TY - JOUR
T1 - Exploring demands of hemodialysis patients in Taiwan
T2 - A two-step cluster analysis
AU - Yu, I. Chen
AU - Fang, Ji Tseng
AU - Tsai, Yun Fang
N1 - Publisher Copyright:
© 2020 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85079083445&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0228259
DO - 10.1371/journal.pone.0228259
M3 - 文章
C2 - 32032397
AN - SCOPUS:85079083445
SN - 1932-6203
VL - 15
JO - PLoS ONE
JF - PLoS ONE
IS - 2
M1 - e0228259
ER -