ABSTRACT Objective: To examine the associations between individual chronic diseases and multidimensional frailty comprising physical, psychological, and social frailty. Methods: Dutch individuals (N = 47,768) age ≥ 65 years completed a general health questionnaire sent by the Public Health Services (response rate of 58.5 %), including data concerning self-reported chronic diseases, multidimensional frailty, and sociodemographic characteristics. Multidimensional frailty was assessed with the Tilburg Frailty Indicator (TFI). Total frailty and each frailty domain were regressed onto background characteristics and the six most prevalent chronic diseases: diabetes mellitus, cancer, hypertension, arthrosis, urinary incontinence, and severe back disorder. Multimorbidity was defined as the presence of combinations of these six diseases. Results: The six chronic diseases had medium and strong associations with total ((f2 = 0.122) and physical frailty (f2 = 0.170), respectively, and weak associations with psychological (f2 = 0.023) and social frailty (f2 = 0.008). The effects of the six diseases on the frailty variables differed strongly across diseases, with urinary incontinence and severe back disorder impairing frailty most. No synergetic effects were found; the effects of a disease on frailty did not get noteworthy stronger in the presence of another disease. Conclusions: Chronic diseases, in particular urinary incontinence and severe back disorder, were associated with frailty. We thus recommend assigning different weights to individual chronic diseases in a measure of multimorbidity that aims to examine effects of multimorbidity on multidimensional frailty. Because there were no synergetic effects of chronic diseases, the measure does not need to include interactions between diseases.
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Abstract: Background: Little is known about frailty among patients hospitalized with heart failure (HF). To date, the limited information on frailty in HF is based on a unidimensional view of frailty, in which only physical aspects are considered when determining frailty. The aims of this study were to study different dimensions of frailty (physical, psychological and social) in patients with HF and the effect of different dimensions of frailty on the incidence of heart failure. Methods: The study used a cross-sectional design and included 965 patients hospitalized for heart failure and 164 healthy controls. HF was defined according to the ESC guidelines. The Tilburg Frailty Indicator (TFI) was used to assess frailty. Probit regression analyses and chi-square statistics were used to examine associations between the occurrence of heart failure and TFI domains of frailty. Results: Patients diagnosed with frailty were 15.3% more likely to develop HF compared to those not diagnosed with frailty (p < 0.001). An increase in physical, psychological and social frailty corresponded to an increased risk of HF of 2.9% (p < 0.001), 4.4% (p < 0.001) and 6.6% (p < 0.001), respectively. Conclusions: We found evidence of the association between different dimensions of frailty and incidence of HF.
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Abstract Frailty syndrome (FS) is an independent predictor of mortality in cardiovascular disease and is found in 15-74% of patients with heart failure (HF). The syndrome has a complex, multidimensional aetiology and contributes to adverse outcomes. Proper FS diagnosis and treatment determine prognosis and support the evaluation of treatment outcomes. Routine FS assessment for HF patients should be included in daily clinical practice as an important prognostic factor within a holistic process of diagnosis and treatment. Multidisciplinary team members, particularly nurses, play an important role in FS assessment in hospital and primary care settings, and in the home care environment. Raising awareness of concurrent FS in patients with HF patients and promoting targeted interventions may contribute to a decreased risk of adverse events, and a better prognosis and quality of life.
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Alongside the growing number of older persons, the prevalence of chronic diseases is increasing, leading to higher pressure on health care services. eHealth is considered a solution for better and more efficient health care. However, not every patient is able to use eHealth, for several reasons. This study aims to provide an overview of: (1) sociodemographic factors that influence the use of eHealth; and (2) suggest directions for interventions that will improve the use of eHealth in patients with chronic disease. A structured literature review of PubMed, ScienceDirect, Association for Computing Machinery Digital Library (ACMDL), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was conducted using four sets of keywords: “chronic disease”, “eHealth”, “factors”, and “suggested interventions”. Qualitative, quantitative, and mixed-method studies were included. Four researchers each assessed quality and extracted data. Twenty-two out of 1639 articles were included. Higher age and lower income, lower education, living alone, and living in rural areas were found to be associated with lower eHealth use. Ethnicity revealed mixed outcomes. Suggested solutions were personalized support, social support, use of different types of Internet devices to deliver eHealth, and involvement of patients in the development of eHealth interventions. It is concluded that eHealth is least used by persons who need it most. Tailored delivery of eHealth is recommended
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Hospitals are encouraged to provide care closer to patients’ homes. This study investigates how patients, informal caregivers, and nurses experience home-based hospital-level care for decompensated heart failure. This mixed-methods study employed semi-structured interviews with 11 patients and 4 informal caregivers, a questionnaire administrated to 16 nurses from the intensive care, cardiac care, and general cardiology ward, and interviews with 4 nurses, supplemented by two group discussions. A convenience sample was utilized, member checks were performed, and two researchers analysed the patient interviews using thematic analysis based on the normalization process theory. Five overarching themes emerged: (i) Appreciation of personal environment, routines, and autonomy. (ii) Quality of care. (iii) Commitment to the treatment. (iv) Influence of personal characteristics. (v) Changing role of informal caregivers. Regarding nurse satisfaction, findings were mapped according to Proctor et al.’s implementation outcomes: acceptability: hospital-at-home care increases job satisfaction, through increased autonomy, personalized care, and patient satisfaction; appropriateness: hospital-at-home was perceived positively, although safety and adherence needed attention; adoption: hospital-at-home was not particularly challenging but offered a refreshing change; feasibility: on-call duty impacted personal commitments for some nurses; fidelity: information folders with clear protocols were deemed helpful. Patients, caregivers, and nurses generally favour home-based heart failure treatment over hospital-based treatment. Key conditions include comprehensive education on home treatment, adherence support like dietary restriction maintenance, prioritizing patient autonomy, recognizing caregiver burden, and exploring cost-effective strategies such as collaboration with home care organizations. Hoofdstuk in boek: https://www.techwijsinzorgenwelzijn.nl/
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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 nonphysical origins of HF.
