Age and gender differences in physical heart failure symptom clusters

Psychometric Analysis of the Heart Failure Somatic Perception Scale in Japanese Patients With Heart Failure

BACKGROUND

Patients with heart failure (HF) experience a wide variety of symptoms. Appropriate recognition of symptoms is important in HF care. The Heart Failure Somatic Perception Scale (HFSPS) measures the presence of HF symptoms and the degree to which physical symptoms are bothersome.

OBJECTIVE

The aim of this study was to assess the validity and reliability of the Japanese version of the HFSPS.

METHODS

Confirmatory factor analysis was used to assess structural validity. Construct validity was assessed using Spearman's rank correlation coefficient to evaluate the association between HFSPS total and subscale scores and global physical health on the Patient-Reported Outcomes Measurement Information System. Internal consistency was assessed using the model-based internal consistency for the HFSPS as a whole and Cronbach α for the subscales.

RESULTS

Participants were 315 Japanese outpatients (72.1% male), with a mean age of 72.9 ± 12.9 years. The result of confirmatory factor analysis was an adequate model fit by adding error correlations. Construct validity was significant for the correlation with global physical health of the Patient-Reported Outcomes Measurement Information System. The model-based internal consistency was 0.95. Cronbach αs for each subscale were 0.88 for dyspnea, 0.60 for chest discomfort, 0.77 for early and subtle symptoms, and 0.77 for edema.

CONCLUSIONS

The findings support the use of the HFSPS in a more diverse population, suggesting that it is a reliable and valid instrument in Japanese patients with HF. The HFSPS may provide an accurate assessment of the symptoms experienced by patients with HF in daily life in future educational intervention studies to improve symptom perception and coping behaviors.

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.

Association Between Nurse-Led Multidisciplinary Education and Cardiac Events in Patients With Heart Failure: A Retrospective Chart Review

PURPOSE

This study examined the modifiable factors, including nurse-led multidisciplinary education and in/out-of-hospital rehabilitation, to predict cardiac events in patients with heart failure (HF) in South Korea.

METHODS

A retrospective review of the medical records was conducted using data of patients admitted for HF between June 2021 and April 2022. A total of 342 patients were included in this study. Information related to HF education, cardiac rehabilitation, and demographic and clinical characteristics were collected. Cardiac events, including emergency department visits, readmissions, and deaths, were defined as a composite of events. After adjusting for covariates, a multivariate Cox proportional hazard regression model was used to explore the association between modifiable factors and cardiac events in patients with HF.

RESULTS

During the follow-up period (median, 823 days), 123 patients (36.0%) experienced at least one cardiac event. In the Cox regression model, patients who received nurse-led multidisciplinary HF education during hospitalization were less likely to experience cardiac events (hazard ratio: 0.487; 95% confidence interval [CI]:0.239-0.993). Additionally, high NT-pro BNP levels were associated with an increased risk of cardiac events.

CONCLUSIONS

The education led by nurses on HF was a factor that reduced adverse prognoses in patients with HF. Our results highlight the importance of a nurse-led multidisciplinary approach during hospitalization.

Multiple myeloma patients undergoing chemotherapy: Which symptom clusters impact quality of life?

AIMS AND OBJECTIVES

To identify symptom clusters and examine their association with health-related quality of life.

BACKGROUND

Multiple myeloma patients undergoing chemotherapy suffer from disease symptoms and adverse effects during the course of the disease. However, single symptom management has little effect, and symptom management for these patients remains challenging. Symptom clusters open a new perspective and provide important clues for symptom management.

DESIGN

A cross-sectional study.

METHOD

Participants were invited to complete the Chinese version of the Memorial Symptom Assessment Scale and Quality of Life Questionnaire-core 30. Appropriate indicators were used for descriptive statistics. Principal component analysis was used to identify symptom clusters. Associations between symptom clusters and quality of life were examined with Pearson correlation coefficients, Pearson correlation matrix and multiple linear regression. This study was reported following the STROBE checklist.

RESULTS

A total of 177 participants were recruited from seven hospitals in this study. We identified self-image disorder, psychological, gastrointestinal, neurological, somatic and pain symptom clusters in multiple myeloma patients with chemotherapy. Approximately 97.65% of patients suffer from multiple symptom clusters. The pain, psychological and gastrointestinal symptom clusters have negatively influence on health-related quality of life. The strongest association was found with the pain symptom cluster.

CONCLUSION

Most of multiple myeloma patients suffer from multiple symptom clusters. When improving the multiple myeloma patients' health-related quality of life, the clinical staff should prioritise relieving the pain symptom cluster.

RELEVANCE TO CLINICAL PRACTICE

When multiple myeloma patients undergoing chemotherapy suffer from multiple symptom clusters, nurses should prioritise relieving the pain symptom cluster to improve their health-related quality of life. When drawing up and providing interventions, nurses should focus on the correlation among symptoms rather than single symptom. By relieving one symptom in a given cluster, other symptoms within the same symptom cluster may also be relieved.

Sex Differences in Acute Heart Failure Management: Is There a Gap in Treatment Quality?

PURPOSE OF REVIEW

Differences between men and women in demographics and clinical phenotype of heart failure have previously been described, as well as disparities in management and outcomes. This review summarizes the latest evidence concerning sex-related differences in acute heart failure and its most severe form, cardiogenic shock.

