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Enregistrement W2587180018 · doi:10.1111/jgs.14762

Natriuretic Peptides in Older Patients With Heart Failure: A Look Into the Seeds of Time

2017· letter· en· W2587180018 sur OpenAlexaffabout
Abhinav Sharma, Karen P. Alexander

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2017
Typeletter
Langueen
DomaineMedicine
ThématiqueHeart Failure Treatment and Management
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicineHeart failurePopulationNatriuretic peptideIncidence (geometry)ConfoundingPopulation ageingIntensive care medicineGerontologyInternal medicineEnvironmental health

Résumé

récupéré en direct d'OpenAlex

If you can look into the seeds of time, And say which grain will grow and which will not, Speak, then, to me… Heart failure (HF) is primarily a disease of older adults. With the aging of the US population, demand for HF care is increasing steadily.1 HF incidence rises from approximately 20 per 1,000 individuals 65 to 69 years of age to more than 90 per 1,000 individuals ≥85 years of age.2, 3 Furthermore, the prevalence of HF is greater than 10% among those aged ≥75 years of age.2 HF is also a substantial cost-driver within the US healthcare system. With approximately 1 million HF hospitalizations and 3 million physician visits for HF in the US each year, the yearly cost attributed to HF in the United States in 2010 was $39.2 billion.4-6 By 2030, this is estimated to increase 127% reaching $69.7 billion.7 These numbers indicate the scope of HF pandemic, but this problem is compounded by inherent complexity in managing older patients with HF. Older HF patients often have multiple comorbidities confounding diagnostic certainty based on symptoms. They are frequently excluded from randomized controlled trials,8 and suffer competing risk of mortality. Natriuretic peptides show great promise for improving the diagnosis, prognostication, risk stratification, and guiding therapies for HF.9 However, understanding how to utilize natriuretic peptides in the older population is essential. Brain Natriuretic Peptide (BNP) and its amino-terminal cleavage equivalent (NT-proBNP) are generated by cardiomyocytes in response to myocardial stretch. Mostly based on evidence derived from younger populations, natriuretic peptides can be used to support the diagnosis of HF in ambulatory patients with dyspnea.5, 6, 10 Natriuretic peptides also support the diagnosis and establish disease severity and prognosis in hospitalized patients with HF. However, natriuretic peptides may be elevated in the setting of comorbidities such as valvular heart disease, hypertension, pulmonary hypertension, pulmonary embolism, sepsis, renal failure, and ischemic heart disease–more common in older populations. Natriuretic peptides may also be elevated among older patients in the absence of HF, presumably from subclinical changes in heart structure and function.4-6, 9 The International Collaborative of NT-proBNP study (ICON) was a pivotal study that demonstrated age-based stratification of NTproBNP levels improved the positive and negative predictive value of NTproBNP in acutely dyspneic patients. Similarly age-based stratification improved the negative predictive value of NTproBNP among stable ambulatory patients.11 The importance of considering older age in diagnostic thresholds for BNP is well described. However, the utility of natriuretic peptides for prognostic risk stratification among older patients hospitalized with HF has not been explored. In this issue of the journal, Passantino and colleagues evaluate the prognostic factors, including natriuretic peptide levels, associated with short (2 months) and long term (1 year) mortality in a cohort of older patients admitted with HF.12 This retrospective study in two Italian centers evaluated 279 patients aged ≥75 years of age. The mean age was 80 years and the average survival time was 2.2 years. This study contributes two points about prognostic risk stratification in older adults with HF. The first is that HF among older patients is associated with very high all-cause mortality. The second is that NTproBNP has strong independent dose-response association with all-cause mortality among this hospitalized population with HF. Passantino and colleagues report a mortality of 36% at 1 year and 77% at 5 years in this population. This is similar to a 1-year mortality of 37% reported among 2,540,838 Medicare beneficiaries hospitalized with HF (median age 80 years).3 What is unclear is the cause of death among these patients; due to the multiple competing risks of death in an older population, these individuals may be dying of HF or other causes. A post-hoc evaluation of The Trial of Intensified versus Standard Medical Therapy in Elderly Patients with Congestive Heart Failure (TIME-CHF) identified that non-cardiovascular death formed a significant burden of all-cause mortality; furthermore, the distribution of cardiovascular to non-cardiovascular death varied based upon the presence of preserved or reduced ejection fraction.13 Similarly, rates of