EACTS Expert Consensus Document on protected cardiac surgery: pre-emptive use of temporary mechanical circulatory support in adult cardiac surgery patients at high risk for perioperative low cardiac output syndrome
Bibliographic record
Abstract
Perioperative low cardiac output syndrome (LCOS) remains a significant complication in adult cardiac surgery, contributing to substantial morbidity, prolonged intensive care, and increased mortality. Its incidence is expected to rise further due to the evolving complexity of referred surgical patients, often characterised by advanced age, multiple comorbidities, challenging anatomy, and impaired haemodynamics. Despite advances in pharmacological and perioperative care, outcomes for high-risk patients have not shown significant improvement, prompting interest in temporary mechanical circulatory support (tMCS) as a proactive strategy. This Expert Consensus Document from the European Association for Cardio-Thoracic Surgery (EACTS) presents the first dedicated guidance on the pre-emptive use of tMCS in high-risk adult cardiac surgical patients. Developed by a multidisciplinary task force, it emphasises structured risk stratification, early initiation, and individualised device management informed by interdisciplinary Heart Team discussions. The document proposes clinical pathways for patient selection, defines criteria for tMCS initiation, and provides practical algorithms for various scenarios, including advanced heart failure, cardiogenic shock, and post-cardiotomy LCOS. It reviews the current evidence on available tMCS devices, such as intra-aortic balloon pumps, microaxial flow pumps, veno-arterial extracorporeal life support and hybrid strategies, and addresses perioperative care, intensive care unit protocols, ethical considerations, as well as informed consent and support withdrawal. Despite promising results, substantial knowledge gaps remain, including long-term outcome data, device selection criteria, and cost-effectiveness analyses. This consensus aims to support clinical decision-making, standardise practice, and stimulate research to improve outcomes in a growing population of high-risk surgical patients.
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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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".