An Evolutionary Concept Analysis in People With Heart Failure-Symptom Clusters or Symptom Cluster Profiles?

The concept of symptom clusters in heart failure (HF) has been defined and measured inconsistently. We used Rodgers' evolutionary method to review related concepts in the HF literature. Symptom clusters and symptom cluster profiles are characterized by multiple symptoms, a synergistic relationship, and result in a myriad of poor outcomes. Researchers should carefully consider the conceptual differences underpinning symptom clusters and symptom cluster profiles and choose the appropriate concept aligned with their research questions, empirical methods, and target HF population.

Cluster analysis of heart failure patients based on their psychological and physical symptoms and predictive analysis of cluster membership

AIM

Patients with heart failure experience multiple co-occurring symptoms that lower their quality of life and increase hospitalization and mortality rates. So far, no heart failure symptom cluster study recruited patients from community settings or focused on symptoms predicting most clinical outcomes. Considering physical and psychological symptoms together allows understanding how they burden patients in different combinations. Moreover, studies predicting symptom cluster membership using variables other than symptoms are lacking. We aimed to (a) cluster heart failure patients based on physical and psychological symptoms and (b) predict symptom cluster membership using sociodemographic/clinical variables.

DESIGN

Secondary analysis of MOTIVATE-HF trial, which recruited 510 heart failure patients from a hospital, an outpatient and a community setting in Italy.

METHODS

Cluster analysis was performed based on the two scores of the Hospital Anxiety-Depression scale and two scores of the Heart-Failure Somatic Perception Scale predicting most clinical outcomes. ANOVA and chi-square test were used to compare patients' characteristics among clusters. For the predictive analysis, we split the data into a training set and a test set and trained three classification models on the former to predict patients' symptom cluster membership based on 11 clinical/sociodemographic variables. Permutation analysis investigated which variables best predicted cluster membership.

RESULTS

Four clusters were identified based on the intensity and combination of psychological and physical symptoms: mixed distress (high psychological, low physical symptoms), high distress, low distress and moderate distress. Clinical and sociodemographic differences were found among clusters. NYHA-class (New York Heart Association) and sleep quality were the most important variables in predicting symptom cluster membership.

CONCLUSIONS

These results can support the development of tailored symptom management intervention and the investigation of symptom clusters' effect on patient outcomes. The promising results of the predictive analysis suggest that such benefits may be obtained even when direct access to symptoms-related data is absent.

IMPLICATIONS

These results may be particularly useful to clinicians, patients and researchers because they highlight the importance of addressing clusters of symptoms, instead of individual symptoms, to facilitate symptom detection and management. Knowing which variables best predict symptom cluster membership can allow to obtain such benefits even when direct access to symptoms-data is absent.

IMPACT

Four clusters of heart failure patients characterized by different intensity and combination of psychological and physical symptoms were identified. NYHA class and sleep quality appeared important variables in predicting symptom cluster membership.

REPORTING METHOD

The authors have adhered to the EQUATOR guidelines STROBE to report observational cross-sectional studies.

PATIENT OR PUBLIC CONTRIBUTION

Patients were included only for collecting their data.

Novel symptom clusters predict disease impact and healthcare utilisation in inflammatory bowel disease: Prospective longitudinal follow-up study

BACKGROUND

Predicting adverse disease outcomes and high-volume users of healthcare amongst patients with inflammatory bowel disease (IBD) is difficult.

AIMS

The aim of this study is to use latent class analysis to create novel clusters of patients and to assess whether these predict outcomes during 6.5 years of longitudinal follow-up.

METHODS

Baseline demographic features, disease activity indices, anxiety, depression, and somatoform symptom-reporting scores were recorded for 692 adults. Faecal calprotectin (FC) was analysed at baseline in 348 (50.3%) patients (<250 mcg/g defined biochemical remission). Using baseline gastrointestinal and psychological symptoms, latent class analysis identified specific patient clusters. Rates of glucocorticosteroid prescription or flare, escalation, hospitalisation, or intestinal resection were compared between clusters using multivariate Cox regression.

