Cognitive performance is associated with worse prognosis in patients with heart failure with reduced ejection fraction

Fino, Patrícia; Sousa, Rita Matos; Carvalho, R.; Sousa, Nuno; Almeida, Filipa; Pereira, Vítor Hugo

doi:10.1002/ehf2.12932

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…The characteristics of the respondents were based on the type of HF; 14 articles examined chronic HF, and seven articles examined acute HF in the form of new HF or Acute Decompensated Heart Failure (ADHF) (Holm et al, 2020 ; Huynh et al, 2021 ; Miao et al, 2023 ; Pandey et al, 2019 ; Pastva et al, 2021 ; Rigueira et al, 2021 ; Seo et al, 2024 ). Three articles describe the characteristics of patients with HF from the aspect of HF reserved Ejection Fraction (HFrEF) by 2,209 respondents (Fino et al, 2020 ; Kuhn et al, 2022 ; Lee et al, 2019 ), and one article describes the characteristics of patients from the aspect of HF preserved Ejection Fraction (HFpEF) by 108 respondents (Sugie et al, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

“…The articles were collected from studies conducted in various countries. These countries include the United States (7) (Gary et al, 2019 ; Gharzeddine et al, 2021 ; Kuhn et al, 2022 ; Lee et al, 2019 ; Pandey et al, 2019 ; Pastva et al, 2021 ; Redwine et al, 2020 ), Netherlands (1) (Kuipers et al, 2022 ), Sweden (1) (Holm et al, 2020 ), Italy (1) (Vellone et al, 2020 ), Portugal (2) (Fino et al, 2020 ; Rigueira et al, 2021 ), Australia (2) (Aili et al, 2022 ; Huynh et al, 2021 ), Japan (4) (Saito et al, 2020 ; Seo et al, 2024 ; Sugie et al, 2018 ; Yamamoto et al, 2022 ), South Korea (2) (Kim et al, 2019 ; Seo et al, 2023 ), and China (1) (Miao et al, 2023 ). Seven articles were in inpatient settings (Holm et al, 2020 ; Huynh et al, 2021 ; Miao et al, 2023 ; Pandey et al, 2019 ; Pastva et al, 2021 ; Rigueira et al, 2021 ; Seo et al, 2024 ) and the rest were in outpatient settings.…”

Section: Resultsmentioning

confidence: 99%

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Mapping cognitive function screening instruments for patients with heart failure: A scoping review

Arseda,

Pahria,

Kurniawan

2024

Belitung Nurs J

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Background: Patients with heart failure (HF) often experience cognitive impairment, which negatively affects their quality of life. An effective screening tool is essential for nurses and healthcare professionals to assess cognitive function as part of HF management. Although many instruments exist, none are specifically designed for patients with HF. Objective: This study aimed to map the instruments for screening cognitive function in patients with HF. Design: A scoping review. Data Sources: Articles published between 2019 and 2023 were searched in PubMed, ScienceDirect, and Google Scholar, with the last search conducted on 27 January 2024. Review Methods: The review followed the scoping review framework by Arksey and O’Malley and adhered to PRISMA guidelines for scoping reviews. Results: Of the 21 articles meeting inclusion criteria, six cognitive function screening instruments were used across various cognitive domains, effectively identifying cognitive impairment in both inpatient and outpatient HF settings. The Montreal Cognitive Assessment (MoCA) was the most frequently used tool, covering a broad range of cognitive domains. MoCA showed high efficacy with a kappa coefficient of 0.82, Cronbach’s alpha reliability of 0.75, sensitivity of 90%, and specificity of 87%. Conclusion: Instruments like MoCA, Mini-Cog, and TICS-m show promise for assessing cognitive function in patients with HF, each with specific strengths and limitations. MoCA is notable for its comprehensive coverage despite being time-consuming and having language barriers. Further research is needed to revalidate and improve the existing instruments. It is crucial for nurses and healthcare professionals to integrate these tools into regular patient management, highlighting the need for continued research in their application.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Mapping cognitive function screening instruments for patients with heart failure: A scoping review

