Haemodynamic monitoring and management of the hypotensive out-of-hospital cardiac arrest patient in the adult intensive care unit: a clinical consensus statement of the Association for Acute CardioVascular Care of the ESC
Bibliographic record
Abstract
AIMS: Out-of-hospital cardiac arrest (OHCA) represents a major public health challenge, with high mortality and significant neurological impairments among survivors. Haemodynamic instability, particularly hypotension (a mean arterial blood pressure <65 mmHg), may be a key contributor to post-resuscitation morbidity and mortality. METHODS AND RESULTS: After return of spontaneous circulation, hypotension can result from various causes, including arrhythmias, mechanical complications, thromboembolism, or different types of shock, as well as sedation, temperature control and positive pressure ventilation. Differentiating between hypotension with vs. without hypoperfusion is critical to avoid unnecessary interventions while ensuring adequate cerebral and myocardial perfusion. Clinical assessment and repeated echocardiography should be routine in all patients. Therapeutic targets should include evidence of preserved end-organ function, including urine output, and normal or decreasing lactate. In selected cases, advanced haemodynamic monitoring with pulmonary artery catheters may be necessary to diagnose the shock-type and monitor treatment effects. Causal treatment of the precipitating cause of hypotension is crucial as well as symptomatic treatment with fluids, vasopressors and inotropes if needed. Mechanical circulatory support may be employed for refractory shock unresponsive to other treatment. CONCLUSION: This clinical consensus statement by the Association for Acute CardioVascular Care (ACVC) of the European Society of Cardiology (ESC) provides clinical guidance for the haemodynamic monitoring and management of hypotension in OHCA patients in intensive care. The document advocates for a multidisciplinary approach that integrates clinical assessment, imaging, and haemodynamic parameters to guide treatment, with the overarching goal of improving survival rates and neurological 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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| 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".