RESULTS

A three-cluster model was the optimum solution; 132 (19.1%) patients had below-average gastrointestinal and psychological symptoms (cluster 1), 352 (50.9%) had average levels of gastrointestinal and psychological symptoms (cluster 2), and 208 (30.1%) had the highest levels of both gastrointestinal and psychological symptoms (cluster 3). Compared with cluster 1, cluster 3 had significantly increased risk of flare or glucocorticosteroid prescription (hazard ratio (HR): 2.13; 95% confidence interval (CI): 1.46-3.10), escalation (HR: 1.92; 95% CI: 1.34-2.76), a composite of escalation, hospitalisation, or intestinal resection (HR: 2.05; 95% CI: 1.45-2.88), or any of the endpoints of interest (HR: 2.06; 95% CI: 1.45-2.93). Healthcare utilisation was highest in cluster 3.

CONCLUSIONS

Novel model-based clusters identify patients with IBD at higher risk of adverse disease outcomes who are high-volume users of healthcare.

Strategies in activating lymphatic system on symptom distress and health-related quality of life in patients with heart failure: secondary analysis of a pilot randomized controlled trial

Background

Abnormal interstitial fluid accumulation remains the major cause for patients with heart failure (HF) to endure a myriad of distressing symptoms and a decline in their health-related quality of life (HRQoL). The lymphatic system is essential in regulating fluid balance within the interstitial compartment and has recently been recognized as an important target for the prevention and mitigation of congestion. This study aimed to investigate the effects of exercises in activating lymphatic system on symptom distress and HRQoL among patients with HF.

Methods and results

This was a pre-determined, secondary analysis of the TOLF-HF [The-Optimal-Lymph-Flow for Heart Failure (TOLF-HF)] study, a two-arm pilot randomized controlled trial evaluating the preliminary effects of the lymphatic exercise intervention in enhancing interstitial decongestion among patients with HF. Participants were randomized to receive either a four-week TOLF-HF program in addition to standard care or standard care alone. The Chinese version of the Minnesota Living with Heart Failure Questionnaire (MLHFQ) was employed to measure symptom distress and HRQoL before and after the intervention. Data analyses included descriptive statistics, the independent sample t-test, Pearson's chi-square test, the Mann-Whitney U test, and covariance analysis. Of the 66 patients enrolled, 60 completed the study. The study results exhibited that the TOLF-HF intervention were effective in alleviating both physical and psychological symptom distress. The intervention group yielded significantly lower MLHFQ total scores in comparison to the control group. The odd ratio of achieving meaningful improvement in HRQoL in TOLF-HF group was 2.157 times higher than those in the control group.

Conclusions

The TOLF-HF program focusing on activating lymphatic system was effective in alleviating physical and psychological symptom distress as well as improving HRQoL for patients with HF. The tolerability, feasibility, and effectiveness of the TOLF-HF intervention make it a promising intervention for patients to manage HF.

Clinical Trial Registration

http://www.chictr.org.cn/index.aspx, identifier (ChiCTR2000039121).

Symptom Cluster Profiles Among Adults with Insomnia and Heart Failure

OBJECTIVE/BACKGROUND

Both heart failure (HF) and insomnia are associated with high symptom burden that may be manifested in clustered symptoms. To date, studies of insomnia have focused only on its association with single symptoms. The purposes of this study were to: (1) describe daytime symptom cluster profiles in adults with insomnia and chronic HF; and (2) determine the associations between demographic and clinical characteristics, insomnia and sleep characteristics and membership in symptom cluster profiles.

PARTICIPANTS

One hundred and ninety-five participants [M age 63.0 (SD12.8); 84 (43.1%) male; 148 (75.9%) New York Heart Association Class I/II] from the HeartSleep study (NCT0266038), a randomized controlled trial of the sustained effects of cognitive behavioral therapy for insomnia (CBT-I).

METHODS

We analyzed baseline data, including daytime symptoms (fatigue, pain, anxiety, depression, dyspnea, sleepiness) and insomnia (Insomnia Severity Index), and sleep characteristics (Pittsburgh Sleep Quality Index, wrist actigraphy). We conducted latent class analysis to identify symptom cluster profiles, bivariate associations, and multinomial regression.

RESULTS

We identified three daytime symptom cluster profiles, physical (N = 73 participants; 37.4%), emotional (N = 12; 5.6%), and all-high symptoms (N = 111; 56.4%). Body mass index, beta blockers, and insomnia severity were independently associated with membership in the all-high symptom profile, compared with the other symptom profile groups.

CONCLUSIONS

Higher symptom burden is associated with more severe insomnia in people with stable HF. There is a need to understand whether treatment of insomnia improves symptom burden as reflected in transition from symptom cluster profiles reflecting higher to lower symptom burden.

