Looking back, moving forward: a bold start to 2025
Bibliographic record
Abstract
As we usher in 2025, the European Heart Journal: Acute Cardiovascular Care delivers a January issue that exemplifies the transformative spirit of innovation in our field. This first issue of the year sets the stage for progress by spotlighting pivotal research spotlighted the journal in 2024 and forward-thinking ideas that promise to shape the future of acute cardiovascular and intensive care. Among the standout studies featured this month is the GRECO trial by van Gils et al.,1 which explored the potential of acyl-ghrelin to improve cognitive and psychosocial outcomes following out-of-hospital cardiac arrest (OHCA). This meticulously conducted Phase 2 trial enrolled 160 comatose patients randomized to receive acyl-ghrelin or placebo within 12 h post-arrest. While the intervention did not yield statistically significant improvements in cognitive performance, it demonstrated a meaningful reduction in depressive symptoms, achieving a clinically significant difference on the Hospital Anxiety and Depression scale. These findings, though preliminary, highlight the neuroprotective potential of acyl-ghrelin and call for further investigation into its broader role in post-cardiac arrest care. Grand et al.2 provide a secondary Bayesian analysis of the BOX trial, revisiting blood pressure targets for comatose OHCA patients. Their findings, derived from sophisticated statistical models, emphasize the uncertainty surrounding higher mean arterial pressure (MAP) targets, revealing a 33% probability of benefit but a 44% probability of harm for 1 year mortality outcomes. Intriguingly, the analysis identified age-related differences, with younger patients showing increased mortality risks at higher MAP levels. These results underscore the complexity of post-resuscitation care and the need for personalized approaches tailored to individual patient profiles. In another groundbreaking study, Andrei et al.3 critically examined the Venous Excess Ultrasound Grading System (VExUS), a tool designed to evaluate systemic venous congestion in the intensive care unit (ICU). Analysing over 500 haemodynamic evaluations, the study found that higher VExUS grades correlated predominantly with cardiac dysfunction parameters, such as left ventricular E/A ratio and right ventricular S-wave velocity. However, the tool demonstrated limited utility in non-cardiac conditions like sepsis, emphasizing the need for complementary methods to assess congestion in diverse ICU populations. This nuanced analysis offers a roadmap for refining diagnostic strategies and enhancing the management of systemic venous congestion. Kunkel et al.4 provide an illuminating study on sex-specific differences in neurohormonal activation following ST-elevation myocardial infarction (STEMI). Their multicentre investigation revealed that women exhibited significantly higher levels of biomarkers, such as pro-atrial natriuretic peptide and mid-regional pro-adrenomedullin, compared with men, alongside longer treatment delays and higher mortality rates. The findings underscore the prognostic importance of these biomarkers in women, suggesting that distinct physiological and treatment factors contribute to sex disparities in STEMI outcomes. This research serves as a clarion call for tailored approaches to addressing these disparities, ensuring equitable care for all patients. Complementing these original investigations is our signature ‘Best in the Year’ series, which this month focuses on acute cardiac care and heart failure. This curated overview synthesizes the most influential trials and publications from 2024, offering readers a comprehensive snapshot of the year’s progress. By bringing together key findings and expert perspectives, this feature aims to inspire innovation and guide future research efforts. This issue reflects the journal’s enduring commitment to advancing science and improving patient outcomes. By embracing cutting-edge research and addressing the complexities of acute cardiovascular care, it invites readers to be part of a dynamic and transformative journey. Let this January issue serves as a clarion call for innovation, inspiring all of us to tackle the challenges ahead with curiosity, collaboration, and an unwavering dedication to excellence. Together, we can redefine the future of acute cardiovascular medicine. Enjoy reading! The editors team. No funding. No new data were generated or analysed in support of this research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".