Board #111 - Research Abstract Improving Cardiopulmonary Resuscitation With a CPR Feedback Device and Refresher Simulations (CPR Cares Study)
Bibliographic record
Abstract
Hypothesis Each year, cardiopulmonary resuscitation (CPR) is provided for thousands of children in North America.1-2 Quality of CPR directly impacts hemodynamics, survival, and neurologic outcome following cardiac arrest.3-4 Unfortunately, well-trained healthcare providers fail to consistently perform CPR within established American Heart Association (AHA) guidelines.5-8 Just-in-Time (JIT) bedside CPR practice with audiovisual feedback improves compliance with AHA guidelines on manikins and real children during cardiopulmonary arrest (CPA).9-10 CPR feedback devices provide visual and/or auditory feedback during CPA, and have been shown to improve the quality of CC when used during training11 and during CPA events.12 We sought to determine whether JIT CPR Training with visual feedback (VisF) before CPA and/or real-time VisF during CPA improves quality of CPR during simulated CPA. Methods We conducted a prospective, randomized, 2x2 factorial trial with explicit methodology.13 Ten International Network for Simulation-based Pediatric Innovation, Research and Education (INSPIRE) simulation programs participated by running standardized 12-minute simulated CPAs from July 2012 to April 2014. We recruited 324 pediatric CPR certified healthcare providers, assigned to 3-person resuscitation teams (n =108 teams). Our two interventions were: a) “Just-in-Time” standardized, brief CPR training with video and VisF before simulated CPA; and b) Real-Time VisF during simulated CPA. Each team was randomized to one of four permutations of JIT-CPR VisF training versus no JIT-CPRVisF training before CPA, and real-time VisF versus no real-time VisF during simulated CPA. Our main outcome measures were: proportion of chest compressions (CC) with depth > 50mm, proportion of CPR time with CC rate 100-120/min, and CC fraction (CCF, % of CPR time) for each simulated CPA event. Results We used a 2x2 factorial analysis to assess the effects of JIT CPR training and/or VisF on the primary and secondary outcome measures. The quality of CPR was poor in the control group, with 12.7% (95% CI: 5.2, 20.1%) mean CC depth compliance and 27.1% (95% CI: 14.2, 40.1%) mean CC rate compliance. JIT-CPR VisF Training, compared to no JIT-CPR VisF training, improved CC depth compliance by 19.9% (95% CI: 11.1, 28.7%; p <0.001) and CC rate compliance by 12.0% (95% CI: 0.8, 23.2%; p=0.037). Real-time VisF, compared to no real-time VisF, improved CC depth compliance by 15.4% (95% CI: 6.6, 24.2%; p value = 0.001) and CC rate compliance by 40.1% (95% CI: 28.8, 51.3%; p< 0.001). Neither intervention had a statistically significant effect on CCF, which was excellent (>89%) in all groups. Combining both interventions showed the highest compliance with AHA guidelines, but was not significantly better than either intervention in isolation. Conclusion The quality of CPR provided by healthcare providers is poor. It is feasible to improve CPR quality using a simple visual CPR quality feedback device during JIT CPR training (with video) or during a simulated CPA event (real-time feedback). JIT CPR VisF training and/or “real-time” VisF during CPR can improve compliance with AHA guidelines for CPR quality that are associated with improved survival outcomes. References 1. Parra DA, Totapally BR, Zahn E, et al. Outcome of cardiopulmonary resuscitation in a pediatric cardiac arrest unit. Crit Care Med. 2000; 28:3296-300. 2. Slonim AD, Patel KM, Ruttimann UE, Pollack MM. Cardiopulmonary resuscitation in pediatric intensive care units. Crit Care Med. 1997; 25:1951-5. 3. Cheskes S, Schmicker RH, Christenson J, et al. Perishock pause: an independent predictor of survival from out-of-hospital shockable cardiac arrest. Circulation. 2011; 124:58-66. 4. Christenson J, Andrusiek D, Everson-Stewart S, et al. Chest compression fraction determines survival in patients with out-of-hospital ventricular fibrillation. Circulation. 2009; 120:1241-7. 5. Kaye W, Mancini ME. Retention of cardiopulmonary resuscitation skills by physicians, registered nurses, and the general public. Crit Care Med. 1986;14:620-622. 6. Broomfield R. A quasi-experimental research to investigate the retention of basic cardioKpulmonKary resuscitation skills and knowledge by qualified nurses following a course in professional development. J Adv Nurs. 1996; 23:1016-1023. 7. Abella B, Becker L, et al. Quality of cardiopulmonary resuscitation during in-hospital cardiac arrest. JAMA. 2005; 293:305-310. 8. Wik L, Kramer-Johansen, Myklebust H, et al. Quality of cardiopulmonary resuscitation during out-of-hospital cardiac arrest. JAMA. 2005; 293:305-310. 9. Niles D, Donoghue A, Kalsi MS et al. “Rolling Refreshers”: a novel approach to maintain CPR psychomotor skill competence. Resuscitation. 2009;80:909-12. 10. Sutton RM, Niles D, Meaney PA et al. “Booster” training: evaluation of instructor-led bedside cardiopulmonary resuscitation skill training and automated corrective feedback to improve cardiopulmonary resuscitation compliance of pediatric basic life support providers during simulated cardiac arrest. Pediatr Crit Care Med. 2011;12:e116-21. 11. Yeung J, Meeks R, Edelson D, Gao F, Soar J, Perkins GD. The use of CPR feedback/prompt devices during training and CPR performance: a systematic review. Resuscitation. 2009;80:743-51. 12. Kirkbright S, Finn J, Tohira H, Bremner A, Jacobs I, Celenza A. Audiovisual feedback device use by health care professionals during CPR: A systematic review and meta-analysis of randomized and non-randomised trials. Resuscitation. 2014; 85:460-71. 13. Cheng A, Auerbach M, Chang T, Hunt EA, Pusic M, Nadkarni V, Kessler D. Designing and Conducting Simulation-based Research. [Published online May 12, 2014]. Pediatrics. Doi: 10.1542/peds.2013-3267. Disclosures Adam Cheng receives grant support from the Heart and Stroke Foundation of Canada, and the Canadian Institute for Health Research. Vincent Grant serves on the Board of Directors and is a stockholder for PACEWellness. David Kessler is a consultant for SonoSim. Vinay Nadkarni receives grant support from Laerdal Foundation, Laerdal Corporation, Nihon-Kohden Corporation; Zoll Corporation, and the Zoll Foundation. Nancy Tofil spoke at a Laerdal Conference, presenting her own original material. She did not receive honoraria, but was reimbursed travel expenses.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".