Using Latent Class Analysis to Identify Different Clinical Profiles Among Patients With Advanced Heart Failure

Context

Although palliative care is guideline-indicated for patients with advanced heart failure (HF), the scarcity of a specialty-trained palliative care workforce demands better identification of patients who are most burdened by the disease.

Objectives

We sought to identify latent subgroups with variations regarding symptom burden, functional status, and multimorbidity in an advanced HF population.

Methods

We performed a latent class analysis (LCA) of baseline data from a trial enrolling advanced HF patients. As LCA input variables, we chose indicators of HF severity, physical and psychological symptom burden, functional status, and the number of comorbidities.

Results

Among 563 patients, two subgroups emerged from LCA, Class A (352 [62.5%]) and Class B (211 [37.5%]). Patients in Class A were less often classified as NYHA class III or IV (88.0% vs. 97.5%, P < 0.001), as compared to Class B patients. Class A patients had fewer symptoms, fewer comorbidities, only 25.9% had impairments in activities of daily living (ADL), and virtually none suffered from clinically significant anxiety (0.4%) or depression (0.9%). In Class B, every patient reported more than three symptoms, almost all patients (92.6%) had some impairment in ADL, and nearly a third had anxiety (30.2%) or depression (28.3%). All-cause mortality after 12 months was higher in Class B, as compared to Class A (18.5% vs. 12.5%, P = 0.047).

Conclusion

Among advanced HF patients, we identified a distinct subgroup characterized by a conjunction of high symptom burden, anxiety, depression, multimorbidity, and functional status impairment, which might profit particularly from palliative care interventions. J Pain Symptom Manage 2022;000:1-9.

Moving beyond Table 1: A critical review of the literature addressing social determinants of health in chronic condition symptom cluster research

Variability in the symptom experience in patients diagnosed with chronic conditions may be related to social determinants of health (SDoH). The purpose of this critical review was to (1) summarize the existing literature on SDoH and symptom clusters (i.e., multiple, co-occurring symptoms) in patients diagnosed with common chronic conditions, (2) evaluate current variables and measures used to represent SDoH, (3) identify gaps in the evidence base, and (4) provide recommendations for the incorporation of SDoH into future symptom cluster research. We identified 118 articles including information on SDoH in chronic condition symptom cluster research. Articles primarily focused on cancer populations. Few articles had the explicit purpose of investigating relationships between SDoH and symptom clusters, and the inclusion of SDoH was often limited to variables used to describe samples. Future studies should be designed to "move beyond Table 1" in their utilization of SDoH as variables and examine relationships between SDoH and symptom clusters. Attention should be paid to the appropriateness of measures being used to collect information on SDoH, and analysis methods that estimate causal connections between variables should be considered. Research regarding the relationship of SDoH with symptom clusters in patients with chronic conditions has the potential to reveal mechanisms of symptom disparities and guide changes to alleviate these disparities.

Predictors of Unrelieved Symptoms in All of Us Research Program Participants With Chronic Conditions

CONTEXT

Over half of American adults are diagnosed with a chronic condition, with an increasing prevalence being diagnosed with multiple chronic conditions. These adults are at higher risk for having unrelieved, co-occurring symptoms, known as symptom clusters.

OBJECTIVES

To identify symptom phenotypes of patients diagnosed with four common chronic conditions, specifically, cancer, chronic obstructive pulmonary disease, heart failure, and/or type 2 diabetes mellitus, and to understand factors that predict membership in symptomatic phenotypes.

METHODS

We conducted a retrospective, cross-sectional analysis using participant responses (N=14,127) to All of Us Research Program, a National Institutes of Health biomedical database, survey questions. We performed hierarchical clustering to generate symptom phenotypes of fatigue, emotional distress, and pain and used multinomial regression to determine if demographic, healthcare access and utilization, and health-related variables predict symptom phenotype.

RESULTS

Four phenotypes, one asymptomatic or mildly symptomatic and three highly symptomatic (characterized by severe symptoms, severe pain, and severe emotional distress), were identified. The percentage of participants belonging to the severe symptoms phenotype increased with the number of chronic conditions. Most notably, foregoing or delaying medical care and rating mental health as poor or fair increased the odds of belonging to a highly symptomatic phenotype.

CONCLUSION

We found meaningful relationships between demographic, healthcare access and utilization, and health-related factors and symptom phenotypes. With the increasing trends of American adults with one or more chronic conditions and a demand to individualize care in the precision health era, it is critical to understand the factors that lead to unrelieved symptoms.

