Towards a symptom cluster model in chronic kidney disease: A structural equation approach.

Abstract

AIMS The aim of this study was to test a symptom cluster model in chronic kidney disease patients based on the Theory of Unpleasant Symptoms, accounting for the relationships between influencing factors, symptom experience and consequences for quality of life. BACKGROUND The evaluation of symptom clusters is a new field of scientific inquiry directed towards more focused symptom management. Yet, little is known about relationships between symptom clusters, predictors and the synergistic effect of multiple symptoms on outcomes. DESIGN Cross-sectional. METHODS Data were collected from 436 patients with advanced stages of chronic kidney disease during July 2013-February 2014 using validated measures of symptom burden and quality of life. Analysis involved structural equation modelling. RESULTS The final model demonstrated good fit with the data and provided strong evidence for the predicted relationships. Psychological distress, stage of chronic kidney disease and age explained most of the variance in symptom experience. Symptom clusters had a strong negative effect on quality of life, with fatigue, sexual symptoms and restless legs being the strongest predictors. Overall, the model explained more than half of the deterioration in quality of life. However, a reciprocal path between quality of life and symptom experience was not found. CONCLUSIONS Interventions targeting symptom clusters could greatly improve quality of life in patients with chronic kidney disease. The symptom cluster model presented has important clinical and heuristic implications, serving as a framework to encourage and guide new lines of intervention research to reduce symptom burden in chronic kidney disease.

DOI: 10.1111/jan.13303

Cite this paper

@article{Almutary2017TowardsAS, title={Towards a symptom cluster model in chronic kidney disease: A structural equation approach.}, author={Hayfa Almutary and Clint Douglas and Ann Bonner}, journal={Journal of advanced nursing}, year={2017}, volume={73 10}, pages={2450-2461} }