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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., (© 2023 The Authors. Journal of Advanced Nursing published by John Wiley & Sons Ltd.)

Aims: The aim of the study was to develop and psychometrically test the Nurse-Patient Mutuality in Chronic Illness (NPM-CI) scale in two versions, one for nurses and one for patients., Method: A multiphase methodological study was conducted. In the first phase, a qualitative investigation was conducted through interviews and an analysis of the content; inductively, the items of two instruments were generated, one for nurses and one for patients. In the second phase, the content and face validity were assessed through the expert consensus method. In the third phase, exploratory factor analysis (EFA), Cronbach's alpha test, intraclass correlation and Pearson correlation coefficients were conducted to estimate construct, criterion validity and instrument reliability. For each phase, the sample included nurses and patients recruited from a large hospital in Northern Italy. Data collection was conducted between June and September 2021., Results: Nurse and patient versions of the NPM-CI scale were developed. Two rounds of consensus reduced the items from 39 to 20; content validity index ranged between 0.78 and 1, content validity ratio was 0.94. Face validity indicated clarity and comprehensibility of the items. EFA identified three latent factors for both the scales. Internal consistency was satisfactory, with Cronbach's alphas ranging between .80 and .90. Test-retest stability was suggested, with an intraclass correlation coefficient of .96 (nurse scale) and .97 (patient scale). Predictive validity was established, with a Pearson correlation coefficient of .43 (nurse scale) and 0.55 (patient scale) between the mutuality scales and satisfaction in providing and receiving care., Conclusion: The results suggest that the NPM-CI scales are sufficiently valid and reliable for the clinical practice among chronic illness patients and the nurses caring for them. A more in-depth exploration of this construct in the context of nursing and patient outcomes is warranted., Patient or Public Contribution: Patients were involved in all study phases., Impact: Mutuality is fundamental in the relationship between nurse and patient, based on trust, equality, reciprocity, and mutual respect. The NPM-CI scale was developed and psychometric estimated through a multiphase study in both nurse and patient versions. The NPM-CI scale measures the factors of 'developing and going beyond', 'being the point of reference' and 'deciding and sharing care'. The NPM-CI scale allows us to measure mutuality in clinical practice and research. Expected outcomes and influencing factors for patients and nurses could be associated., (© 2023 John Wiley & Sons Ltd.)