Obesity-Related High-Output Heart Failure: An Integrative Review

BACKGROUND

High-output heart failure (HF) is a type of HF characterized by signs and symptoms of HF and a cardiac output of 8 L/min or greater or a cardiac index greater than 3.9 L/min/m 2 . High-output HF occurs secondary to an underlying condition that requires high cardiac output due to an increase in oxygen consumption or decreased systemic vascular resistance. Obesity is a major cause of high-output HF, yet there is limited research on obesity-related high-output HF. Thus, the pathophysiologic mechanisms of this syndrome are not fully understood.

OBJECTIVE

The objectives of this integrative review were to describe the current state of the research regarding obesity-related high-output HF and to recommend direction for future research.

METHODS

We conducted an integrative review focusing on the peer-reviewed literature on patients with obesity-related high-output HF using Whittemore and Knafl's methodology. MEDLINE, CINAHL, and EMBASE electronic databases were searched for all publications indexed in the databases as of March 9, 2022. A narrative synthesis of definitions and symptoms, obesity as an underlying condition, pathophysiology, and treatments of obesity-related high-output HF was completed.

RESULTS

A total of 6 articles were included in the integrative review, with 1 nonexperimental, retrospective study and 5 literature reviews. Understanding of obesity-related high-output HF is very limited because of scant empirical evidence in the existing literature. Possible pathophysiologic mechanisms include increased pressure in the upper airways, adipokine dysregulation, increased metabolic activity, and insulin resistance.

CONCLUSION

Additional research is needed on the pathophysiologic mechanisms of obesity-related high-output HF to begin investigations on therapeutic interventions to improve health outcomes.

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

Background

Heart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice.

Methods

The review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed.

Results

Of the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures.

Conclusion

This scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.

Sleep Deficiency: A Symptoms Perspective: Exemplars from Chronic Heart Failure, Inflammatory Bowel Disease, and Breast Cancer

Sleep deficiency is associated with disabling daytime symptoms, including excessive daytime sleepiness (EDS) and fatigue. The purpose of this article is to discuss the contributions of sleep deficiency and sleep disorders to fatigue and EDS among people with chronic conditions. We use exemplars from the literature on chronic heart failure, inflammatory bowel disease, and breast cancer to (1) describe the prevalence of fatigue and EDS and their consequences; (2) examine the evidence for the contributions of sleep deficiency and sleep disorders to these symptoms; and (3) recommend implications for future research and practice.

Sleep Characteristics, Mood, Somatic Symptoms, and Self-Care Among People With Heart Failure and Insomnia

BACKGROUND

Almost 50% of people with heart failure (HF) experience chronic insomnia and must perform self-care to manage their day-to-day healthcare needs. Understanding multifactorial influences on self-care, including demographic, clinical, and sleep characteristics, and mood and somatic symptoms will help identify people at highest risk for poor self-care. However, past research focused only on the associations of single symptoms and self-care. Multivariate approaches are needed to account for the synergistic associations of self-care with sleep, mood, and somatic symptoms among people with HF.

OBJECTIVES

The aims of the study were to (a) evaluate the levels of self-care maintenance and self-care confidence among people with stable HF and chronic insomnia; (b) identify the clinical and demographic correlates of self-care maintenance and confidence among people with stable HF and chronic insomnia; and (c) identify the associations between sleep characteristics, mood and somatic symptoms, and self-care maintenance and confidence among people with stable HF and chronic insomnia.

METHODS

We utilized a cross-sectional design with 195 adult participants who had chronic HF and insomnia. We assessed for symptoms of anxiety; depression; dyspnea; fatigue; stress; insomnia severity; and sleep disturbance, impairment, and quality. Self-care was measured using the Self-Care for Heart Failure Index v6.2. We used generalized linear models to test the associations between the demographic and clinical factors and self-care maintenance and confidence; exploratory and confirmatory factor analysis to identify the factor structure underlying the symptoms; and structural equation modeling to test the combined associations of the demographic and clinical factors and latent factors with self-care maintenance and confidence.

RESULTS

Self-care maintenance, confidence, and management were inadequate in most participants. We identified three latent factors among the nine symptoms: "sleep characteristics," "mood," and "somatic symptoms." In the structural equation model, "sleep characteristics," White race, and having a left ventricular ejection fraction of <45 were associated with self-care maintenance. Age was negatively associated with self-care confidence.