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Abstract People over 65 years of age constitute over 80% of patients with heart failure (HF) and the incidence of HF is 10 per 1,000 in people aged above 65 years. Approximately 25% of older patients with HF exhibit evidence of frailty. Frail patients with cardiovascular disease (CVD) have a worse prognosis than non-frail patients, and frailty is an independent risk factor for incident HF among older people. Planning the treatment of individuals with HF and concomitant frailty, one should consider not only the limitations imposed by frailty syndrome (FS) but also those associated with the underlying heart disease. It needs to be emphasized that all patients with HF and concomitant FS require individualized treatment.
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The author reflects on the need for a comprehensive assessment of the structure and quality of the family or social network given that relationships are affected after the diagnosis of a cardiovascular disease. He points out that families may experience changing needs for support during the disease trajectory and emotional support may be necessary to cope with changing roles. He advocates for a family-oriented approach for patients with heart failure and their families.
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OBJECTIVE: To examine the willingness of grown-ups with congenital heart disease (GUCH) to participate in the GUCH Training Program-Individualised (GTI), an exercise program specifically designed for GUCH, and to identify factors affecting their willingness to participate.In this cross-sectional study, all outpatient GUCH of the University Medical Center Groningen in The Netherlands, living within a 30-km radius of Groningen (n = 311), were asked to participate.In total, 116 (37%) of the 311 GUCH who are invited to participate in our study returned completed questionnaires. The median age of the respondents was 40 (interquartile range 31-50) years and 55% were women.Respondents (n = 116) completed a questionnaire that queried physical activity, perceived physical fitness, psychosocial determinants (motivation, self-efficacy, and social support) related to physical activity, and willingness to participate in GTI.Of the 116 respondents, 68 (59%) were willing to participate in GTI. They were less physically active, had worse perceived physical fitness, were less satisfied with their fitness, were generally more motivated to engage in physical activity, and had more social support than patients unwilling to participate. The best logistic regression model predicting willingness to participate in GTI included the variables perceived physical fitness and motivation for physical activity in general.CONCLUSIONS: Asking GUCH to participate in an exercise program supervised by physical therapists is a good strategy. Taken into account nonresponse, a participation rate in the exercise program of over 20% is to be expected. Perceived physical fitness and motivation for physical activity in general are important predictors of patients' willingness to participate
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PURPOSE: To assess the association of clinical variables and the development of specified chronic conditions in ICU survivors.MATERIALS AND METHODS: A retrospective cohort study, combining a national health insurance claims database and a national quality registry for ICUs. Claims data from 2012 to 2014 were combined with clinical data of patients admitted to an ICU during 2013. To assess the association of clinical variables (ICU length of stay, mechanical ventilation, acute physiology score, reason for ICU admission, mean arterial pressure score and glucose score) and the development of chronic conditions (i.e. heart diseases, COPD or asthma, Diabetes mellitus type II, depression and kidney diseases), logistic regression was used.RESULTS: 49,004 ICU patients were included. ICU length of stay was associated with the development of heart diseases, asthma or COPD and depression. The reason for ICU admission was an important risk factor for the development of all chronic conditions with adjusted ORs ranging from 2.05 (CI 1.56; 2.69) for kidney diseases to 5.14 (CI 3.99; 6.62) for depression.CONCLUSIONS: Clinical variables, especially the reason for ICU admission, are associated with the development of chronic conditions after ICU discharge. Therefore, these clinical variables should be considered when organizing follow-up care for ICU survivors.
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