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Enregistrement W4200601702 · doi:10.1002/ejhf.1896

December 2021 at a glance: focus on medical treatment, valvular heart disease and prognostic models

2021· article· en· W4200601702 sur OpenAlexaff
Daniela Tomasoni, Marianna Adamo, Marco Metra

Notice bibliographique

RevueEuropean Journal of Heart Failure · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueHeart Failure Treatment and Management
Établissements canadiensSurgical Specialties (Canada)
Organismes subventionnairesnon disponible
Mots-clésMedicineSacubitrilEjection fractionHeart failureValsartanInternal medicineEnalaprilClinical endpointCardiologyHeart failure with preserved ejection fractionRandomized controlled trialClinical trialBlood pressureAngiotensin-converting enzyme

Résumé

récupéré en direct d'OpenAlex

Guideline-directed medical therapy (GDMT) represents a powerful tool to prevent and reduce cardiovascular mortality and hospitalizations in patients with heart failure (HF) with reduced ejection fraction (HFrEF).1 In clinical practice, many factors may lead to GDMT underutilization and only a minority of patients receive target doses used in the landmark trials.2-5 Seferovic et al.6 proposed new strategies for the implementation of GDMT. Professional education, motivation, and training, as well as patient empowerment for self-care, modern technologies, multidisciplinary team management, novel drugs and better patient profiling are possible solutions to implement GDMT. ACTIVITY-HF is a randomized controlled trial that enrolled 201 patients with HFrEF and aimed at comparing the effects of sacubitril/valsartan versus enalapril on exercise capacity. The primary endpoint of change from baseline to 12 weeks in peak oxygen consumption did not differ between groups.7 Results are consistent with previous findings form the OUTSTEP-HF trial.8 Sacubitril/valsartan was compared to valsartan alone in patients with HF with preserved ejection fraction (HFpEF) in the Prospective Comparison of ARNI with ARB Global Outcomes in HF with Preserved Ejection Fraction (PARAGON-HF) trial.9-11 Suzuki et al.12 showed that non-completion of the run-in period, an event occurring in 16.1% of the 4822 randomized patients, was associated with multiple factors, including lower systolic blood pressure, lower serum sodium and haemoglobin, worse renal function, higher N-terminal pro-B-type natriuretic peptide, higher New York Heart Association functional class, prior HF hospitalization, and lack of prior use of renin–angiotensin system inhibitors or beta-blocker. Most of these factors seem to be related to a more advanced disease. Heart failure with preserved ejection fraction is a heterogeneous syndrome with multiple aetiologies and phenotypes.13-15 Kammerlander et al.16 investigated the prevalence of HFpEF following left-sided valve repair. Out of 973 patients included, 673 underwent surgery and 300, with HFpEF, did not (control group). After surgery, 67.4% of patients fulfilled the criteria for the diagnosis of HFpEF, 20.6% were without HF and 12% developed HF with either mid-range or reduced ejection fraction. Of note, only a minority of patients were correctly diagnosed with HFpEF by cardiologists at follow-up. Patients who developed HF, irrespective of ejection fraction, had a higher risk of death, compared to those without HF, and a similar risk compared to the HFpEF control group. The role of biomarkers for the prognostic stratification of HF patients is well established.17, 18 In a cohort of 708 patients with aortic stenosis, several biomarkers were associated with the endpoints of death and death or HF hospitalizations. Using a machine-learning method, interleukin-6 (a marker of inflammation) and fibroblast growth factor-23 (a marker of calcification) resulted the most strongly associated with adverse outcomes.19 Many risk prediction models have been developed.20, 21 Codina et al.22 proposed a head-to-head comparison of different prediction models, including Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, Seattle Heart Failure Model (SHFM), PARADIGM Risk of Events and Death in the Contemporary Treatment of Heart Failure (PREDICT-HF) and Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator. A total of 1166 patients were included. The four scores had similar performance. However, correlation between them was relatively poor. Furthermore, SHFM and PREDICT-HF underestimated, whereas BCN-Bio-HF, even if it showed the best accuracy, overestimated the risk of events. The outcome of patients with acute myocarditis and life-threatening ventricular arrhythmias is still unsettled. Gentile et al.23 retrospectively studied the incidence and the predictors of recurrent major arrhythmic events (MAEs), defined as sudden cardiac death or successfully defibrillated ventricular fibrillation, or sustained ventricular tachycardia (sVT) after discharge. Out of 156 patients, 58 (37.2%) experienced MAEs after discharge. MAEs with sVT at presentation, late gadolinium enhancement involving ≥2 myocardial segments and absence of positive short-tau inversion recovery at first cardiac magnetic resonance were identified as valuable tools for risk stratification. Peripartum cardiomyopathy (PPCM) usually occurs during pregnancy or soon after delivery, in the absence of other causes of HF.24, 25 The extent of hypertension in women with PPCM has been investigated by Jackson et al.26 using data from the European Society of Cardiology EURObservational Research Programme PPCM Registry. Maternal and neonatal outcomes were analysed in three phenotypes of women: PPCM with no hypertension (PPCM-noHTN), hypertension with no pre-eclampsia (PPCM-HTN) and PPCM with pre-eclampsia (PPCM-PE). Women with PPCM-PE presented with more severe symptoms and signs of HF than those with PPCM-noHTN, despite having better baseline left ventricular ejection fraction and a greater likelihood of left ventricular recovery. Differences were also found in neonatal outcomes.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,013
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,191
Score d'incertitude au seuil0,638

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,013
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,002
Communication savante0,0070,005
Science ouverte0,0010,003
Intégrité de la recherche0,0060,009
Charge utile insuffisante (le modèle a refusé de juger)0,1910,069

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 Opus0,020
Tête enseignante GPT0,265
Écart entre enseignants0,246 · 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 source (Gemma direct ou Codex distillé), 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
GenreÉditorial

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
Publié2021
Routes d'admission1
Résumé présentoui

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Même revueEuropean Journal of Heart FailureMême sujetHeart Failure Treatment and ManagementTravaux en français237 207