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Enregistrement W4405496235 · doi:10.1093/ehjacc/zuae145

Acute cardiovascular care 2024 in review: acute heart failure

2024· article· en· W4405496235 sur OpenAlexaboutno aff
Elke Platz, Milica Aleksić, Frederik H. Verbrugge

Notice bibliographique

RevueEuropean Heart Journal Acute Cardiovascular Care · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueHeart Failure Treatment and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineHeart failureIntensive care medicineAcute careAcute decompensated heart failureCardiologyInternal medicineHealth care

Résumé

récupéré en direct d'OpenAlex

Heart failure (HF) is a complex syndrome that continues to be a major public health concern, associated with significant morbidity and mortality. The pathophysiologic understanding, diagnosis and treatment of HF remains a dynamic field. In chronic HF, findings from randomized clinical trials in 2024 demonstrated promising results of potential novel therapies for patients with obesity-related HF and patients with HF and mildly reduced (HFmrEF) or preserved ejection fraction (HFpEF).1–4 In this ‘year in review’ article, we will focus on acute HF (AHF)-related publications in the European Heart Journal–Acute Cardiovascular Care (EHJ–ACVC) and in the European Heart Journal family in the context of recent guidelines and expert consensus statements, highlighting selected clinical trials, and observational studies (Graphical Abstract). We selected frequently viewed papers and those that caught our attention. Publications related to cardiogenic shock will be covered in a separate editorial. Acute heart failure refers to the rapid or more gradual onset of symptoms and/or signs of HF, leading patients to seek urgent medical attention.5 AHF often results in an unplanned hospital admission or emergency department visit. However, the diagnosis of AHF can be challenging, especially in the presence of concomitant obesity, chronic kidney disease, and/or lung disease, such as chronic obstructive pulmonary disease. In a ‘Biomarker spotlight’ of the EHJ–ACVC authors from the Study Group on Biomarkers of the European Society of Cardiology (ESC) Association for Acute Cardiovascular Care highlighted the opportunities of clinical decision support using machine learning and natriuretic peptides for the improved diagnosis of AHF.6 In this article, the authors summarize the training, testing, and external validation of a clinical decision-support tool called Collaboration for the Diagnosis and Evaluation of Heart Failure (‘CoDE-HF’) which is based on data from 10 369 adult patients with suspected AHF from 13 countries.7 The CoDE-HF tool used a machine learning algorithm to combine N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations and clinical variables to calculate the probability of AHF for an individual patient. CoDE-HF had an excellent diagnostic performance and was more accurate than any approach using NT-proBNP thresholds alone. Prospective studies are now underway to evaluate the impact of implementing this decision-support tool on patient outcomes. Clinical decision-support tools will likely play an increasingly important role in the fast-paced emergency department setting where diagnostic accuracy is essential to enable tailored treatment, risk assessment, and appropriate disposition of more and more complex patients, many of whom are elderly.8 Non-invasive imaging plays an increasingly important role to facilitate the rapid diagnosis of AHF, including in patients with HFpEF.9 Lung ultrasound for the detection of pulmonary congestion is highlighted as a useful imaging method in the assessment of patients with suspected AHF in the 2021 ESC Heart Failure Guideline.5 In an effort to move towards greater standardization, a clinical consensus statement published by the European Association of Cardiovascular Imaging details the technique and its clinical utility in both patients with acute and chronic HF.10 Lung ultrasound may also be valuable in the detection of subclinical pulmonary congestion, indicating AHF, in patients with ST-elevation myocardial infarction (STEMI), as demonstrated in an observational study by Carreras-Mora et al.11 They found that among 373 patients with STEMI a score combining Killip class and lung ultrasound findings provided enhanced risk stratification compared with the Killip class and a previously described ‘LUCK’ score with respect to both inpatient mortality and 1 year major adverse cardiovascular events. In addition, early data from ultrasound imaging approaches to intravascular congestion assessment in patients with severe tricuspid regurgitation and AHF may offer additional insights into volume status in this subset of patients.12 The Association for ACVC of the ESC Acute Cardiovascular Care of the ESC published a clinical consensus statement on the diagnosis and treatment of right ventricular failure secondary to acutely increased right ventricular afterload (acute cor pulmonale).13 This consensus document provides a concise and practical overview of clinical, biomarker, and imaging tools in the assessment of patients with suspected acute cor pulmonale, including useful illustrations. Management strategies cover ventilatory support, medical treatment, and mechanical circulatory support strategies. In addition to updates to guideline-directed medical therapy (GDMT) for chronic HF, the 2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic HF also highlighted the importance of standardization of the management of patients with AHF, and in this context, the COACH trial.14 The COACH trial was a cross-sectional, stepped-wedge, cluster randomized trial including 5452 patients enrolled at 10 hospitals in Canada.15 The trial intervention consisted of a strategy to support clinicians in making decisions about admitting or discharging patients who presented to the emergency department with AHF. During the intervention phase, hospital staff used the Emergency Heart Failure Mortality Risk Grade 30-Day Mortality-ST Depression (‘EHMRG30-ST’) score to assess whether patients had a low, intermediate, or high risk of death within 7 days or within 30 days. The study protocol recommended that low-risk patients be discharged early (in ≤3 days) and managed with standardized outpatient care up to 30 days of follow-up, whereas