Abstract P192: What is the Role of Chest Compression Depth during Out-of-Hospital CPR?
Bibliographic record
Abstract
Introduction: Current CPR compression depth guidelines were empirically derived. We sought to study patterns of CPR compression depth and their associations with patient outcomes in out-of-hospital cardiac arrest (OOHCA). Methods: We studied OOHCA patients from the Resuscitation Outcomes Consortium Epistry - Cardiac Arrest for whom electronic CPR data were available from proprietary accelerometer technology attached to the prehospital defibrillators. We calculated anterior chest wall depression in millimeters and the period of active CPR (chest compression fraction [CCF]) for each minute of CPR. We controlled for covariates including compression rate and calculated adjusted odds ratios (OR) for any return of circulation (ROSC) and 24 hour survival. We calculated unadjusted OR for hospital discharge. Results: We included 615 adult patients from 6 U.S. and Canadian cities with these characteristics: mean age 68.9; male 62 %; witnessed 43%; bystander CPR 31%; initial rhythms - VF/VT 25%, PEA 17%, asystole 43%, unspecified non-shockable 15%; mean compression rate 103/min; mean compression depth <38mm 51%, 38–51mm 39%, >51mm 11%; median CCF 0.66; outcomes - ROSC 57%, 24 hour survival 18%, discharge 5%. We found an inverse association between depth and compression rate (P<0.0001; see Table ), no association between depth and CCF (P=0.30), and a positive association between CCF and rate (P<0.0001). ORs with 95% CIs for each 5mm increment in compression depth and the outcomes were: 1) ROSC - adjusted OR 1.06 (0.97–1.16), 2) 24 hour survival - adjusted OR 1.01 (0.98–1.03), 3) discharge - unadjusted OR 1.13 (0.95–1.34). Conclusions: We found half of patients received suboptimal compression depth, an inverse association between compression depth and rate, and no clear association between survival and increased compression depth. Further study is required to determine the optimum chest compression depth and its interaction with rate and CCF in order to maximize outcomes. Compression Rate versus Compression Depth
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".