May 2020 at a Glance: Ischaemic Heart Failure and Sex-Related Differences
Notice bibliographique
Résumé
Almost half of patients with heart failure (HF) had an ischaemic aetiology.1 However, HF occurrence in patients with chronic coronary syndrome is unclear. During a 5-year follow-up, Parma et al.2 observed a 16.4% incidence of cardiovascular death, HF hospitalization and new-onset HF (primary endpoint) in patients with chronic coronary syndrome without history of HF. Hospitalization for HF and new-onset HF occurred in 2.8% and 11.6% of patients, respectively. Malik et al.3 showed that the risk of developing HF at 5 years after the diagnosis of diabetes is low in patients without ischaemic heart disease (IHD) or end-stage renal disease (ESRD) (4%), intermediate (8-12%) in diabetic subjects with IHD or ESRD, and high (20%) in those with diabetes complicated by both IHD and ESRD. Sama et al.4 performed a network analysis to recognize pathophysiological pathways distinguishing ischaemic from non-ischaemic HF. They identified six pathways, related to inflammation, endothelial dysfunction superoxide production, coagulation, and atherosclerosis, and five key network proteins, acid phosphatase 5, epidermal growth factor receptor, insulin-like growth factor binding protein-1, plasminogen activator urokinase receptor, and secreted phosphoprotein 1, as specific for ischaemic HF and potential targets for specific treatment. The role of coronary revascularization in asymptomatic patients with HF of ischaemic aetiology remains debated.5 A comprehensive review showing the pathophysiology and diagnosis of this condition, as well as the role of coronary revascularization, is reported in this issue.6 Thuijs et al.7 performed a pre-specified analysis from the Evaluation of XIENCE Versus Coronary Artery Bypass Surgery for Effectiveness of Left Main Revascularization (EXCEL) trial. They found no differences in the composite of death, stroke, or myocardial infarction at 3 years in patients stratified by left ventricular ejection fraction (LVEF) and treated by percutaneous vs. surgical revascularization of the left main coronary artery. However, patients with reduced LVEF had worse outcomes compared to the others. Whether indications for implantable cardioverter-defibrillator (ICD) therapy after acute myocardial infarction should be merely based on LVEF is debated.8 Docherty et al.9 developed and validated a risk model to predict the occurrence of sudden cardiac death early and up to 2 years after myocardial infarction. Including simple variables, this score could easily identify patients who may benefit from ICD therapy. Fudim et al.10 noted that 1-year mortality after ICD implantation, for primary or secondary prevention, increased with increasing age reaching an incidence of 32% in the oldest patients (median age 82 years). This confirms the importance of a careful selection of ICD candidates in order to avoid futility. Treatment of ischaemic HF with mesenchymal stem cells is an area of active research.11 Mathiesen et al.11 reported the long-term results of the Autologous Mesenchymal Stromal Cell Therapy in Heart Failure (MCS-HF) trial, a 2:1 randomized double-blind trial comparing intramyocardial injections of autologous mesenchymal stromal cells (MSCs) vs. placebo in patients with ischaemic HF. They found a significant reduction in left ventricular end-systolic volume in the treatment compared to the placebo arm. Also LVEF, stroke volume and myocardial mass improved after MCS injection. Studies exploring the efficacy of heterologous MCSs in ischaemic HF are ongoing.12 Sex-related differences in HF biomarkers are known.13 Suthahar et al.14 reviewed sex-specific differences and the interaction with obesity. Rossello et al.15 conducted a patient-level meta-analysis including three randomized trials comparing the effect of mineralocorticoid receptor antagonists (MRA) and placebo. Women differ from men in their clinical features and event rates. However, no sex-specific impact of MRA on cardiovascular mortality and HF hospitalization was noted. Interestingly, sex, and namely male sex, was an independent predictor of MRA non-use in a recent large survey.16
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,084 | 0,019 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».