Prehospital echocardiography in pulseless electrical activity victims using portable, handheld ultrasound
Bibliographic record
Abstract
Potentially treatable causes of sudden cardiac arrest, such as pericardial tamponade, myocardial insufficiency or hypovolemia, should be identified as soon as possible (that is, at the scene). Although these diagnoses are mainly made by echocardiography, old and new ERC or ILCOR guidelines only recommend pauses of ventilation or chest compressions as 'brief interruptions' at a maximum of 10 seconds, thereby potentially limiting transthoracic ultrasound examinations. We introduced an ALS-based algorithm of focused echocardiographic evaluation during resuscitation (FEER) to be performed in a time-sensitive manner. We tested both the capability of FEER to differentiate states of pulseless electrical activity, and its feasibility in the out-of-hospital setting using mobile, battery-powered ultrasound systems. Trained emergency physicians (EP) applied FEER to assessing basic ventricular function by 'eye-balling' in less than 10 seconds in prehospital cardiac arrest victims who were being resuscitated. True pulseless electrical activity (PEA) was defined according to the ERC as 'clinical absence of cardiac output despite electrical activity'. In contrast, any PEA was classified as a 'pseudo-PEA' when cardiac output was visualized by echocardiography. Seventy-eight CPR cases (age 66 ± 19 years) were included. On arrival of the EP on the scene, a true PEA was suspected in 31/78 cases. However, in 20/31 PEA cases cardiac wall movement was detected (pseudo-PEA) and correctable causes such as pericardial tamponade (four cases), poor ventricular function (14 cases) and hypovolemia (two cases) were treated. Fourteen out of 20 pseudo-PEA cases survived to hospital admission. In 11/30 PEA cases, no cardiac wall movement was visible (true PEA). All such patients died on the scene. FEER-based changes in therapy were induced in 25/31 cases. Application of FEER was feasible within a 10-second time-frame of CPR interruptions. While differentiating PEA states, FEER has the ability to identify a pseudo-PEA state, allowing further treatment of the underlying disorder on the scene to improve outcome.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".