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Factors influencing the quality of life of haemodialysis patients according to symptom cluster
Author(s) -
Shim Hye Yeung,
Cho MiKyoung
Publication year - 2018
Publication title -
journal of clinical nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 102
eISSN - 1365-2702
pISSN - 0962-1067
DOI - 10.1111/jocn.13904
Subject(s) - quality of life (healthcare) , medicine , latent class model , polytomous rasch model , cluster (spacecraft) , psychological intervention , physical therapy , logistic regression , spouse , descriptive statistics , hemodialysis , clinical psychology , psychometrics , psychiatry , item response theory , nursing , statistics , mathematics , sociology , computer science , anthropology , programming language
Aims and objectives To identify the characteristics in each symptom cluster and factors influencing the quality of life of haemodialysis patients in Korea according to cluster. Background Despite developments in renal replacement therapy, haemodialysis still restricts the activities of daily living due to pain and impairs physical functioning induced by the disease and its complications. Design Descriptive survey. Two hundred and thirty dialysis patients aged >18 years. They completed self‐administered questionnaires of Dialysis Symptom Index and Kidney Disease Quality of Life instrument‐Short Form 1.3. Methods To determine the optimal number of clusters, the collected data were analysed using polytomous variable latent class analysis in R software (po LCA ) to estimate the latent class models and the latent class regression models for polytomous outcome variables. Differences in characteristics, symptoms and QOL according to the symptom cluster of haemodialysis patients were analysed using the independent t test and chi‐square test. The factors influencing the QOL according to symptom cluster were identified using hierarchical multiple regression analysis. Results Physical and emotional symptoms were significantly more severe, and the QOL was significantly worse in Cluster 1 than in Cluster 2. The factors influencing the QOL were spouse, job, insurance type and physical and emotional symptoms in Cluster 1, with these variables having an explanatory power of 60.9%. Physical and emotional symptoms were the only influencing factors in Cluster 2, and they had an explanatory power of 37.4%. Conclusions Mitigating the symptoms experienced by haemodialysis patients and improving their QOL require educational and therapeutic symptom management interventions that are tailored according to the characteristics and symptoms in each cluster. Relevance to clinical practice The findings of this study are expected to lead to practical guidelines for addressing the symptoms experienced by haemodialysis patients, and they provide basic information for developing nursing interventions to manage these symptoms and improve the QOL of these patients.