it was recommended that intermediate- and high-risk patients be admitted to the hospital. Although early discharge occurred at a similar rate in the intervention and control groups (57 vs. 58%), the trial demonstrated a 12% reduction in the primary outcome of all-cause death or cardiovascular hospitalization in the interventional arm compared with the control arm (hazard ratio 0.88, 95% CI 0.78–0.99), consistent with a favourable effect of post-discharge care. Although no specific guideline recommendations were based on the results of this trial, strategies to standardize the assessment and risk stratification of patients with AHF may not only have beneficial impacts on AHF patients’ outcomes but also have the potential to mitigate emergency department crowding.16,17 Future research in this area will be critical to develop innovative approaches to rapid follow-up care for patients with AHF to facilitate decongestive therapy and GDMT titration in the outpatient setting even when rapid ‘traditional’ outpatient follow-up may not be feasible, such as virtual care pathways.18 The 2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic HF further highlighted the need and opportunities for novel, standardized approaches to decongestion in patients with AHF.14 Such approaches were tested in the Efficacy of a Standardized Diuretic Protocol in Acute Heart Failure (ENACT-HF) study, an international, multicentre, open-label, non-randomized trial, comparing the current standard of care of each centre with a standardized diuretic protocol, including urinary sodium to guide therapy in a two-phase, sequential design among 401 patients with AHF.19 A standardized natriuresis-guided diuretic protocol to guide decongestion in AHF was feasible, and resulted in higher natriuresis and diuresis, as well as a shorter length of stay. In-hospital mortality was low and did not significantly differ between the two arms. The Pragmatic Urinary Sodium-based treatment Algorithm in Acute Heart Failure (PUSH-AHF) trial assessed whether natriuresis-guided diuretic therapy in patients with AHF improves natriuresis and clinical outcomes, in a pragmatic, open-label trial among patients with AHF.20 Patients with AHF (n = 310) requiring treatment with intravenous loop diuretics were randomly assigned to natriuresis-guided therapy or standard of care. Natriuresis in the natriuresis-guided arm was significantly greater than in the standard of care arm (409 ± 178 vs. 345 ± 202 mmol, P = 0.006). However, there was no significant difference between the two arms for the combined endpoint of time to all-cause mortality or first HF rehospitalization [hazard ratio 0.92 (95% CI 0.62–1.38), P = 0.70]. A pre-specified analysis from this trial demonstrated that natriuresis-guided diuretic treatment improved diuresis and natriuresis irrespective of baseline estimated glomerular filtration rate (eGFR) and occurrence of worsening renal function, was effective in patients with low eGFR, and the observed effect on eGFR was transient and not associated with worse clinical outcomes.21 Larger, multicentre trials could help us understand the implications of different diuretic strategies, including combination therapies, for patient outcomes and inform future care strategies for patients with AHF and congestion. Several studies have demonstrated elevated levels of inflammatory biomarkers in patients with AHF, and that these were associated with worse outcomes during follow-up.22 Whether anti-inflammatory therapy improves outcomes in patients with AHF is unclear. Two small clinical trials investigated the efficacy and safety of steroids and colchicine in this population. The CORTAHF trial was a randomized, open-label trial among 101 patients with AHF and both elevated NT-proBNP and high-sensitivity C-reactive protein (hsCRP) levels to 40 mg of prednisone for 7 days or usual care.23 Steroid therapy was associated with reduced inflammation as measured by hsCRP levels at Day 7 (primary endpoint). Secondary endpoints demonstrated improved quality of life at Day 7 and reduced 90 day risk of death or worsening HF (hazard ratio 0.31, 95% CI 0.11–0.86, P = 0.016). This study was limited by the lack of blinding of both patients and investigators which may have introduced bias, although the findings are nevertheless encouraging. The COLCICA trial was a multicentre, randomized, double-blind, placebo-controlled trial that randomized 278 patients with AHF and elevated NT-proBNP requiring at least one dose of IV furosemide to colchicine or placebo for 8 weeks. The primary endpoint, the time-averaged reduction in NT-proBNP levels at 8 weeks, did not differ significantly between the colchicine group and the placebo group. The reduction in inflammatory markers was significantly greater with colchicine: ratio of change 0.60 (P < 0.001) for C-reactive protein and 0.72 (P = 0.019) for interleukin-6. No differences were found in new worsening HF episodes (14.9% with colchicine vs. 16.8% with placebo, P = 0.70). This trial did not limit enrolment to patients with elevated inflammatory markers at baseline which may have impacted the results. Further well-designed and sufficiently powered trials are needed to evaluate the effect of anti-inflammatory therapies on clinical stability and patient outcomes. Although great strides have been made in the diagnosis and treatment of chronic HF over the past decades, there is an ongoing need for improved early diagnosis and timely, tailored treatment of AHF, irrespective of the setting in which these episodes are managed. We are looking forward to more innovative research in 2025 and encourage you to send your best papers to the EHJ–ACVC. None. There are no new data associated with this article.

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)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,440
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,009
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,002

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 Opus0,017
Tête enseignante GPT0,277
Écart entre enseignants0,260 · 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.

Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

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

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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