Characteristics of pediatric non-cardiac extracorporeal cardiopulmonary resuscitation (ECPR) programs in north american hospitals: A cross-sectional survey
Bibliographic record
Abstract
Background: Extracorporeal cardiopulmonary resuscitation (eCPR) has been applied in the pediatric population, yet there are no standardized inclusion/exclusion criteria, modalities of eCPR, or staffing models. Methods: Cross-sectional survey of hospitals with formalized non-cardiac eCPR programs. Variables included hospital and surgical group demographics, patient characteristics, eCPR inclusion/exclusion criteria, cannulation approaches and mortality. Results: Surveys were completed by 35/49 hospitals in the United States (32) and Canada (3) that have formalized non-cardiac eCPR programs (71% response rate). Respondents tended to work in >200 bed free-standing children's hospitals (22/35, 63%). Pediatric general surgeons perform eCPR in 27/35 (77%) with a median group size of 6.5 surgeons (IQR 5,9);8/35 (23%) of respondents take in-house call and 68% have a formal backup system for eCPR. Dedicated simulation programs were reported by 20/35 (57%) of respondents. Annual eCPR activations average approximately 6/year;approximately 41% of patients survived to decannulation, with 36% surviving to discharge. Cannulations occurred in a variety of settings [ED (in 40% of institutions), PICU (97%), NICU (66%), OR (69%);as well as CICU, Cardiac Catheterization Lab, IR], and were mostly done through a cervical approach. eCPR exclusion criteria included pre-hospital arrest (20/35, 57%), prolonged CPR [variably reported as >30 to >60 minutes] (15/35, 43%), lethal chromosomal anomalies (14/35, 40%), terminal underlying disease (14/35, 40%) and COVID+ status (5/35, 14%). Conclusions: eCPR requires substantial resources and is associated with modest survival outcomes. Codification of indications and surgical approaches may help clarify the utility and success of eCPR in select situations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".