A cluster analysis of psychological and physical symptoms in patients with heart failure
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): CECRI (Center of Excellence for Nursing Scholarship) Rome Background The experience of psychological and physical symptoms of heart failure can be burdensome for patients. Identification of profiles of symptom burden may lead to greater understanding of the mechanism underlying this experience and improve symptom-centered care. Purpose The aim of this analysis was to identify profiles of psychological and physical symptoms in heart failure patients and compare the sociodemographic and clinical characteristics of patients in each cluster. Methods This was a secondary analysis of baseline data from the MOTIVATE-HF trial, that enrolled 510 patients across Italy. Cluster analysis was used to identify profiles of patients based on the two scores of the Hospital Anxiety and Depression scale and the total score of the Heart Failure Somatic Perception Scale. Each profile was described in terms of sociodemographic characteristics (e.g., age, gender, severity of disease), self-care maintenance and management behaviors, and self-efficacy (Self-care of Heart Failure Index), generic quality of life (12-item Short Form Survey), health-related quality of life (Kansas City Cardiomyopathy Questionnaire), and cognitive status (Montreal Cognitive Assessment Scale). Comparisons between the characteristics of each profile were performed by analysis of variance (ANOVA) and chi square test. Results Patients (n = 510) were 72.37 years old on average (SD = 12.28), with a slightly higher prevalence of men (58%). Sixty per cent of the sample were symptomatic: New York Heart Association (NYHA) class 2, 31.4% in NYHA class III, and 6.5% in NYHA class IV. Three profiles were identified: (1) low distress, characterized by the youngest patients (69.76, SD = 12.17), mostly in NYHA class II, and with the highest score on self-care self-efficacy and self-care maintenance behaviors; (2) average distress, characterized by the oldest patients (74.45, SD = 11.99), who were mostly retired, with the highest level of education, and poorest (physical) health-related quality of life and low self-care behaviors; (3) high distress, characterized by an average age of 73.08 (SD = 12.29), with the lowest hemoglobin level, the worst cognitive status, and the worst generic and health-related quality of life. Conclusions The profiles identified in this study and their characteristics provide new insights into the burden of psychological and physical symptoms experienced by heart failure patients. This represents the first step in promoting symptom-centered care in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".