Cardiac ultrasound in cardiovascular emergency and critical care: a clinical consensus statement of the European Association of Cardiovascular Imaging, the Acute CardioVascular Care Association of the European Society of Cardiology, and the European Association of Cardiothoracic Anaesthesia and Intensive Care
Bibliographic record
Abstract
Cardiac ultrasound (CUS) has emerged as an indispensable bedside diagnostic and monitoring tool in cardiovascular emergencies and critical care, enabling rapid, noninvasive assessment of cardiac structure and function. This multidisciplinary consensus statement, jointly developed by the EACVI, ACVC, and EACTAIC, provides comprehensive, practical guidance on the use of CUS in acute care settings. The document outlines a pathophysiological framework for applying CUS across a wide spectrum of clinical scenarios, including cardiogenic shock, acute myocardial infarction, mechanical complications, acute heart failure, severe valvular disease, pericardial tamponade, pulmonary embolism, and cardiac arrest. It describes key ultrasound modalities, measurement techniques, and diagnostic considerations essential for accurate interpretation and decision-making. Emphasis is placed on integrating CUS with multimodal imaging and organ-specific ultrasound to improve diagnostic accuracy and risk stratification. The consensus further defines recommended training pathways, competence levels, and governance standards to ensure high-quality practice and mitigate medicolegal risks. Looking ahead, the document highlights future perspectives, including the transformative potential of artificial intelligence, big data, and connected technologies to enhance CUS capabilities. By standardising approaches and promoting interdisciplinary collaboration, this statement aims to optimise patient outcomes and advance the role of cardiac ultrasound as a cornerstone of emergency and critical cardiovascular care.
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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 | 0.037 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".