital discharge. We estimated adjusted odds ratios (aORs) using multivariable logistic regression and inverse probability weighting (IPW). Among 576 adult OHCA patients who received ECPR, 138 (24.3%) received PCI. PCI patients were more likely to arrest at home (59.4% vs. 46.1%; p=0.049) and have higher a greater initial incidence rates of ventricular fibrillation (VF) as the first detected rhythm (68.1% vs. 48.9%; p<0.001). Survival to hospital discharge was similar between groups (PCI: 18.1%, non-PCI: 20.1%). Adjusted causal inference analyses, including multivariable logistic regression (OR 0.99, 95% CI: 0.56–1.75, p = 0.98), inverse probability weighting (OR 1.03, 95% CI: 0.58–1.82, p = 0.93), and augmented IPW models (OR 1.06, 95% CI: 0.58–1.93, p = 0.85), showed no statistically significant association between PCI and survival to hospital discharge. PCI was not associated with improved survival in adult ECPR patients. These findings highlight the need for further prospective studies to clarify the role of PCI in ECPR and identify patient populations that may benefit from this intervention.
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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 | 0.075 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".