Cross-sectional and longitudinal characterisation of cognitive function and outcomes in patients presenting to hospital with cardiovascular risk factors
Notice bibliographique
Résumé
Abstract Background/Introduction Cardiovascular (CV) diseases including atrial fibrillation and arteriosclerosis are associated with impaired cognitive function. Cognitive dysfunction can impact the process of shared clinical decision making, reduce adherence to polypharmacy, and decrease quality of life. The prevalence of cognitive dysfunction in contemporary patients with CV diseases and its implication on future CV events is not well known. Purpose We 1) quantified cognitive function in patients presenting to hospital with CV diseases, 2) identified clinical variables and blood biomarkers associated with cognitive dysfunction, and 3) quantified the hazard of abnormal cognitive function for predicting MACCE (major adverse CV and cerebrovascular events). Methods and results Of 1625 consecutive patients presenting acutely to a large teaching hospital with CV diseases, 614 patients (median age [Q1, Q3] 68 [58, 76] years; 66% male) who completed the Montreal Cognitive Assessment (MoCA) were analysed. The median [Q1, Q3] MoCA score was 25 points [21, 27]. 360 patients (59%) had an abnormal score (<26). At baseline, patients with abnormal scores were more likely to be female (odds ratio, OR [95% confidence intervals], 1.874 [1.287, 2.728]), have BMI<30 (OR 0.584 [0.410, 0.831]), heart failure (OR 1.492 [1.043, 2.135]), diabetes (OR 2.212 [1.529, 3.199]), chronic kidney disease (CKD-EPI<60 ml/min, OR 1.553 [1.021, 2.361]), and have more CV co-morbidities (OR per additional co-morbidity 1.415 [1.246, 1.605]). Amongst 12 CV biomarkers tested, elevated Bone Morphogenetic Protein 10 (OR 1.325 [1.022, 1.719]) and Growth Differentiation Factor 15 (OR 1.419 [1.054, 1.912]) increased odds of abnormal scores. Cox proportional hazards model adjusted for competing risk of non-CV death assessed the relationship between abnormal cognitive function and MACCE (stroke, TIA, myocardial infarction, hospitalisation for heart failure, CV death). Follow-up time ranged from 2.7 to 6.1 years. Patients were censored at 2.5 years for this analysis. 130 out of 614 patients experienced a MACCE (21%) and 71 had a non-CV death (12%). Patients with abnormal MoCA scores were at higher risk for MACCE (subhazard ratio, sHR [95% CI] 1.827 [1.253, 2.664]). The hazard remained significant after adjustment for age, sex, obesity, atrial fibrillation, stroke, heart failure, hypertension, coronary artery disease, diabetes, peripheral artery disease and renal dysfunction (sHR 1.367 [1.056, 2.326]; Figure). All-cause mortality was 1.785 times higher for those with abnormal MoCA scores [1.061, 3.002]. Conclusion In this study, 3 out of 5 patients with CV diseases had abnormal MoCA scores at baseline. Abnormal cognitive scores significantly predicted patients who went on to experience a MACCE within 2.5 years of follow-up. These observations call for further research and action to provide additional diagnostics, support and early intervention to address cognitive dysfunction in CV patients. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): EU H2020 CATCH ME Cumulative incidence function
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 ».