Use of CPR feedback devices in resuscitation training: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
The use of cardiopulmonary resuscitation (CPR) feedback devices during training is increasing. This review evaluates whether incorporating CPR feedback devices in training improves patient survival, CPR quality in actual resuscitation, skill acquisition and retention after training. This systematic review was part of the continuous evidence evaluation process of the International Liaison Committee on Resuscitation (ILCOR). We searched MEDLINE, EMBASE, and SCOPUS databases from inception to September 30, 2024, including randomized controlled trials (RCTs) in all languages (with an English abstract) comparing CPR training with and without feedback devices. Outcome included patient survival, quality of clinical performance in resuscitation, and CPR skill acquisition and retention. Non-RCT studies, unpublished work without peer review or animal studies were excluded. Risk of bias was assessed using Cochrane tools, and certainty of evidence was graded using the Grading of Recommendations Assessment, development and Evaluation (GRADE) approach. Standardized mean difference (SMD) were calculated and pooled effects were analyzed using random-effects models. PROSPERO CRD42023488130. We identified 20 RCTs with 4579 participants. Risks of bias ranged from low to critical (low: 8, moderate: 9, and critical: 3). No studies evaluated the patient survival, clinical performance in resuscitation or cost-effectiveness. Compared to no feedback, using CPR feedback devices during training significantly improved key quality metrics. Pooled effect sizes were 0.76 (95%CI 0.02 – 1.50) for mean compression depth (15 studies), 0.98 (95%CI: 0.10 – 1.87) for depth compliance (16 studies), 0.29 (95%CI: 0.10 – 0.48) for mean rate (17 studies), 0.44 (95%CI: 0.23 – 0.66) for rate compliance (9 studies), and 0.53 (95%CI: 0.31 – 0.75) for recoil compliance (10 studies) in favour of using feedback devices during training. Heterogeneity was large (I 2 > 50%) in all analyses. Planned subgroup analyses revealed no statistically significant interaction between healthcare professionals and laypersons. Using the GRADE approach, the certainty of evidence was downgraded for certain outcomes due to critical risk of bias for 3 studies and inconsistency but upgraded for strong association. The use of CPR feedback devices during resuscitation training improves key quality metrics of CPR performance, with moderate to high certainty of evidence. However, further studies are needed to evaluate the impact on cost-effectiveness, clinical performance and patient outcomes.
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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 | 0.028 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.035 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".