Exploring demands of hemodialysis patients in Taiwan: A two-step cluster analysis

AIMS AND OBJECTIVES: To classify hemodialysis patients into subgroups via cluster analysis according to the Somatic Symptoms Disturbance Index, Taiwanese Depression Scale, and Herth Hope Index scores. Patient demands in each cluster were also examined. BACKGROUND: Overall patient demands among hemodialysis patients have been demonstrated in numerous reports; however, variables among subgroups have not been explored. METHODS: Data were analyzed from a cross-sectional survey of 114 hemodialysis patients recruited from dialysis centers in Northern Taiwan. Hope, depression, and symptom disturbance were used as parameters for clustering because they have been shown to be important factors affecting patient demands. A two-step cluster analysis was performed to classify participants into clusters. Patient demands in each cluster were analyzed. RESULTS: Among the 114 participants, there was a negative correlation between hope and depression as well as between hope and symptom disturbance; there was a positive correlation between depression and symptom disturbance. Two clusters were identified: Cluster 1 (n = 49) included patients with moderate levels of hope and symptom disturbance, and high levels of depression; and Cluster 2 (n = 65) included patients with low levels of depression and symptom disturbance and high levels of hope. Demographic profiles differed between the two clusters. Regarding patient demands, medical demand showed the highest average score; whereas, occupational demand exhibited the lowest average score. Psychological and occupational demands differed significantly between the two clusters. The two clusters were defined as subgroups: Cluster 1 was labeled “resting”; Cluster 2 was labeled “active”. CONCLUSIONS: Cluster analysis may further classify hemodialysis patients into distinct subgroups base on their specific patient demands. A better understanding of patient demands may help health professionals to provide a holistic individualized treatment to improve patients’ outcomes.