Depressive Symptoms, Cognition, and Everyday Function Among Community-Residing Older Adults

Brewster, Glenna; Peterson, Lindsay; Roker, Rosalyn; Ellis, Michelle L.; Edwards, Jerri D.

doi:10.1177/0898264316635587

Cited by 29 publications

(13 citation statements)

References 52 publications

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“…The findings also suggest that individuals with HFpEF have more depressive symptoms compared with individuals with no history of HF. Given the high prevalence of depressive symptoms in HFpEF [66][67][68] and the association between depressive symptoms and cognitive impairment, [69][70][71] screening for depressive symptoms should be incorporated into clinical practice.…”

Section: Implications For Practicementioning

confidence: 99%

Factors Associated With Cognitive Impairment in Heart Failure With Preserved Ejection Fraction

et al. 2020

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Background: Cognitive impairment is prevalent in heart failure and is associated with higher mortality rates. The mechanism behind cognitive impairment in heart failure with preserved ejection fraction (HFpEF) has not been established. Objective: The aim of this study was to evaluate associations between abnormal cardiac hemodynamics and cognitive impairment in individuals with HFpEF. Methods: A secondary analysis of Atherosclerosis Risk in Communities (Atherosclerosis Risk in Communities) study data was performed. Participants free of stroke or dementia who completed in-person assessments at visit 5 were included. Neurocognitive test scores among participants with HFpEF, heart failure with reduced ejection fraction (HFrEF), and no heart failure were compared. Sociodemographics, comorbid illnesses, medications, and echocardiographic measures of cardiac function that demonstrated significant (P < .10) bivariate associations with neurocognitive test scores were included in multivariate models to identify predictors of neurocognitive test scores among those with HFpEF. Multiple imputation by chained equations was used to account for missing values. Results: Scores on tests of attention, language, executive function, and global cognitive function were worse among individuals with HFpEF than those with no heart failure. Neurocognitive test scores were not significantly different among participants with HFpEF and HFrEF. Worse diastolic function was weakly associated with worse performance in memory, attention, and language. Higher cardiac index was associated with worse performance on 1 test of attention. Conclusions: Cognitive impairment is prevalent in HFpEF and affects several cognitive domains. The current study supports the importance of cognitive screening in patients with heart failure. An association between abnormal cardiac hemodynamics and cognitive impairment was observed, but other factors are likely involved.

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Section: Implications For Practicementioning

confidence: 99%

Factors Associated With Cognitive Impairment in Heart Failure With Preserved Ejection Fraction

et al. 2020

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“…D uring the past decade, it has been increasingly recognized that deficits across multiple domains of cognitive function are commonly experienced by those with major depressive disorder (MDD) and mediate poor psychosocial and workplace outcomes. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] Moreover, insufficient outcomes in MDD are observed in persons who are "euthymic," underscoring the mediational role of other nonmood MDD domains in determining health outcomes. 4,[7][8][9][10] The mediational role of cognitive function in affecting health outcomes in MDD invites the need for direct assessment of cognitive functions with tools that are freely available, computerized, user-friendly, and patient-administered that provide actionable information.…”

mentioning

confidence: 99%

The THINC-Integrated Tool (THINC-it) Screening Assessment for Cognitive Dysfunction

McIntyre

Best

Bowie

et al. 2017

J. Clin. Psychiatry

112

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“…Previous studies have identi ed that cognitive function deteriorates naturally with age [28]. Depressive symptoms were associated with cognitive decline [29], so CESD-10 score can be viewed as a predictor. High waist circumference suggested abdominal obesity, nowadays, urban residents often replace walking with transportation and thereby have insu cient exercise, furthermore, the habit of drinking alcohol and unhealthy diet, and so on, which are not only associated with abdominal obesity but also have an impact on cognition [30][31].…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction of cognitive trajectories among middle-aged and elderly Chinese adults

Jia

Xiang

et al. 2021

Preprint

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Background Cognition represents heterogeneous in trajectories with increasing age. Early identification of such trend is essential for the prevention of high risk population related to cognition decline. This study aimed to explore the heterogeneity and determinants of cognitive trajectories, and construct prediction models for distinguishing cognitive trajectories among the middle-aged and elderly Chinese people at a community level. Methods Data was retrospectively collected from the China Health and Retirement Longitudinal Study with a consecutive survey in 3 waves (2011, 2013, 2015). The Mini-mental State Examination (MMSE) scale was used for cognitive measurement. The heterogeneity of cognitive trajectories was identified through mixed growth model, and the determinants of cognitive trajectories were analyzed by logistic regression. Machine learning (ML) algorithms, namely logistic regression (LR), support vector machine (SVM), and random forest (RF), combined with recursive feature elimination were used to predict cognitive trajectories using epidemiological variables. Area under the receiver operating characteristic curve (AUROC) and brier score were used to assess discrimination and calibration, respectively. Results Three cognitive trajectories were identified: “persistently-high” (PH, 83.3%) class with a high cognitive score in baseline and remained high subsequently, “medium-decrease” (MD, 6.6%) class with a medium cognitive score in baseline and a rapid decline subsequently, “low-increase” (LI, 10.1%) with the lowest baseline cognitive score and a rapid growth subsequently. For classification of PH vs. LI and MD vs. LI, the AUROCs of three ML methods were almost larger than 0.90, and brier scores were almost less than 0.10. For PH vs. MD, ML performed less well with the maximum AUROCs close to 0.68, while the calibration was still good. The initial MMSE score was the most significant predictors for distinguishing MD vs. LI and PH vs. LI, while age was the most important predictors in distinguishing MD vs. PH. Conclusions Prediction of cognitive trajectories and identification of its key predictors are of great significance for understanding the heterogeneity of cognitive trajectories as well as interventions for cognitive decline.

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Depressive Symptoms, Cognition, and Everyday Function Among Community-Residing Older Adults

Cited by 29 publications

References 52 publications

Factors Associated With Cognitive Impairment in Heart Failure With Preserved Ejection Fraction

Factors Associated With Cognitive Impairment in Heart Failure With Preserved Ejection Fraction

The THINC-Integrated Tool (THINC-it) Screening Assessment for Cognitive Dysfunction

Machine learning-based prediction of cognitive trajectories among middle-aged and elderly Chinese adults

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