Development of a health equity tool in resuscitation sciences and application to current research in extracorporeal cardiopulmonary resuscitation for cardiac arrest
Bibliographic record
Abstract
BACKGROUND: Extracorporeal cardiopulmonary resuscitation (ECPR) is increasingly used for adults with cardiac arrest (CA) refractory to Advanced Cardiovascular Life Support (ACLS). Concerns exist that adding ECPR could worsen health inequities, defined as differences in health outcomes that are unfair or unjust. Current guidelines do not explicitly address this issue. This study narratively reviews the latest evidence on ECPR, focusing on its implications for health equity and derives a health equity tool that may serve as a basis of comparison for resuscitation sciences. METHODS: We searched the American Heart Association (AHA) and International Liaison Committee on Resuscitation (ILCOR) websites for the latest ACLS guidelines and scientific summaries on ECPR for CA and identified randomized controlled trials (RCTs) and observational studies. We identified population and individual characteristics associated with inequities based on the literature and expert opinion. These characteristics were used as a health equity tool to assess: differences in baseline risk, population exclusion and trial representation in studies, outcome analyses, and implementation barriers. We used the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) Evidence to Decision (EtD) framework to evaluate ECPR's impact on health equity. RESULTS: Four RCTs involving 435 patients were conducted in the (2/4) USA, (1/4) Czech Republic, and (1/4) Netherlands. We identified thirteen characteristics associated with health inequities. All trials took place in urban, high-resourced hospitals and excluded older adults (60-75+ years). Across all RCTs, women were under-represented, and in the two USA-based trials, Black individuals were under-represented. There was no difference in baseline rate of survival with minimal or no neurologic impairment between sexes, but an observed trend favoring younger patients (<65). One trial's subgroup analysis showed no significant differences in ECPR effectiveness by sex or age. We noted that implementing ECPR for out-of-hospital CA faces challenges due to demographic variability, differences in emergency services, access to existing ECPR programs, and limited implementation outside urban areas. CONCLUSIONS: A health equity tool based on axes of health inequities for resuscitation identified that health equity is reduced with the use of ECPR for CA. Mitigation strategies should involve evaluating demographics, health equity measures, outcomes and ensuring equitable access to ECPR across catchment areas before and after 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 | 0.383 | 0.587 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.042 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".