Developing and Testing a Protocol for Managing Cardiopulmonary Resuscitation of Patients with Suspected or Confirmed COVID-19: In Situ Simulation Study
Bibliographic record
Abstract
BACKGROUND: Resuscitating patients with suspected or confirmed COVID-19 imposes unique challenges to organizations and code blue teams. Studies that applied the American Heart Association (AHA) COVID-19-related Interim Resuscitation Guideline and similar European guidelines are scarce. OBJECTIVE: This study aimed to develop and test a cardiopulmonary resuscitation protocol based on the AHA COVID-19-related Interim Resuscitation Guideline. METHODS: The study was conducted as an in situ simulation in a medical intensive care unit. The COVID-19 cardiopulmonary resuscitation protocol was created and validated by 11 health care team members and tested using 4 simulation sessions where 46 code blue team members participated. During the simulation, we observed role clarity, the effectiveness of communication, team dynamics, infection control measures, and the availability of essential supplies and equipment. RESULTS: The main issues identified in each simulation session were debriefed to the code blue teams and used to further revise the protocol. These include the assignment of tasks, availability of equipment and supplies, and failure of communication between the in-room and out-of-room teams. Solutions included changes in the placement of team members and roles and responsibilities; the creation of an isolation code medication package, a respiratory therapy kit, and an isolation code blue bag; and the use of two-way radios and N-95 masks with eye goggles to enhance communication between the teams. CONCLUSIONS: This study shed light on the challenges to implement the AHA COVID-19-related Interim Resuscitation Guideline. The in situ simulation was an effective approach for rapid training, identifying unreliable equipment and ineffective and inefficient workflow, and managing the complexity of the physical environment.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".