Symptom Clusters and Their Impacts on Hospital Readmission in Patients With Heart Failure: A Cross-Sectional Study

Signs and Symptoms Clusters Among Patients With Acute Heart Failure: A Correlational Study

BACKGROUND

Patients with acute heart failure present to the emergency department with a myriad of signs and symptoms. Symptoms evaluated in clusters may be more meaningful than those evaluated individually by clinicians.

OBJECTIVE

Among emergency department patients, we aimed to identify signs and symptoms correlations, clusters, and differences in clinical variables between clusters.

METHODS

Medical record data included adults older than 18 years, International Classification of Diseases, Tenth Revisions codes , and positive Framingham Heart Failure Diagnostic Criteria. Exclusion criteria included medical records with a ventricular assist device and dialysis. For analysis, correlation, and the Gower distance, the independent t test, Mann-Whitney U test, χ 2 test, and regression were performed.

RESULTS

A secondary analysis was conducted from the data set to evaluate door-to-diuretic time among patients with acute heart failure in the emergency department. A total of 218 patients were included, with an average age of 69 ± 15 years and predominantly White (74%, n = 161). Two distinct symptom clusters were identified: severe and mild congestion. The severe congestion cluster had a more comorbidity burden compared with the mild congestion cluster, as measured by the Charlson Comorbidity index (cluster 1 vs cluster 2, 6 [5-7] vs 5 [4-6]; P = .0019). Heart failure with preserved ejection fraction was associated with the severe congestion symptom cluster ( P = .009), and heart failure with mildly reduced ejection fraction was associated with the mild congestion cluster ( P = .019).

CONCLUSIONS

In conclusion, 2 distinct symptom clusters were identified among patients with acute heart failure. Symptom clusters may be related to ejection fraction or overall cardiac output and comorbidity burden.

Unsupervised machine learning identifies symptoms of indigestion as a predictor of acute decompensation and adverse cardiac events in patients with heart failure presenting to the emergency department

BACKGROUND

Patients with known heart failure (HF) present to emergency departments (ED) with a plethora of symptoms. Although symptom clusters have been suggested as prognostic features, accurately triaging HF patients is a longstanding challenge.

OBJECTIVES

We sought to use machine learning to identify subtle phenotypes of patient symptoms and evaluate their diagnostic and prognostic value among HF patients seeking emergency care.

METHODS

This was a secondary analysis of a prospective cohort study of consecutive patients seen in the ED for chest pain or equivalent symptoms. Independent reviewers extracted clinical data from charts, including nine categories of subjective symptoms reported during initial evaluation. The diagnostic outcome was acute HF exacerbation and prognostic outcome was 30-day major adverse cardiac events (MACE). Outcomes were adjudicated by two independent reviewers. K-means clustering was used to derive latent patient symptom clusters, and their associations with outcomes were assessed using multivariate logistic regression.

RESULTS

Sample included 438 patients (age 65±14 years; 45% female, 49% Black, 18% HF exacerbation, 32% MACE). K-means clustering identified three presentation phenotypes: patients with dyspnea only (Cluster A, 40%); patients with indigestion, with or without dyspnea (Cluster B, 23%); patients with neither dyspnea nor indigestion (Cluster C, 37%). Compared to Cluster C, indigestion was a significant predictor of acute HF exacerbation (OR=1.8, 95%CI=1.0-3.4) and 30-day MACE (OR=1.8, 95%CI=1.0-3.1), independent of age, sex, race, and other comorbidities.

CONCLUSION

Indigestion symptoms in patients with known HF signify excess risk of adverse events, suggesting that these patients should be triaged as high-risk during initial ED evaluation.

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.

The prognostic impact of symptom clusters in patients with heart failure: A systematic review and meta-analysis

AIM

To determine the impact of symptom clusters on clinical outcomes among heart failure patients.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Peer-reviewed articles were searched from 12 English and Chinese language databases from inception to August 2021.

REVIEW METHODS

Narrative syntheses were first conducted to integrate symptom clusters reported in the identified studies. This was followed by meta-analysis to synthesize the evidence on the association or predictive effects of symptom clusters on clinical outcomes.

RESULTS

Twelve studies were identified. Among studies which identified highly correlated symptoms as in a cluster, meta-analysis indicated that severe congestive (r = .45, 95% CI = 0.38-0.52), weary (r = .41, 95% CI = 0.33-0.50), ischaemic (r = .29, 95% CI = 0.04-0.51) and stress-related (r = .62, 95% CI = 0.31-0.81) symptom clusters were correlated with a poorer health-related quality of life. As for studies used latent class to identified patient cohorts of similar symptom pattern, high symptom cohorts (hazard ratio = 1.86, 95% CI = 1.39-2.48) and incongruent physical and psycho-cognitive symptom cohorts was associated with a significantly higher risk (hazard ratio = 2.10, 95% CI = 1.44-3.07) of combined event rate relative to low symptom presentation.

CONCLUSIONS

This review has identified the impact of symptom clusters on clinical outcomes in heart failure patients. In addition to the classical physical symptoms highlighted in the clinical management guidelines, our findings suggested the important predictive role of psycho-cognitive and weary symptoms in determining the clinical outcomes of HF patients.

IMPACT

This review concluded the promising prospect of symptom clusters in shaping clinical outcomes of heart failure. The findings highlighted the importance of integrating care to minimize the disease impact on psycho-cognitive function and weary symptoms among this clinical cohort. The review also inform the direction on how to advance the knowledge on symptom clusters among this clinical cohort.

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.

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 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.

Gender Differences in the Impact of Cognitive Function on Health Literacy among Older Adults with Heart Failure

Heart failure (HF)-related cognitive decline is a common condition and may be associated with health literacy. However, gender differences in this context have not been explored fully. This secondary data analysis aimed to identify gender differences in the impact of cognitive function on health literacy among older patients with HF. A total of 135 patients (75 men and 60 women) with a mean age of 73.01 ± 6.45 years were recruited. Older women with HF had higher cognitive impairment (15%) and inadequate health literacy (56.7%) compared to men. Cognitive function was the strongest predictor of health literacy in men (β = 3.668, p < 0.001) and women (β = 2.926, p = 0.004). Notably elderly women are likely to face double the burden of the influence of cognitive function on health literacy in comparison with men. It is necessary to assess cognitive function and health literacy during HF illness trajectories on a regular basis. Healthcare professionals working with patients with HF should be aware of gender differences in cognitive function and health literacy and the importance of assessing these factors.