Symptom Cluster Profiles Among Adults with Insomnia and Heart Failure

Conley, Samantha; Jeon, Sangchoon; Breazeale, Stephen; O’Connell, Meghan; Hollenbeak, Christopher S.; Jacoby, Daniel; Linsky, Sarah; Yaggi, H. Klar; Redeker, Nancy S.

doi:10.1080/15402002.2022.2060226

Cited by 11 publications

(26 citation statements)

References 48 publications

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“…One study found that people in the allhigh symptom cluster profile, compared with those in physical and emotional symptom cluster profiles, had higher odds of having more severe insomnia. 29 Three studies reported that cultural factors might influence symptom clusters in HF and may be an antecedent. 21,22,28 Beliefs and norms within a culture play a role in making decisions about which phenomena should be considered as symptoms of an illness.…”

Section: Antecedentsmentioning

confidence: 99%

“…Most included studies examined at least 2 symptoms, yet only a few authors included 2 symptoms within a symptom cluster. 20,29,30 No author described clusters composed of only 1 symptom in a cluster.…”

Section: Multiple Symptomsmentioning

confidence: 99%

“…7,22,24,25 Six studies used person-oriented statistical approaches to identifying subgroups of people who shared multiple similar symptoms to determine symptom cluster profiles. 6,7,[28][29][30][31] Latent class analysis (LCA) is a categorical person-oriented method often used by researchers to determine symptom cluster profiles in people with HF. In addition, LCA is a data-driven approach, which indicates that a priori hypotheses are not required.…”

Section: Multiple Symptomsmentioning

confidence: 99%

“…6,[28][29][30][31] One recent study used LCA to identify 3 daytime symptom cluster profiles, in which multiple symptoms appeared in 3 symptom clusters simultaneously, such as anxiety and depression occurring in emotional and all-high symptom clusters concurrently. 29 Other person-oriented studies used latent profile analysis, a finite mixture model, to identify related subgroups (latent classes) with similar responses to symptoms. 28,31 Psychical and psychological symptoms were frequently observed in different symptom cluster profiles.…”

Section: Synergistic Relationshipmentioning

confidence: 99%

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

Wang¹,

Conley²,

Redeker³

et al. 2024

Advances in Nursing Science

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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.

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Section: Antecedentsmentioning

confidence: 99%

Section: Multiple Symptomsmentioning

confidence: 99%

Section: Multiple Symptomsmentioning

confidence: 99%

Section: Synergistic Relationshipmentioning

confidence: 99%

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

Wang¹,

Conley²,

Redeker³

et al. 2024

Advances in Nursing Science

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“…For instance, higher psychological distress is associated with lower quality of life and younger age (Lee et al, 2010;Zambroski et al, 2005); higher physical distress is associated with NYHA class III-IV and female gender (Lee et al, 2010). Other authors also found symptom severity to be associated with other variables such as mutuality levels (Zhou et al, 2022) and sleep quality (Conley et al, 2023;He & Pan, 2022). Specific symptom data may not always be collected, contrary to other sociodemographic or basic clinical information such as NYHA-class.…”

Section: Introductionmentioning

confidence: 99%

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

Locatelli,

Iovino,

Pasta

et al. 2023

Journal of Advanced Nursing

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AimPatients 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.DesignSecondary analysis of MOTIVATE‐HF trial, which recruited 510 heart failure patients from a hospital, an outpatient and a community setting in Italy.MethodsCluster 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.ResultsFour 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.ConclusionsThese 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.ImplicationsThese 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.ImpactFour 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 MethodThe authors have adhered to the EQUATOR guidelines STROBE to report observational cross‐sectional studies.Patient or Public ContributionPatients were included only for collecting their data.

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Sleep Difficulties in Palliative Care Patients

Howell

2023

Textbook of Palliative Care

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Symptom Cluster Profiles Among Adults with Insomnia and Heart Failure

Cited by 11 publications

References 48 publications

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

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

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

Sleep Difficulties in Palliative Care Patients

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