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Enregistrement W2910990228 · doi:10.1002/ejhf.1254

January 2019 at a Glance: Prognostic Assessment, Left Ventricular Assist Devices, Disease Management and Quality of Care

2019· editorial· en· W2910990228 sur OpenAlexaff
Marco Metra

Notice bibliographique

RevueEuropean Journal of Heart Failure · 2019
Typeeditorial
Langueen
DomaineMedicine
ThématiqueHeart Rate Variability and Autonomic Control
Établissements canadiensSurgical Specialties (Canada)
Organismes subventionnairesnon disponible
Mots-clésMedicineEjection fractionInternal medicineHeart failureCardiologyDiabetes mellitusCoronary artery diseaseAtrial fibrillationKidney diseaseProspective cohort studyEndocrinology

Résumé

récupéré en direct d'OpenAlex

Tromp et al.1 prospectively studied the clinical characteristics, echocardiographic parameters and outcomes in 1204 patients with heart failure (HF) and preserved ejection fraction from different areas from Asia. Seventy per cent of patients had ≥ 2 co-morbidities, including hypertension (71%), anaemia (57%), chronic kidney disease (50%), diabetes (45%), coronary artery disease (29%), atrial fibrillation (29%) and obesity (26%). Southeast Asian patients had the highest prevalence of all co-morbidities and were at higher risk for adverse outcomes, independent of co-morbidity burden and cardiac geometry.1 N-terminal pro B-type natriuretic peptide (NT-proBNP) and troponin T (TnT) are the two main biomarkers for prognostic assessment of patients with HF and reduced ejection fraction (HFrEF). They were the only biomarkers having an independent prognostic value in some retrospective analyses.2 Diabetes is a major determinant of outcomes in patients with HF.3 Rørth et al.4 assessed the prognostic value of NT-proBNP and TnT in HFrEF patients with and without diabetes enrolled in PARADIGM-HF (Prospective Comparison of ARNI With ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure). NT-proBNP plasma levels did not differ between patients with and without diabetes. In contrast, TnT levels were higher in diabetic, compared with non-diabetic, patients. Both biomarkers had an independent prognostic value in either diabetic or non-diabetic patients.4 Abnormalities in the autonomic nervous system play a major role in the progression of HF.5 Paleczny et al.6 assessed the prognostic value of cardiac baroreflex sensitivity (BRS) in contemporary, optimally treated patients with HFrEF. BRS, assessed using three different methods, was not related to survival, irrespective of the method used. BRS, assessed by the phenilephrine method, correlated with several clinically important variables, including left ventricular ejection fraction.6 Patient-reported outcomes have gained major interest also as endpoints of clinical trials.7, 8 Luo et al.9 analysed the relation between the change in the Kansas City Cardiomyopathy Questionnaire (KCCQ), from baseline to 3 months, in 2038 patients undergoing exercise training in ACTION-HF (A Controlled Trial Investigating Outcomes of Exercise Training). Worsening health status was associated with increased all-cause mortality/hospitalization. An improvement in health status, up to an 8-point increase in KCCQ, was associated with decreased all-cause mortality/hospitalization. Additional improvements in health status beyond an 8-point increase in KCCQ were not associated with different outcomes.9 Multiple algorithms were elaborated to predict the effects of cardiac resynchronization therapy (CRT).10 Cikes et al.11 tested the hypothesis that a machine learning algorithm utilizing both complex echocardiographic data and clinical parameters could be used to predict the response to CRT in HFrEF patients. This algorithm was applied to 1106 HF patients from MADIT-CRT (Multicenter Automatic Defibrillator Implantation Trial with CRT). Patients were categorized into four mutually exclusive phenogroups based on similarities in clinical parameters, and left ventricular volume and deformation traces at baseline. Two of these phenogroups were associated with a substantially better treatment effect of CRT with defibrillation on the primary outcome.11 Left ventricular assist device (LVAD) implantation has a major role for the treatment of patients with advanced HF.12, 13 Schmitto et al.14 report the 2-year outcomes of 50 adults implanted with the HeartMate 3 LVAD and enrolled in the CE Mark Study.14 At 2 years, Kaplan–Meier survival was 74 ± 6%, 5 patients (10%) were transplanted, and 32 patients (64%) remained with support. Adverse event rates included bleeding requiring surgery (16%), gastrointestinal bleeding (20%), driveline infection (24%), ischaemic stroke (16%), haemorrhagic stroke (8%), right HF (14%), and outflow graft thrombosis (2%). Notably, no haemolysis, pump thrombosis, or pump malfunction events occurred. At 2 years, 47% of patients remained in New York Heart Association (NYHA) class I and 41% in NYHA class II.14 Medical treatment still has a major role also in patients with advanced HF and LVADs.13, 15 Exercise training has a major role, too. A position statement by the HF Association reviews current knowledge and gives practical recommendations about this topic.16 HF clinics with specialist-trained nurses remain scarce in primary care (PC) in Sweden. Liljeroos and Strömberg17 describe the results of the implementation of PC HF clinics in Sweden. Their introduction was associated with a reduced number of HF hospitalizations and HF emergency room visits as well as with an increased proportion of patients treated according to guidelines and satisfied of their care. Ferreira et al.18 compared the characteristics and outcomes of HF patients with worsening HF enrolled in BIOSTAT-CHF (BIOlogy Study to TAilored Treatment in Chronic Heart Failure) either as inpatients or as outpatients. Inpatients had higher rates of the primary outcome of death or HF hospitalization with a rate of 33.4 vs. 18.5 per 100 person-years. However, the primary outcome event rates were high also for outpatients: 8.4%, 29.8% and 43.3% in the low, intermediate, and high-risk categories, respectively. These findings suggest that also outpatients with worsening HF have poor prognosis and may be the focus of future trials.

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)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
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 enseignants0,276
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
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 Opus0,009
Tête enseignante GPT0,278
Écart entre enseignants0,269 · 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
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

Citations3
Publié2019
Routes d'admission1
Résumé présentoui

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Même revueEuropean Journal of Heart FailureMême sujetHeart Rate Variability and Autonomic ControlTravaux en français237 207