November 2016 at a Glance: The Left Atrium, Screening for Heart Failure, Multimodality Imaging for Cardiac Resynchronization Therapy
Notice bibliographique
Résumé
Omersa et al. have analysed the Slovenian hospitalization database to examine the burden of heart failure (HF) hospitalizations in the years 2004–2012.1 Age standardized HF hospitalization rates have decreased by approximately 7% over the years. Crude rates have increased, likely because of aging of the population. HF hospitalizations remain burdened by a high readmission rates, 55% for all-cause readmissions and 37.5% for HF readmissions, in the year after discharge. Comorbidities were confirmed as major risk factors.1 These data are consistent with recent findings from other European countries.2-4 We have two articles, both based on the screening of asymptomatic subjects with ≥1 risk factor for HF and with normal left ventricular (LV) ejection fraction. The first study excluded also the subjects with valve disease or atrial fibrillation and was based on echocardiography. Overall, 62% of the subjects had ≥1 abnormality of the cardiac structure or function: LV hypertrophy, 13%, abnormal E/e', 12%, impaired global longitudinal strain (GLS), 33%, left atrial enlargement, 31%. During a median follow-up of 14 months, 12% of the subjects developed symptomatic HF or had a cardiovascular death and these were accurately predicted by the echocardiographic abnormalities.5 The second study was based on biomarkers. Among them, N-terminal-pro-brain natriuretic peptide (NT-proBNP) and high-sensitivity troponin I (hsTnI) were the most powerful predictors of HF events and major adverse cardiac events (MACE). The Authors then evaluated the efficiency of a marker-based approach using a STOP-HF-like subgroup of subjects. It was estimated that the number needed to screen to prevent one HF event or MACE would have been <100 and the number needed to treat ≤20 or 10 to prevent one HF event or one MACE, respectively.6 A marker-based screening strategy seems therefore as potentially useful to prevent cardiovascular events. The role of the left atrium in HF has often been neglected, compared with the left ventricle. However, its importance is steadily increasing along with the growing need to better evaluate HF with preserved ejection fraction (HFpEF) and concomitant arrhythmias, namely atrial fibrillation.7-9 The review by Triposkiadis et al. in this issue of the journal gives justice to this need. It shows the abnormalities in the mechanical, neurohormonal and regulatory function of the left atrium in HF and their contribution to LV diastolic function, exercise capacity, outcomes and effects of cardiac resynchronization therapy (CRT) in HF. Lastly, the effects of medical treatment, CRT and transcatheter therapies on left atrial function are discussed.10 Tokitsu et al. have examined the prognostic role of a very simple parameter, the pulse pressure, in a cohort of 951 consecutive patients with HFpEF.11 The pulse pressure was directly correlated with the pulse wave velocity and the stroke volume index and inversely correlated with serum haemoglobin and estimated glomerular filtration rate. It had a U-shaped relation with the rate of major cardiovascular events and HF hospitalizations with the higher rates in the patients with the highest or the lowest quintile of pulse pressure.11 Variability in the response to CRT and how to maximize its benefits remain a major issue.12-14 Lead positioning is one of the most important variables. Two studies in this issue of the journal regard the use of multimodality imaging for lead placement for CRT.15, 16 In the first study, the non-scarred myocardial segment with the latest mechanical activation was identified by 99 m Technetium myocardial perfusion imaging, for vitality, and speckle-tracking echocardiography, for dissynchrony. Then, cardiac computed tomography venography was used to visualize coronary sinus branches in relation to LV myocardium and select the sinus branch closest to the centre of the optimal pacing site. The study was designed as a double-blind, prospective, randomized trial. The primary endpoint was based on clinical data to identify the non-responders and was a composite of death, hospitalizations, lack of improvement in New York Heart Association class and/or in the 6 min walk distance. A total of 215 patients were enrolled and 182 were randomised to the imaging group or routine lead placement. Lead placement with multimodality imaging was associated with a lower proportion of non responders (26% vs. 42%, P = 0.02). No differences in LV volumes were found.15 The second study used cardiac magnetic resonance imaging to detect non-scarred myocardial areas and longitudinal myocardial strain imaging by speckle-tracking echocardiography to identify the area with the greatest dissynchrony. The primary endpoint was, in this case, a ≥15% reduction in LV end-systolic volume and was reached in 78% of the patients assigned to the imaging modality vs. 56% of those who underwent lead placement with the routine procedure.16 A nice editorial comment by Gorcsan puts these two studies in context. We still need to know how much imaging do we need to maximize the results of CRT.17 Thus, this month we are left with new evidence of the increasing burden of HF, data showing that echocardiography or biomarkers can be used for screening of patients with asymptomatic cardiac dysfunction, a new, very simple, prognostic marker for patients with HFpEF, the pulse pressure, a thorough review of the role of the left atrium in HF and two trials suggesting that multimodality imaging may help for lead positioning for CRT.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,075 | 0,014 |
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 ».