non-cardiovascular and cardiovascular death may vary across BNP levels. Passantino and colleagues found that NTproBNP was one of the strongest predictors in a multivariable model of short and long term mortality. Other independent predictors included estimated glomerular filtration rate, hemoglobin, diabetes, systolic blood pressure on admission, and moderate to severe tricuspid regurgitation. The NTproBNP levels rose in informative value to 6,000 pg/mL with an optimum cut point for predicting 2-month and 1-year mortality around 8,200 pg/mL. In comparison, among 33,349 Center for Medicare/Medicaid patients admitted in the US within the Get-With-The-Guidelines-Heart Failure registry (CMS-GWTG-HF; median age 80), BNP was a identified as an independent predictor of 30-day mortality; however, blood urea nitrogen (BUN), systolic blood pressure on admission, age, and respiratory rate were even stronger predictors.14 While comprehensive, the modeling by Passantino and colleagues lacks several variables including BUN, dementia, discharge to nursing home, availability of specialized heart failure clinic or cardiology follow-up. Such variables are likely critical when evaluating mortality among older patients. Furthermore, this study focused on mortality, not symptomatic improvements or rehospitalization. Prognostic stratification based on natriuretic peptides serves to place patients across a spectrum of risk; ideally, those at the highest risk should be treated more intensely with close follow-up. Presumably higher natriuretic peptide values in this population were noted by treating physicians and influenced care delivered, but information on specifics of intensification of treatment or HF symptoms over time is not available. Of note, the predictive information of baseline natriuretic peptides was remarkably similar for 2-month and 1-year mortality. Even if acute interventions improve symptoms, outcomes at 2 months and 1 year seem fixed in relation to initial BNP levels. Current US guidelines suggest that natriuretic peptide guided HF therapy can be useful in guideline directed medical therapy (Class IIa, Level of Evidence: B)5; however, the randomized trials used to evaluate natriuretic peptide guided HF therapy were heterogenous, with small sample sizes, varying methodology, and mixed results. In the meta-analysis of biomarker guided therapy, the signal for benefit was only among those aged less than 75 years. Recently, the NIH funded GUIDE-IT trial,15 the largest trial in NTproBNP guided HF therapy, was stopped 18 months early due to lack of benefit for biomarker guided therapy compared to usual care. Given that quality of life and time spent out of the hospital may be a more valued among older patients, the prognostic utility of NTproBNP in terms of rehospitalization remain an important question to answer. The role of natriuretic peptides in mortality risk stratification in older HF patients seems clear. We need more information about natriuretic peptides and cardiovascular and non-cardiovascular cause of death, and information on the use of natriuretic peptides to risk stratify for rehospitalization or advanced therapies such as cardiac resynchronization devices, implantable cardioverter defibrillators (ICDs), and mechanical circulatory support. Finally, the role of natriuretic peptides in end of life decision making warrants consideration. For example, the decision to turn off an ICD may be informed by a natriuretic peptide level which indicates very high short term morality regardless of a defibrillator shock. As the population ages, managing the HF epidemic will place continued demands on the healthcare system so continued efforts to clarify the use of natriuretic peptides to inform that care and improve the lives of older patients with HF will be crucial. Conflict of Interest: AS is supported by an Alberta Innovates Health Solution Clinician Scientist Fellowship. He has received research support from Roche Diagnostics, the Heart Failure Society of America, and the Bayer-Vascular Canadian Cardiovascular Society Research Grant. AS has no conflicts of interest to report. KA has no conflicts of interest to report. Author Contributions: Dr. Sharma and Dr. Alexander contributed to this editorial.

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)0,002
score de la tête « metaresearch » (Gemma)0,010
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: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,010
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,006
Science ouverte0,0010,001
Intégrité de la recherche0,0030,007
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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 Opus0,005
Tête enseignante GPT0,231
Écart entre enseignants0,226 · 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
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
Publié2017
Routes d'admission2
Résumé présentoui

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Même revueJournal of the American Geriatrics Society→Même sujetHeart Failure Treatment and Management→Travaux en français237 207→