Arseda,

Pahria,

Kurniawan

2024

Belitung Nurs J

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“…Presented results confimed that ICT devices could be a helpful tool in differentiation the patients with HF due to cognitive abilities. Recent studies confirmed that reduced cognitive performance is associated with worse prognosis, defined as higher major adverse cardiovascular events incidence, all-cause mortality, or increased risk of rehospitalization 8,28 regardless of the cause of impaired CF.…”

Section: Discussionmentioning

confidence: 95%

Tablet-Based Assessment of Cognitive Function Among Heart Failure Patients

Urban

Horozaniecka

Wlodarczak

et al. 2022

Critical Pathways in Cardiology: A Journal of Evidence-Based Medicine

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Background: Cognitive impairment accompanying heart failure (HF) is an additional HF comorbidity, which may potentially affect the patient’s self-care and compliance. We aimed to assess cognitive function (CF) using an application with games created as a cognitive training tool for children and adults, applied using a tablet, and to compare the results obtained by HF patients with the results obtained by healthy age-matched controls. Methods: A total of 69 individuals (49 HF patients and 10 healthy controls) were assessed using 4 games dedicated to measuring cognitive skills as well as questionnaires regarding their socioeconomic status. Additionally, HF patients were asked about their quality of life and anxiety and depression. Results: HF patients demonstrated worse results in each game assessing their cognitive functions as compared to the healthy age-matched controls, which is consistent with the previous studies on CF in HF. We have also noticed interesting patterns of relations between CF and sleep and education. Conclusions: We have demonstrated that information and communication technology devices can be successfully applied as feasible tools for cognitive assessment in the HF population. This is important as tablet-based CF assessment can be done on a large population without the involvement of trained personnel.

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“…Considering CHF, PROs have a higher possibility of recording the effects of the course of the illness than other chronic diseases [6]. Moreover, studies showed that PRO was closely associated with the prognosis of CHF [7][8][9][10]. The 2022 American Heart Association guideline for management of HF recommended that standardized assessment of patientreported outcomes was able to provide incremental information for patients' prognosis [7].…”

Section: Introductionmentioning

confidence: 99%

Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge

Tian

Han

et al. 2023

Health Qual Life Outcomes

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Background Patient-reported outcomes (PROs) can be obtained outside hospitals and are of great significance for evaluation of patients with chronic heart failure (CHF). The aim of this study was to establish a prediction model using PROs for out-of-hospital patients. Methods CHF-PRO were collected in 941 patients with CHF from a prospective cohort. Primary endpoints were all-cause mortality, HF hospitalization, and major adverse cardiovascular events (MACEs). To establish prognosis models during the two years follow-up, six machine learning methods were used, including logistic regression, random forest classifier, extreme gradient boosting (XGBoost), light gradient boosting machine, naive bayes, and multilayer perceptron. Models were established in four steps, namely, using general information as predictors, using four domains of CHF-PRO, using both of them and adjusting the parameters. The discrimination and calibration were then estimated. Further analyze were performed for the best model. The top prediction variables were further assessed. The Shapley additive explanations (SHAP) method was used to explain black boxes of the models. Moreover, a self-made web-based risk calculator was established to facilitate the clinical application. Results CHF-PRO showed strong prediction value and improved the performance of the models. Among the approaches, XGBoost of the parameter adjustment model had the highest prediction performance with an area under the curve of 0.754 (95% CI: 0.737 to 0.761) for death, 0.718 (95% CI: 0.717 to 0.721) for HF rehospitalization and 0.670 (95% CI: 0.595 to 0.710) for MACEs. The four domains of CHF-PRO, especially the physical domain, showed the most significant impact on the prediction of outcomes. Conclusion CHF-PRO showed strong prediction value in the models. The XGBoost models using variables based on CHF-PRO and the patient’s general information provide prognostic assessment for patients with CHF. The self-made web-based risk calculator can be conveniently used to predict the prognosis for patients after discharge. Clinical Trial Registration URL: http://www.chictr.org.cn/index.aspx; Unique identifier: ChiCTR2100043337. Graphical abstract

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Cognitive performance is associated with worse prognosis in patients with heart failure with reduced ejection fraction

Cited by 11 publications

References 34 publications

Mapping cognitive function screening instruments for patients with heart failure: A scoping review

Mapping cognitive function screening instruments for patients with heart failure: A scoping review

Tablet-Based Assessment of Cognitive Function Among Heart Failure Patients

Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge

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