DISCUSSION

Poor sleep characteristics negatively influence the ability of people with HF and insomnia to perform self-care behaviors. Knowledge of the associations among age, left ventricular ejection fraction, and race with self-care will help clinicians and future researchers identify those at risk for poor self-care.

Age and gender differences in physical heart failure symptom clusters

BACKGROUND

Gender differences exist in structure and function of the heart resulting in HF symptom variation. Previous HF symptom cluster research described symptom clusters that were linked to functional status, mortality, quality of life and rehospitalization. Age and gender differences between cluster groups were described in one study.

OBJECTIVES

Identify physical HF symptom clusters and explore age and gender differences between clusters.

METHODS

Secondary analysis study of adults with HF. Cluster analysis was conducted using hierarchical agglomerative clustering techniques. A pictorial dendrogram output displays clusters.

RESULTS

Three symptom clusters were identified in this sample of 133 older HF patients that differed by gender (p = 0.04), age (p = 0.00) and beta blocker use (p = 0.01). Symptom clusters were consistent with worsening HF, acute HF and chronic HF.

CONCLUSION

Symptom clusters differ by age and gender. Education should be directed at increasing patient awareness of their individual symptom clusters.

Quality of life of chronic heart failure patients: a systematic review and meta-analysis

Despite various individual studies on the quality of life (QOL) in patients with CHF, a comprehensive study has not yet been conducted; therefore, this study aims to assess the QOL of CHF patients. In the present systematic review and meta-analysis, PubMed, Scopus, and the Web of science databases were searched from January 1, 2000, to December 31, 2018, using QOL and heart failure as keywords. The searches, screenings, quality assessments, and data extractions were conducted separately by two researchers. A total of 70 studies including 25,180 participants entered the final stage. The mean QOL score was 44.1 (95% confidence interval (CI) 40.6, 47.5; I² = 99.3%) using a specific random effects method in 40 studies carried out on 12,520 patients. Moreover, according to the geographical region, heart failure patients in the Americas had higher scores. In 14 studies, in which a general SF-36 survey was implemented, the average physical component score (PCS) and mental component score (MCS) were 33.3 (95% CI 31.9, 34.7; I² = 88.0%) and 50.6 (95% CI 43.8, 57.4; I² = 99.3%), respectively. The general and specific tools used in this study indicated moderate and poor QOL, respectively. Therefore, it is necessary to carry out periodic QOL measurements using appropriate tools as part of the general care of CHF patients.

Uncertainty levels differ by physical heart failure symptom cluster

BACKGROUND

The role of uncertainty, unpredictable symptoms, and unknown illness trajectory are frequent concerns reported in heart failure (HF) literature. Illness uncertainty can lead to difficulty interpreting symptoms, potentially impacting outcomes. Impaired functional status, quality of life, all-cause mortality, rehospitalization, and event-free survival are predicted by symptom clusters. No studies to date describe levels of uncertainty by physical symptom cluster in HF.

AIMS

Describe physical HF symptom clusters and determine if uncertainty levels differ by symptom cluster.

METHODS

Results are based on a secondary analysis of data from patients hospitalized with an acute exacerbation of HF. The Heart Failure Somatic Perception Scale (HFSPS) and Mishel's Uncertainty in Illness Scale (MUIS-C) were completed. Symptom clusters were determined by hierarchical agglomerative clustering. Controlling for age and gender, ANCOVA (post hoc LSD) analyses explored uncertainty levels by symptom cluster group.

RESULTS

One hundred and thirty-three primarily older (76.4 ± 12.1), Caucasian (92.5%) adults (55.2% male), with an ischemic HF etiology (71.6%) were enrolled. Three clusters were found: 1. Shortness of breath, n = 47, 2. Edema, n = 39, and 3. Cardiac, n = 43. Adjusting for age and gender, uncertainty levels differed by cluster group (p ≤ 0.001), with edema cluster members reporting greater illness uncertainty than cardiac cluster members (74.6 vs 69.5, respectively, p = 0.033).

CONCLUSIONS

Differences exist in illness uncertainty levels based on the symptom experience of patients with HF. Care and management of HF symptoms should include a complete assessment of unique symptom cluster profiles.

Symptom Status Questionnaire - Heart Failure - Brazilian Version: cross-cultural adaptation and content validation

BACKGROUND

In Brazil, there are no instruments available to measure the presence, frequency, severity and distress related to heart failure (HF) symptoms.

AIMS

To adapt the Symptom Status Questionnaire - HF (SSQ-HF) into Brazilian Portuguese and to examine the content validity of the adapted version.