RECENT FINDINGS

Data from the last 5 years reaffirm the previous observations, with women with acute heart failure being older, more often having preserved ejection fraction and less frequently having an ischemic cause of the acute decompensation. Despite women still receive less invasive procedures and a less optimized medical treatment, the most recent studies find similar outcomes regardless of sex. These disparities persist in the context of cardiogenic shock, where women receive less mechanical circulatory support devices even if they present with more severe forms. This review reveals a different clinical picture of women with acute heart failure and cardiogenic shock compared to men, which translates into disparities in management. More female representation in studies would be needed in order to better understand the physiopathological basis of these differences and minimize inequalities in treatment and outcomes.

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.

Distinct implications of body mass index in different subgroups of nonobese patients with heart failure with preserved ejection fraction: a latent class analysis of data from the TOPCAT trial

BACKGROUND

Obesity is a well-defined risk factor for heart failure with preserved ejection fraction (HFpEF), but it is associated with a better prognosis in patients with diagnosed HFpEF. The paradoxically poor prognosis in nonobese patients with HFpEF may be driven by a subset of high-risk patients, which suggests that the nonobese HFpEF subpopulation is heterogeneous.

METHODS

Latent class analysis (LCA) was adopted to identify the potential subgroups of 623 nonobese patients enrolled in the TOPCAT trial. The baseline characteristics of the identified nonobese subgroups were compared with each other and with the obese patients. The risks of all-cause, cardiovascular, and noncardiovascular mortality, and an HF composite outcome were also compared.

RESULTS

Two subgroups of nonobese patients with HFpEF (the physiological non-obesity and the pathological non-obesity) were identified. The obese patients were younger than both nonobese subgroups. The clinical profile of patients with pathological non-obesity was poorer than that of patients with physiological non-obesity. They had more comorbidities, more severe HF, poorer quality of life, and lower levels of physical activity. Patients with pathological non-obesity showed low serum hemoglobin and albumin levels. After 2 years of follow-up, more patients in the pathological group lost ≥ 10% of body weight compared with those in the physiological group (11.34% vs. 4.19%, P = 0.009). The prognostic implications of the two subgroups were opposite. Compared to patients with obesity, patients with physiological non-obesity had a 47% decrease in the risk of HF composite outcome (hazard ratio [HR] 0.53, 95% confidence interval [CI] 0.40-0.70, P<0.001) and a trend of decreased all-cause mortality risk (HR 0.75, 95% CI 0.55-1.01, P=0.06), while patients with pathological non-obesity had a 59% increase (HR 1.59, 95% CI 1.24-2.02, P<0.001) in all-cause mortality risk.

CONCLUSIONS

Two subgroups of nonobese patients with HFpEF with distinct clinical profiles and prognostic implications were identified. The low BMI was likely physiological in one group but pathological in the other group. Using a data-driven approach, our study provided an alternative explanation for the "obesity paradox" that the poor prognosis of nonobese patients with HFpEF was driven by a pathological subgroup.

The Situation-Specific Theory of Heart Failure Self-care: An Update on the Problem, Person, and Environmental Factors Influencing Heart Failure Self-care

Many studies of heart failure (HF) self-care have been conducted since the last update of the situation-specific theory of HF self-care.

OBJECTIVE

The aim of this study was to describe the manner in which characteristics of the problem, person, and environment interact to influence decisions about self-care made by adults with chronic HF.

METHODS

This study is a theoretical update. Literature on the influence of the problem, person, and environment on HF self-care is summarized.

RESULTS

Consistent with naturalistic decision making, the interaction of the problem, person, and environment creates a situation in which a self-care decision is needed. Problem factors influencing decisions about HF self-care include specific conditions such as cognitive impairment, diabetes mellitus, sleep disorders, depression, and symptoms. Comorbid conditions make HF self-care difficult for a variety of reasons. Person factors influencing HF self-care include age, knowledge, skill, health literacy, attitudes, perceived control, values, social norms, cultural beliefs, habits, motivation, activation, self-efficacy, and coping. Environmental factors include weather, crime, violence, access to the Internet, the built environment, social support, and public policy.

CONCLUSIONS

A robust body of knowledge has accumulated on the person-related factors influencing HF self-care. More research on the contribution of problem-related factors to HF self-care is needed because very few people have only HF and no other chronic conditions. The research on environment-related factors is particularly sparse. Seven new propositions are included in this update. We strongly encourage investigators to consider the interactions of problem, person, and environmental factors affecting self-care decisions in future studies.

State of the Science: The Relevance of Symptoms in Cardiovascular Disease and Research: A Scientific Statement From the American Heart Association

Symptoms of cardiovascular disease drive health care use and are a major contributor to quality of life. Symptoms are of fundamental significance not only to the diagnosis of cardiovascular disease and appraisal of response to medical therapy but also directly to patients' daily lives. The primary purpose of this scientific statement is to present the state of the science and relevance of symptoms associated with cardiovascular disease. Symptoms as patient-reported outcomes are reviewed in terms of the genesis, manifestation, and similarities or differences between diagnoses. Specifically, symptoms associated with acute coronary syndrome, heart failure, valvular disorders, stroke, rhythm disorders, and peripheral vascular disease are reviewed. Secondary aims include (1) describing symptom measurement methods in research and application in clinical practice and (2) describing the importance of cardiovascular disease symptoms in terms of clinical events and other patient-reported outcomes as applicable.

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.