Corrine Jurgens

Aims To determine if distinct trajectories of coronary heart disease (CHD) self-care behaviours could be identified, linked to differences in quality of life (QoL), and predicted based on baseline characteristics. Methods and results A secondary analysis of a prospective, longitudinal, observational study. Patients with CHD answered questionnaires at study enrolment and six months later: Self-Care of Coronary Heart Disease Inventory (three subscales: maintenance, management and confidence, scored 0-100, higher score = better self-care), Hospital Anxiety and Depression Scale, 12-Item Short Form Survey, 16-Item European Health Literacy Survey Questionnaire, CHD Education Questionnaire. Latent growth mixture modelling was used to identify distinct self-care trajectories over time. On average, patients (n = 430, mean age 64.3 ± 8.9, 79% male) reported inadequate self-care (maintenance 61.6 ± 15.4, management 53.5 ± 18.5) at enrolment. Two distinct trajectories of self-care behaviours were identified: First, an “inadequate-and-worsening” (IN-WORSE) trajectory (57.2%), characterised by inadequate self-care, improvement in maintenance (4.0 ± 14.5-point improvement, p < 0.001) and worsening of management over time (6.3 ± 24.4-point worsening, p = 0.005). Second, an “inadequate-but-maintaining” (IN-MAIN) trajectory (42.8%), characterised by inadequate self-care, improvement in maintenance (5.0 ± 16.2-point improvement, p < 0.001) and stability in management over time (0.8 ± 21.9-point worsening, p = 0.713). In comparison, patients in the IN-WORSE trajectory had less favourable characteristics (including lower health literacy, knowledge, confidence) and significantly lower QoL. Not attending rehabilitation (OR 2.175; CI 1.020-4.637, p = 0.044) and older age (OR 0.959; CI 0.924-0.994, p = 0.024) predicted (IN-WORSE) trajectory inclusion. Conclusion Two self-care trajectories were identified, both suboptimal. Rehabilitation predicted membership in the more favorable trajectory and some positive characteristics were identified among patients in that group. Therefore, interventions supporting these factors may benefit patients’ self-care and QoL.

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.