METHODS

The instrument was translated, back-translated and evaluated by an expert committee for semantic, idiomatic, cultural, and conceptual equivalences. An agreement ≥80% was considered adequate. The adapted version was evaluated by both an expert committee (n = 9) for clarity, theoretical relevance and practical relevance (acceptable content validity coefficient (CVC): ≥0.70), and by a patient committee regarding understanding (n = 40).

RESULTS

The adapted version obtained 100% agreement regarding the equivalences. The total instrument CVC was 0.99. All patients understood the items.

CONCLUSION

The SSQ-HF-Brazilian version has satisfactory evidence of equivalence and content validity. Additional psychometric tests are deemed to confirm that the instrument can be used in Brazil.

Comparison of Health-Related Quality of Life in Outpatients with Chagas and Matched Non-Chagas Chronic Heart Failure in Colombia: A Cross-Sectional Analysis

Chagas disease represents an important cause of heart failure (HF) and affects health-related quality of life (HRQoL). The study aimed to evaluate and compare the HRQoL of patients with chagasic HF and matched non-Chagas controls to identify factors associated with HRQoL. A cross-sectional study with pair-matched controls was conducted in Colombia. From October 2018 to December 2019, a total of 84 HF patients were screened for study subjects. Four were excluded, resulting in 80 patients for the analysis, among whom 40 patients with Chagas were enrolled as cases and 40 gender- and age-matched non-Chagas patients as controls. The Kansas City Cardiomyopathy Questionnaire (KCCQ) and the Minnesota Living with Heart Failure Questionnaire (MLWHFQ) were used to measure HRQoL. Demographic, clinical, and laboratory data were obtained from each subject. Health-related quality of life scores were significantly worse among the Chagas group than among the non-Chagas group in the KCCQ domains of physical functioning and symptoms and in the MLWHFQ scale. In the multivariate analysis, the variables associated with lower HRQoL scores were living alone, obesity, having less than 12 years of education, and an increase in left ventricular diameters in the systole and diastole. Health-related quality of life in patients with chronic HF is impaired across all domains. Chagas patients showed worse HRQoL scores than non-Chagas patients. Six variables, some potentially modifiable, were independently associated with worse HRQoL.

Identification of Self-Management Behavior Clusters Among People Living with HIV in China: A Latent Class Profile Analysis

BACKGROUND

Self-management directly affects the health outcomes and quality of life among people living with HIV (PLWH). A better understanding of self-management level will provide evidence for researchers to develop effective interventions.

PURPOSE

This study aims to identify the latent classes among PLWH in their levels of self-management behavior, and to explore the sociodemographic and disease-related predictors within these classes.

MATERIALS AND METHODS

A total of 868 PLWH were recruited from August 2017 to January 2019 in Sichuan Province, China. A latent class profile analysis was used to identify participants' self-management behavior, and multinomial logistic regression was used to explore the sociodemographic and disease-related predictors of the different latent classes.

RESULTS

Model fit indices supported a three-class model. The mean self-management scores in the three classes were 23.56 (SD=6.02), 37.91 (SD=3.80), and 47.95 (SD=4.18), respectively. The latent classes were Class 1 (a poor level of self-management behavior, 12.1%, n=104), Class 2 (a moderate level of self-management behavior, 56.1%, n=491) and Class 3 (a good level of self-management behavior, 31.7%, n=273). Antiretroviral trerapy (ART) status, infection route, and educational level were the main predictors of self-management behavior.

CONCLUSION

The findings indicated that the level of self-management behaviors among PLWH in China is inadequate. Those with a lower educational level, who were infected through blood/injecting drugs, and who were not receiving ART, showed a significantly lower level of self-management behavior. These results could help healthcare professionals to quickly recognize PLWH who are at a high risk of low-level self-management, using individual characteristics and could provide a scientific basis for the development of effective and targeted programs to improve self-management level in PLWH.

Palliative Care in Acute Heart Failure

Purpose of Review

Palliative care is increasingly acknowledged as beneficial in supporting patients and families affected by heart failure, but policy documents have generally focused on the chronic form of this disease. We examined palliative care provision for those with acute heart failure, based on the recently updated National Consensus Project Clinical Practice Guidelines for Quality Palliative Care.

Recent Findings

The commonest reason for hospitalization in those > 65 years, acute heart failure admissions delineate crisis points on the unpredictable disease trajectory. Palliative care is underutilized, often perceived as limited to end-of-life care rather than determined by regular systematic needs assessment. No dominant paradigm of palliative care provision has emerged from the nascent evidence base related to this clinical cohort, underscoring the need for further research.

