Combined Influence of Depression and Physical Frailty on Cognitive Impairment in Patients with Heart Failure

Prevalence of cognitive frailty and its associated factors in a population of Iranian older adults

BACKGROUND

In recent years cognitive frailty has emerged as an important predictor of adverse health outcomes in older adults. Herein, we aimed to investigate the prevalence and associated factors of cognitive frailty in a population of community-dwelling older adults in Iran.

METHOD

This cross-sectional study was conducted as part of the second cycle of the Amirkola Health and Aging Project (AHAP). Physical frailty and cognitive impairment were evaluated using the FRAIL questionnaire and the mini-mental state examination (MMSE) respectively. Cognitive frailty was defined as co-existence of frailty and cognitive impairment without presence of dementia. Depression and disability were assessed using the Persian version of geriatric depression scale (GDS) and instrumental activities of daily living (IADL) questionnaire.

RESULTS

Overall 1775 individuals (47.1% female) with mean age of 69.7 ± 7.3 years were included in the final analysis. The prevalence of cognitive frailty was 12.0%. The prevalence of cognitive frailty among males and females was 4.3% and 20.7%, respectively. After adjusting for all possible confounders through binary logistic regression analysis, factors such as older age (OR 1.06, CI 1.03-1.09), female gender (OR 2.25, CI 1.42-3.57), illiteracy (OR 3.84, CI 2.03-8.29), more comorbidities (OR 1.21, CI 1.12-1.31), depression (OR 2.01, CI 1.40-2.86), and greater IADL disability (OR 1.68, CI 1.44-3.96), were independently and significantly associated with cognitive frailty.

CONCLUSION

In this population of Iranian older adults, prevalence of cognitive frailty was consistent with its estimated mean global prevalence. Age, gender, illiteracy, comorbidities, depression and IADL disability were associated with cognitive frailty. Further research is required to develop screening tools and prevention strategies.

The Prevalence of Cognitive Frailty and Its Association with Sleep duration and Depression Among Older Adults with Heart Failure

OBJECTIVES

The prevalence of cognitive frailty and its associated factors in older population with heart failure have not been extensively studied. We investigated the prevalence of cognitive frailty and its association with sleep duration and depression among older adults with heart failure.

METHODS

This secondary analysis used a cross-sectional sample with 168 older adults with heart failure from an academic tertiary care hospital in South Korea.

RESULTS

Cognitive frailty was found in 58 (34.5%) older adults with heart failure. Our main finding revealed that longer sleep durations of ≥ 8 h (adjusted odds ratio (OR) = 2.62, 95% confidence interval (CI) = 1.04-6.59) and depression (adjusted OR = 2.84, 95% CI = 1.22-6.61) predicted an increased risk of cognitive frailty.

CONCLUSIONS

Early detection on changes in sleep patterns and depression can play a crucial role in reducing the risk of cognitive frailty in patients with heart failure. Longitudinal studies are needed to explore that sleep patterns and depression are both linked to greater risk of developing cognitive frailty among older adults with heart failure.

CLINICAL IMPLICATIONS

Development of a validated instrument for cognitive frailty screening may be beneficial for older adults with heart failure to prevent adverse outcomes.

Importance analysis of psychosociological variables in frailty syndrome in heart failure patients using machine learning approach

The prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients' responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important than the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professionals, including psychologists and social workers, in drawing their attention to the non-physical origins of HF.

Association of sleep duration and physical frailty with cognitive function in older patients with coexisting atrial fibrillation and heart failure

AIM

To investigate the associations of sleep duration and physical frailty with cognitive function in older patients with both atrial fibrillation and heart failure.

DESIGN

This study used a cross-sectional, secondary data analysis design.

METHOD

We included outpatients aged ≥ 65 years with coexisting atrial fibrillation and heart failure in South Korea. We used a sample of 176 patients (men = 100) with HF among 277 data from the parent study. The data were collected through a self-report, structured questionnaire and electronic medical record.

