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Enregistrement W2930874916 · doi:10.1002/ejhf.1442

Cognitive Impairment in Asian Patients with Heart Failure: Prevalence, Biomarkers, Clinical Correlates, and Outcomes

2019· letter· en· W2930874916 sur OpenAlexaboutno aff
YanHong Dong, Shuan Yong Teo, Kathleen Kang, Melissa Tan, Lieng Hsi Ling, Poh Shuan Daniel Yeo, David Sim, Fazlur Jaufeerally, Kui Toh Gerard Leong, Hean Yee Ong, Dinna Soon, Lee Sheldon, Seet Yoong Loh, Ru‐San Tan, Siew Pang Chan, Mark Richards, Carolyn S.P. Lam

Notice bibliographique

RevueEuropean Journal of Heart Failure · 2019
Typeletter
Langueen
DomaineMedicine
ThématiqueHeart Failure Treatment and Management
Établissements canadiensnon disponible
Organismes subventionnairesNational Medical Research CouncilMedical Research Council
Mots-clésMedicineHeart failureCognitive impairmentInternal medicineCognitionCardiologyIntensive care medicineDiseasePsychiatry

Résumé

récupéré en direct d'OpenAlex

Cognitive impairment (CI) is prevalent in heart failure (HF) patients in western populations (25–74%) but such information among Asian populations is scant.1 HF patients with impairment in multiple cognitive domains have higher risk of developing dementia.2 CI links to poor self-care, leading to poorer prognosis and increased mortality.3 The 2016 European Society of Cardiology (ESC) guidelines highlight the importance of customized management for cognitively impaired HF patients to improve self-care.4 Amino-terminal pro-brain natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT) are diagnostic markers for HF and acute myocardial infarction, respectively. They predict mortality in HF patients5 and associate with CI in older adults.6 Given the limited data on CI in Asian HF patients, we examined the prevalence of CI in Singaporean HF patients and explored correlations between CI and biomarkers, clinical factors, quality of life (QoL), and outcomes. One hundred patients (> 18 years old) with diagnosis of HF (according to ESC criteria) were prospectively and consecutively recruited in compensated chronic HF status from Singapore hospitals.7 All patients were assessed by trained research personnel using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and a locally validated formal neuropsychological test battery covering seven domains (Attention, Language, Verbal and Visual Memory, Visuoconstruction, Visuomotor Speed, Executive Function). Since brief screening tests are unable to diagnose CI, a comprehensive formal neuropsychological test battery was used for diagnosis. Education-adjusted cut-offs of 1.5 standard deviation below the established norms were used on individual tests. Failure in at least half of the tests in a domain constituted failure. CI is defined by impairment in at least one domain. Clinical factors included left ventricular ejection fraction (LVEF) and cardiovascular risk factors such as diabetes mellitus (DM), hypertension, coronary artery disease (CAD), atrial fibrillation (AF), smoking, stroke, and chronic kidney disease (CKD). Plasma for NT-proBNP and hs-cTnT assays was collected at baseline. Health outcomes and QoL (Minnesota Living with Heart Failure Questionnaire) were collected at 1 year. Continuous variables were expressed as mean (± standard deviation) and categorical variables as percentages. Structural equation modelling (SEM) analysis adjusted for demographics examined the association between CI, biomarkers, clinical factors, QoL, hospitalization for HF, and death. Receiver operating characteristic (ROC) analysis examined the discriminatory ability of MMSE and MoCA for CI determined by formal neuropsychological tests. Chi-squared automatic interaction detector algorithm identified the optimal cut-points for quantitative predictors. This study complied with the Declaration of Helsinki, received ethics approval, and informed consent was obtained from all participants. Among 100 Asian patients with chronic HF [age: 58.68 ± 10.53; female: 15%; hypertension: 65%, DM: 54%, CAD: 62%, AF: 20%; CKD: 42%; prior stroke: 14%; reduced LVEF (< 50%): 65%], 44 of 100 HF patients had undiagnosed