Summary

Embedding palliative support as mainstream to heart failure care from the point of diagnosis may better ensure treatment strategies for those admitted with acute heart failure remain consistent with patients’ preferences and values.

[Using lived experiences and theoretical insights to gain a better understanding of the adaptation difficulties encountered by elderly patients with chronic heart failure during the hospital-to-home transition]

Heart failure is one of the most common reasons for hospitalization in older people, and the hospital-to-home transition can be unsuccessful for these patients. Existing care programs focus primarily on the physiological aspects of the disease and are rarely based on theory. Using Roy's adaptation model (1), the aim of this study was to develop a thorough understanding of the adaptation difficulties and factors that influence how well elderly patients with chronic heart failure cope with the hospital-to-home transition, in order to develop a nursing interventions program. Based on the process proposed by Sidani and Braden (2011), this qualitative descriptive study adopted a deductive approach, with the use of intermediary theories and empirical data, as well as an inductive approach, where older people with chronic heart failure (n=7), caregivers (n=6), and healthcare professionals (n=14) participated in semi-structured individual interviews. The triangulation of data highlights the difficulties and factors influencing adaptation at the physical, psychological, and social levels. Gaining a better understanding of the experience of older people with heart failure when it comes to their transition from hospital to home, and doing so with a holistic vision, provides information for interventions that can contribute to better management of chronic disease and a better quality of life for these elderly patients.

Identification of health behaviour clusters among people at high risk of stroke: A latent class profile analysis

AIMS

To identify the possible latent classes of health behaviour reported by people at high risk of stroke and to explore the predictors of these different classes of health behaviour.

DESIGN

A cross-sectional survey study.

METHODS

A stratified cluster random sampling method was used to collect data from 2,500 individuals at high risk of stroke who were from Henan Province, China, from January 2018-January 2019. A latent class profile analysis was used to identify the health behaviour clusters and multinomial logistic regression was used to determine which factors predicted the emergent latent classes of health behaviour.

RESULTS

High-risk individuals (N = 2,236) at high risk of stroke replied to the survey (89.44% response rate). Model fit indices (AIC = 257,509.610, BIC = 260,228.733, Entropy = 0.956) supported a three-class model of health behaviours. The latent classes were Class 1 (a good level of adaptive health behaviour, 31%, N = 693), Class 2 (a moderate level of adaptive health behaviour, 36%, N = 805) and Class 3 (a poor level of adaptive health behaviour, 33%, N = 738); Based on physical and belief, behaviour and clinical profiles, the three classes were further labelled self-realization deficiency subgroup, social contact anxiety subgroup and health responsibility absence subgroup respectively. Older age, male gender, no spouse, lower education and household income were risk factors associated with good health behaviour. After controlling these socio-demographic variables, high levels of health-related knowledge and attitude were the main positive predictors of health behaviour.

CONCLUSIONS

This study has identified three different latent classes of health behaviour and their predictive factors in people at high risk of stroke in the Chinese setting.

IMPACT

This study has significance for the promotion of adaptive health behaviour in individuals at high risk of stroke. It has allowed the identification of specific clusters of health behaviour that vary in terms of their adaptiveness and forms the basis for the development of a targeted intervention to promote health behaviour for each different subgroup.

Symptom clusters and quality of life among patients with chronic heart failure: A cross-sectional study

AIM

This study aimed to identify symptom clusters among patients with chronic heart failure (HF) and examine their independent relationships with quality of life (QoL).

METHODS

A descriptive cross-sectional design was adopted, and 201 Chinese participants were recruited. Their symptom profiles and QoL were assessed using the Memorial Symptom Assessment Scale-Heart Failure and Minnesota Living with Heart Failure Questionnaire. Exploratory factor analysis was used to identify the symptom clusters. Pearson's correlation analysis and multiple regression analysis were conducted to examine their independent relationships with QoL.

RESULTS

Six distinct symptom clusters were identified: the fatigue, dyspneic, discomfort, congestive, ischemic, and emotional symptom clusters. These six symptom clusters accounted for 57.508% of the variance in patient symptom experiences and were positively related to their overall QoL. Moreover, the fatigue (β = .317, p < .001), dyspneic (β = .228, p < .001), congestive (β = .363, p < .001), and emotional (β = .200, p < .001) symptom clusters independently predicted QoL.

