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Enregistrement W2090985469 · doi:10.1111/j.1527-5299.2006.06103.x

Congestive Heart Failure Patient Factors in the Device Era

2006· letter· en· W2090985469 sur OpenAlexaboutno aff
Gretchen Wells

Notice bibliographique

RevueCongestive Heart Failure · 2006
Typeletter
Langueen
DomaineMedicine
ThématiqueCardiac pacing and defibrillation studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHeart failureMedicineCardiac resynchronization therapyEjection fractionCardiologyInternal medicineQuality of life (healthcare)PopulationRandomized controlled trialCanadian Cardiovascular SocietySinus rhythmIntensive care medicineImplantable cardioverter-defibrillatorAtrial fibrillationMyocardial infarction

Résumé

récupéré en direct d'OpenAlex

Nonpharmacologic approaches (ie, device-based therapies) for the treatment of congestive heart failure (CHF) are now the standard of care. Currently, implantable cardioverter-defibrillators (ICDs) are considered for the heart failure patient with both ischemic and nonischemic heart disease, New York Heart Association (NYHA) class II or III symptoms, and an ejection fraction (EF) ≤35%. Cardiac resynchronization therapy (CRT) should be considered in the heart failure patient with an EF ≤35% with sinus rhythm and NYHA class III or IV heart failure (HF) on optimal medical therapy with evidence of cardiac dyssynchrony. As the number of individuals with HF grows in the United States, more devices will likely be implanted. Moreover, the indications for device therapy may expand pending the outcome of ongoing trials.1 It is therefore imperative that the clinician become familiar with the management of HF patients in the device era. An important but frequently overlooked concern in this population—the impact of quality of life, anxiety, and depression in the device patient—is addressed by Sears and colleagues2 in this issue of Congestive Heart Failure. The purpose of their paper is 2-fold: (1) to review the development of device-based CHF management, and (2) to provide approaches on how to optimally manage patient outcomes. There are key points to cover while discussing the possibility of an implanted device with HF patients. One of the first issues to be addressed should be patient and caregiver expectations. The Multicenter InSync Randomized Clinical Evaluation (MIRACLE) trial3 clearly demonstrated significant clinical improvement with CRT, including quality-of-life indices in the patient with moderate-to-severe HF and an intraventricular conduction delay. The CRT patient should be well educated, however, regarding expected outcomes in the individual patient. Up to one third of CRT patients are “nonresponders.” Efforts to identify patients who will benefit most from CRT continue to be studied. One target is to focus on mechanical dyssynchrony identified by echocardiography rather than electrical dyssynchrony (QRS duration). In patients with NYHA class III or IV HF, up to one third of patients who meet standard criteria for CRT may have little or no dyssynchrony on echocardiography, and one third of patients with a narrow QRS will demonstrate significant mechanical dyssynchrony. The ongoing Predictors of Response to Cardiac Resynchronization Therapy (PROSPECT) trial4 will determine whether echocardiographic parameters of dyssynchrony can predict a favorable clinical response to CRT. The patient with an ICD should be carefully counseled regarding the goals of the device. Although indicated for the primary prevention of malignant arrhythmias for the HF patient with an EF ≤35%, the absolute risk of sudden death for this population is still low. In addition to appropriate shocks, inappropriate shocks can be quite distressing. Studies have confirmed, however, that most patients tolerate the device well and that the quality of life with ICD therapy is superior to that with antiarrhythmic drugs.5 Nevertheless, in patients with frequent shocks (a subgroup of about 15%), the percentage of psychologically distressed patients is >50%.6 Depression is increasingly recognized in the HF population. A recent meta-analysis found the prevalence of depression in the population to be 21.5%, which is 2–3 times the rate of that found in the general population.6 Moreover, there are higher rates of death, increased health care use, and hospitalizations in the clinically depressed HF patient. Therefore, identification of such patients is necessary so that appropriate treatment can be instituted. Patient self-assessment is increasingly recognized as an important component of evaluation of the HF patient. Studies suggest that clinically significant depression is identified more readily by questionnaire than by diagnostic interview.7 Sears and associates2 outline methods to screen the device population for anxiety and depression. Once a concern is identified, appropriate treatment should be initiated. The “ABC model” proposed by the authors incorporates appropriate education prescriptions, behavioral prescriptions, and cognitive coping prescriptions. This approach has not been rigorously evaluated in the device population, and the authors suggest that future studies are necessary to determine the utility of cognitive—behavioral treatments in the HF patient with a device. What this important article teaches us is that there are poorer clinical outcomes for the HF patient with depression, anxiety, or poor quality of life. Recent studies have confirmed that depression is common in the HF population and may be underrecognized. As more devices are implanted in this group, strategies to optimize patient outcome will need to be employed. Sears and coworkers have devised a method to identify patient concerns and target quality-of-life activities. We can all look forward to an explosion of research assessing cognitive—behavioral treatments in patients with CHF and implanted devices.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,457
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,004
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 Opus0,024
Tête enseignante GPT0,274
Écart entre enseignants0,251 · 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
GenreCommentaire

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
Publié2006
Routes d'admission1
Résumé présentoui

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