Abstract 17760: Maintaining High Quality CPR With an Integrated Manual/Mechanical Resuscitation Protocol
Bibliographic record
Abstract
Background: High quality manual chest compressions (CC) can be achieved on scene during resuscitation of cardiac arrest patients, but manual CC quality can deteriorate during patient extrication and transport. The purpose of this study was to describe the effect on CC quality of an integrated manual/mechanical chest compression protocol developed to maintain CC quality and patient/provider safety throughout resuscitation. Methods: CC quality was monitored using a monitor with accelerometer-based CC sensing (E Series/X Series, ZOLL Medical) during the treatment of consecutive out-of-hospital cardiac arrest patients between 3/1/2013-4/30/2015. The EMS agency performed manual CC guided by real-time audiovisual feedback on scene but deployed the AutoPulse load-distributing band CC device (LDB, ZOLL Medical) in a choreographed manner for extrication and transport. The LDB was also placed prophylactically on patients after ROSC. Descriptive statistics are reported as median (IQR). Results: A total of 71 OHCA patients were treated (median age 58 yrs, 66 % male) of which 39 received only manual CC and 32 received both manual and LDB CCs (22 with LDB deployed during ongoing manual CC and 10 with LDB placed after ROSC). With real-time CC feedback, high quality CCs were performed [depth 2.38 in (2.17-2.64), rate 100.3 cpm (99.2-102.5), CC fraction 87.0% (83.8-89.5)]. For patients requiring transport, the LDB was started after 14.2 min (11.6-19.2) of manual CC and was placed with minimal interruptions- total of 27.5 sec (23-42) pause time in 2 minutes prior to LDB deployment. During transport, CC fraction remained high (91.7%; 89.3-95.4) with use of LDB. Seven of 10 patients prophylactically placed on the LDB rearrested, 3 rearrested during transport. Conclusion: A choreographed and rehearsed integrated chest compression protocol featuring both manual and mechanical compressions, allows for high quality resuscitation with minimal compression interruptions. Placement of a mechanical device on patients after ROSC may be beneficial as over two-thirds of patients in this study rearrested, one-third of which occurred during transport when delivery of manual compressions is difficult and potentially dangerous.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".