CONCLUSION

The six symptom clusters that were identified in this study and the relationships that they shared with QoL are expected to inform future approaches to symptom management. Interventions that target these symptom clusters will improve the QoL of patients with HF.

Perceived Control Predicts Symptom Status in Patients With Heart Failure

BACKGROUND

Symptom status is an important indicator of disease progression in patients with heart failure (HF). Perceived control is a target of most self-care interventions and is associated with better outcomes in HF; however, little is known about the relationship between perceived control and symptom status in patients with HF.

OBJECTIVE

The aims of this study were to (1) determine the relationship of perceived control to HF symptom status and (2) examine the associations of perceived control to self-care and of self-care to symptom status.

METHODS

A total of 115 patients with HF were included. Data on symptom status (Memorial Symptom Assessment Scale-HF), perceived control (Control Attitudes Scale-Revised), and self-care (Self-Care of Heart Failure Index) were collected. Other covariates included were age, gender, New York Heart Association class, comorbidity burden, and depressive symptoms. Multiple regression analyses were performed to analyze the data.

RESULTS

Lower perceived control predicted worse symptom status after controlling for covariates (P = .009). Other covariates predictive of worse symptom status were younger age, New York Heart Association class III/IV, and higher levels of depressive symptoms. Higher levels of perceived control were associated with better self-care (P = .044). Better self-care was associated with better symptom status (P = .038).

CONCLUSIONS

Lower levels of perceived control were independently associated with worse symptom status in patients with HF. Intervention strategies targeting perceived control should be tested to determine whether they could improve symptom status.

Multisymptom Profile Predicts Increased Risk of Poor Outcomes After Initial Placement of Implantable Cardioverter Defibrillator

CONTEXT

Patients with implantable cardioverter defibrillators (ICDs) are at risk for multiple physical and psychological symptoms. Identification of specific symptom profiles associated with poor outcomes may elucidate novel strategies to enhance symptom management.

OBJECTIVES

The objectives were to determine common symptoms after initial ICD implantation, identify classes of individuals with similar symptom profiles, describe patient characteristics associated with different symptom profiles, and determine if symptom profiles at hospital discharge predicted outcomes three and 12 months after implantation.

METHODS

This was a secondary data analysis of a randomized controlled trial that compared patient + partner versus patient-only interventions designed to help patients manage symptoms, prepare for ICD shocks, and resume daily activities. Symptoms were measured with the Patient Concerns Assessment. Latent class regression analysis was used to identify symptom classes at baseline, three-month, and 12-month follow-up. Associations between patient characteristics, class membership, and outcomes were examined using chi-square, analysis of variance, and Poisson regression.

RESULTS

The study included 301 patients (74% male, mean age 64 ± 11.9 years). Three classes were identified: Multi-Symptom (N = 119, 40%), Tired-Rundown (N = 130, 43%), and Mostly Asymptomatic (N = 52, 17%). Patients in the Multi-Symptom class were younger (59.9 years, P < 0.001) and reported more anxiety (P < 0.001) and depression (P < 0.01) than the other classes. Membership in the Multi-Symptom class predicted lower quality of life and resulted in nearly double the rate of hospitalizations after 12 months (P = 0.02, IRR 1.9).

CONCLUSION

Evaluation of symptom profiles after ICD implantation offers a promising strategy for identifying patients at risk for poor health outcomes.

Influence of depression and gender on symptom burden among patients with advanced heart failure: Insight from the pain assessment, incidence and nature in heart failure study

INTRODUCTION

Patients with advanced heart failure (HF) experience many burdensome symptoms that increase patient suffering.

METHODS

Comparative secondary analysis of 347 patients with advanced HF. Symptom burden was measured with the Memorial Symptom Assessment Scale-HF. Depression was measured using the Patient Health Questionnaire-9.

RESULTS

Mean number of symptoms was 13.6. The three most frequent symptoms were non-cardiac pain, shortness of breath, and lack of energy. Patients with depression reported higher symptom burden. Symptom burden differed when compared by gender. Women reported higher symptom burden for other pain, dry mouth, swelling of the arms and legs, sweats, feeling nervous, nausea, and vomiting. Men reported higher symptom burden with sexual problems.

CONCLUSIONS

Given the high rates of symptoms and distress, interventions are needed to alleviate the symptom burden of patients with advanced HF. Reported symptom burden in patients with advanced heart failure was higher when depressive symptoms were present. Women reported varied number and severity of symptoms than men.