Resuscitative transesophageal echocardiography
Bibliographic record
Abstract
Resuscitative transesophageal echocardiography (rTEE) has emerged as a transformative point-of-care imaging modality that integrates diagnostic and procedural guidance into real-time resuscitation. Unlike transthoracic echocardiography (TTE), rTEE provides continuous, high-resolution cardiac imaging without interrupting chest compressions, overcoming traditional limitations in patients with undifferentiated shock or cardiac arrest. This review summarizes the evolution, technical foundations, and clinical applications of rTEE across resuscitation, extracorporeal membrane oxygenation (ECMO), and peri-arrest care. We discuss the development of focused scanning protocols—such as the ACEP 3-view, Resuscitative TEE 4-view, and 3 + 2 frameworks—that enable rapid qualitative assessment of cardiac activity, ventricular function, volume status, and reversible causes of arrest. Diagnostic advantages include superior rhythm classification (distinguishing pulseless electrical activity (PEA) , pseudo-PEA, fine VF, and standstill), improved pulse-check accuracy, and identification of the area of maximal compression (AMC) to optimize CPR quality. Procedurally, rTEE supports real-time ECMO cannulation, monitoring, and decannulation, complementing ELSO recommendations for both V-A and V–V configurations. Evidence-based echocardiographic parameters—such as LVOT velocity time integral (VTI), MAPSE, TAPSE, and t-IVT—inform readiness for ECMO liberation and predict recovery or need for durable mechanical support. Focused rTEE training pathways and credentialing frameworks are now available for anesthesiologists, intensivists, and emergency physicians, expanding its accessibility in perioperative and critical care environments. As the technology becomes more widespread, future research should standardize rTEE competency assessment, validate outcome-based protocols, and further integrate rTEE into precision-guided resuscitation algorithms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".