RESULTS

Our main finding showed that long sleep duration and physically frail status were significant predictors of cognitive impairment in older adults with both atrial fibrillation and heart failure. Healthcare providers should be aware of the importance of assessing sleep duration and physical activity in older adults with both atrial fibrillation and heart failure to prevent or delay cognitive impairment.

Prevalence and Incidence of Cognitive Impairment and Dementia in Heart Failure - A Systematic Review, Meta-Analysis and Meta-Regression

INTRODUCTION

The burden of cognitive impairment in HF patients is significant and leads to longer hospital stay, higher readmission rates, and increased mortality. This review seeks to synthesize the available studies to determine the prevalence and incidence of cognitive impairment and dementia in HF patients.

METHODS

PubMed, Embase, PsychoINFO and Cochrane databases were systematically searched from their inception through to 3 May 2021. Study and population characteristics, total patients with HF, prevalence of cognitive impairment and dementia in HF patients and cognitive assessment tool were abstracted by two reviewers.

RESULTS

In heart failure patients, overall prevalence for cognitive impairment and dementia was 41.42% (CI) and 19.79% (dementia) respectively. We performed a meta-regression analysis which demonstrated that the risk of cognitive impairment and dementia increased with age.

DISCUSSION

Further research should investigate whether HF accelerates the rate of cognitive decline and the progression of dementia.

Brain Imaging Changes and Related Risk Factors of Cognitive Impairment in Patients With Heart Failure

Background/Aims

To explore the imaging changes and related risk factors of heart failure (HF) patients with cognitive impairment (CI).

Methods

A literature search was systematically carried out in PubMed, Web of Science, Embase, and Cochrane Library. In this systematic review, important relevant information was extracted according to the inclusion and exclusion criteria. The methodological quality was assessed by three scales according to the different study types.

Results

Finally, 66 studies were included, involving 33,579 patients. In the imaging changes, the severity of medial temporal lobe atrophy (MTA) and the decrease of gray Matter (GM) volume were closely related to the cognitive decline. The reduction of cerebral blood flow (CBF) may be correlated with CI. However, the change of white matter (WM) volume was possibly independent of CI in HF patients. Specific risk factors were analyzed, and the data indicated that the increased levels of B-type natriuretic peptide (BNP)/N-terminal pro-B-type natriuretic peptide (NT-proBNP), and the comorbidities of HF, including atrial fibrillation (AF), diabetes mellitus (DM) and anemia were definitely correlated with CI in patients with HF, respectively. Certain studies had also obtained independent correlation results. Body mass index (BMI), depression and sleep disorder exhibited a tendency to be associated with CI. Low ejection fraction (EF) value (<30%) was inclined to be associated with the decline in cognitive function. However, no significant differences were noted between heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) in cognitive scores.

Conclusion

BNP/NT-proBNP and the comorbidities of HF including AF, DM and anemia were inextricably correlated with CI in patients with HF, respectively. These parameters were independent factors. The severity of MTA, GM volume, BMI index, depression, sleep disorder, and low EF value (<30%) have a disposition to associated with CI. The reduction in the CBF volume may be related to CI, whereas the WM volume may not be associated with CI in HF patients. The present systematic review provides an important basis for the prevention and treatment of CI following HF.

Integrating assessment of cognitive status in elderly cardiovascular care

Cardiovascular clinicians tend to pay little attention to issues related to cognition, and yet those caring for older adults will encounter a variety of conditions that may lead to cognitive impairment. Most commonly, these include cardiovascular disease-specific conditions such as cerebrovascular disease or heart failure, but may also include neurodegenerative conditions, mood disorders, medication side effects and polypharmacy, and nutritional deficiencies and metabolic derangements among others. This review presents evidence supporting the importance of assessing cognitive status in older adults with cardiovascular disease, and suggests a practical approach to assessment and management of cognitive impairment in this population when it is found. Special attention is paid to the importance of collaboration between cardiovascular and geriatric specialists, and the value it may bring to patients.