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Enregistrement W4406282912 · doi:10.1093/ehjacc/zuaf006

EHJ ACVC where translational science meets care

2025· article· en· W4406282912 sur OpenAlexaff
Pascal Vranckx, David A. Morrow, Sean van Diepen, Frederik H. Verbrugge

Notice bibliographique

RevueEuropean Heart Journal Acute Cardiovascular Care · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueBiomedical Ethics and Regulation
Établissements canadiensCanadian VIGOUR CentreUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicineTranslational scienceTranslational researchIntensive care medicineFamily medicinePathology

Résumé

récupéré en direct d'OpenAlex

The February issue of the European Heart Journal Acute Cardiovascular Care highlights potentially groundbreaking translational research and clinical studies, showcasing innovations that may reshape acute cardiovascular medicine. From inflammation and diagnostic advancements to device innovation, this issue offers clinically impactful findings and expert commentary, affirming the journal’s leadership in bridging bench science and bedside care. Schupp et al.,1 in a pivotal sub-study of the ECLS-SHOCK trial,2,3 investigate the prognostic role of C-reactive protein in 371 patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS). Elevated C-reactive protein levels in the highest tertile (>61.0 mg/L) were independently associated with a 3.5-fold higher 30-day mortality risk compared to the lowest tertile [≤5.0 mg/L; adjusted odds ratio: 3.54; 95% confidence interval (CI), 1.88–6.68; P = 0.001]. Patients with higher C-reactive protein levels tended to be older and less likely to present with acute cardiac arrest, suggesting a connection between systemic inflammation and more advanced shock progression. Importantly, extracorporeal life support did not improve 30-day mortality regardless of C-reactive protein levels. Incorporating C-reactive protein into the IABP-SHOCK II risk score4 minimally improved the model’s discrimination (AUC: 0.74; 95% CI, 0.68–0.79), suggesting outcomes are principally driven by readily available clinical variables. François Roubille’s commentary contextualizes these findings, calling for targeted anti-inflammatory strategies in managing AMI-CS. Honda et al.5 bring further translational insight with the LASCAR-AHF trial, evaluating low-dose carperitide (recombinant α-human A-type natriuretic peptide) in acute heart failure (AHF). This multicentre randomized trial found no significant difference in the composite endpoint of all-cause mortality and heart failure hospitalizations between the carperitide group (29.5%) and standard treatment [28.0%; hazard ratio (HR): 1.26; 95% CI, 0.78–2.06; P = 0.827]. Secondary outcomes, such as dyspnoea relief and biomarker improvements, were also similar, while the carperitide group experienced greater renal function decline. The findings align with previous studies questioning the clinical utility of natriuretic peptides in AHF, prompting critical evaluation of their role in contemporary management strategies. Device innovation takes centre stage in a study by Ikeda et al.,6 utilizing observational data from the Japanese PVAD registry. High-flow percutaneous ventricular assist devices (PVADs; e.g. Impella 2.5 or CP) were associated with lower rates of complications, including haemolysis (HR: 0.38; 95% CI, 0.24–0.58) and kidney injury (HR: 0.32; 95% CI, 0.18–0.57), compared to low-flow devices (e.g. Impella 5.0 or 5.5), while also associated with lower all-cause mortality (HR: 0.79; 95% CI, 0.65–0.96). In this non-randomized analysis, high-flow PVADs appeared to deliver superior safety and efficacy, without increasing bleeding or sepsis risks. In the absence of a randomized trial, these hypothesis-generating findings point to the potential importance of tailoring device selection to patient profiles, marking a leap forward in optimizing mechanical support for CS. Innovation in diagnostics features prominently in a study by Moon et al.,7 who developed a deep-learning model integrating electrocardiogram data for AHF diagnosis in emergency settings. Analysing data from over 19 000 emergency care visits, the model achieved outstanding accuracy (AUC-ROC: 0.90 in external validation), outperforming traditional diagnostic methods. Including clinical biomarkers, such as troponin and creatinine, further enhanced its performance. This artificial intelligence (AI)–driven approach offers a promising tool for rapid AHF diagnosis, setting a new standard for precision medicine in emergency care. In a thought-provoking review, Van Aerde et al.8 address the shifting demographics of cardiac intensive care unit (CICU) patients, highlighting the increasing prevalence of elderly patients in CICUs, and the high proportion of patients with multiple comorbid conditions. Intensive care unit–acquired muscle weakness emerges as a major contributor to long-term morbidity and mortality after intensive care, emphasizing the need for standardized tracking of complications and holistic care strategies. This review underscores the importance of patient-centred models to improve long-term outcomes in CICU survivors. This issue’s Best in the Year 2024 series focuses on acute aortic diseases,9–11 offering a comprehensive review of our journal’s most impactful research in this critical area this year. The February edition of the European Heart Journal Acute Cardiovascular Care exemplifies the journal’s dedication to advancing translational research and clinical care. By spotlighting inflammation, leveraging AI-driven diagnostics, and optimizing device-based therapies, this issue equips healthcare professionals with actionable insights to improve patient outcomes. Dive into this essential issue and discover the future of acute cardiovascular medicine, where science meets care. Enjoy reading, the editors. None declared. No new data were generated or analysed in support of this research.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,906
Score d'incertitude au seuil0,654

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,305
Écart entre enseignants0,287 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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