CI (50% with multiple domain impairment). Four participants were excluded due to outliers in NT-proBNP or hs-cTnT level (n = 2) or missing biomarker data (n = 2). HF patients with CI were older and less educated than those without CI (age: 64.3 ± 8.3 vs. 54.2 ± 10.1, P < 0.0001; education: 7.98 ± 4.2 vs. 10.0 ± 3.4 years, P = 0.010) (Table 1). Cognitively impaired patients had normal MMSE (25.2 ± 3.4) but low MoCA scores (21.3 ± 4.6). Patients with CI more frequently had ischaemic HF and more cardiovascular risk factors than those without CI (ischaemic aetiology: 71.4% vs. 44.4%, P = 0.004; total cardiovascular risk factors: 3.48 ± 1.7 vs. 2.76 ± 1.5, P = 0.030). Their neuropsychological impairment was characteristic of vascular pathology with frequently impaired Visuomotor Speed (information processing speed) (60%), Visuoconstruction (construction of geometric figures) (48%), and Visual Memory (recall of visual information) (43%). SEM analysis showed that age strongly predicts CI (β = 1.113, P = 0.002). Older patients (> 63 years old) had six times greater risk of having CI per additional year (β = 6.086, P = 0.003). In HF patients with stroke (n = 14), 64% (n = 9) had CI (six with multiple domain impairment). Since stroke strongly associates with CI, those with documented stroke were excluded to elucidate cognition correlations with HF. Excluding age and patients with prior stroke (n = 14), NT-proBNP was independently associated with CI, hospitalization, and QoL (CI: β = 1.660, P = 0.026; hospitalization: β = 2.538, P = 0.010; QoL: β = 8.412, P < 0.001). By ROC analyses in this subset, MMSE and MoCA were similar in detecting CI [area under the curve (95% confidence interval): 0.740 (0.641–0.840)/0.770 (0.675–0.866), P = 0.481], with optimal cut-off points of < 28 for MMSE and < 25 for MoCA (sensitivity 0.79/0.71; specificity 0.63/0.61; positive predictive value 0.62/0.59; negative predictive value 0.79/0.73; classification accuracy 69.8%/65.6%, respectively). In this pilot study, the prevalence of undiagnosed CI in Asian HF patients was high (44%). Older patients have greater risk of CI. Excluding age and documented stroke, NT-proBNP predicted CI, hospitalization, and QoL. The high rate of undiagnosed CI is concerning as CI will affect HF self-care given medication complexity, numerous lifestyle modifications, and recognition of symptoms.3 Using published MMSE cut-point (< 24), 74% of cognitively impaired patients would be undetected without formal neuropsychological evaluation. Optimal cut-points of MMSE < 28 and MoCA < 25 in our study are consistent with previous study.8 NT-proBNP is associated with CI in cerebrovascular diseases.6 Here, NT-proBNP was associated with CI even without documented stroke, suggesting that HF per se may be related to CI. Since there is no guideline for cognitive screening in HF,1 we propose that HF patients without prior stroke > 63 years old, with MMSE < 28 or MoCA < 25, or high NT-proBNP, may be considered high risk and be formally evaluated for CI. Several limitations require acknowledgement. First, a small sample size limits the generalizability of our findings. A larger study is needed to validate the suggested cut-offs for detecting CI in HF patients. Secondly, the lack of a healthy control group limits our conclusion on whether CI is associated with HF per se. In conclusion, the prevalence of undiagnosed CI in Asian HF patients is high. HF patients with high levels of NT-proBNP may be at risk of developing CI. Therefore, HF patients without prior stroke > 63 years old, with MMSE < 28 or MoCA < 25, or high NT-proBNP, may warrant formal evaluation for CI. This work was supported by National Medical Research Council (NMRC) Research Training Fellowship Seed Funding [NMRC/SEEDFUNDING/014/2015] and NMRC Transition Award [NMRC/TA/0060/2017]. Conflict of interest: none declared.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,013
Tête enseignante GPT0,267
Écart entre enseignants0,254 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations27
Publié2019
Routes d'admission1
Résumé présentoui

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