Background: Patients with hemodialysis commonly experience multiple symptoms that occur together form symptom clusters. Symptom clusters experienced by patients with hemodialysis affects health-related quality of life (HRQOL). Identifying symptom clusters among patients with hemodialysis could help health worker to develop effective interventions from clusters level to increase HRQOL.
Aims: The aims of this study were to identify variable predictors for HRQOL, to investigate symptom burden, to identify HRQOL, to identify symptom clusters using comprehensive measurement tools, and to identify the relationship between demographic data and symptom clusters with HRQOL.
Methods: This study was a cross-sectional correlational study. A convenience sample of 320 patients undergoing hemodialysis were recruited from the dialysis units in two referral hospitals in Indonesia (Fatmawati General Hospital and Cipto Mangunkusumo National Hospital) from July 2019 to January 2020. Questionnaires were used to examine the following variables: the Indonesian version Chronic Kidney Disease – Symptom Burden Index (CKD-SBI), and Kidney Disease Quality of Life – 36 (KDQOL-36). Physical component summary (PCS) and mental component summary (MCS) of KDQOL-36 were used to represent HRQOL. All data were analyzed using SPSS 17 and R statistical software. Data analysis involved descriptive statistics to identify demographic status, symptoms burden, and HRQOL. Exploratory factor analysis was used to identify symptom clusters, bivariate analysis (Pearson correlation, independent t-test, and ANOVA) were used to identify the relationship between demographic data and symptom clusters with HRQOL, and hierarchical multiple linear regression was used to determine variable predictors for HRQOL.
Results: Total 320 subjects were collected. The subjects were mainly male (54.7%), age (mean = 51.50, SD = 14.56), average duration of hemodialysis (mean = 46.28, SD = 43.76), married (81.9%), normal BMI (18.5 - 24.9 kg/m2) (66.9%), graduated from senior high school (53.4%), not work (60%), and live with 1 to 2 comorbidities (44.3%).This study found that lack of energy was the highest symptom burden under 4 dimensions: occurrence 269 (84.0%),severity (mean = 4.28, SD = 3.08), distress (mean = 4.42, SD = 3.09), and frequency (mean = 4.41, SD = 3.27). Among patients with hemodialysis, the physical component summary (PCS) has the lowest score (mean = 29.08, SD = 22.31). After analyzed the data by using exploratory factor analysis, three symptom clusters were consistent along dimensions involve fluid volume symptoms, sexual symptoms, and psychological symptoms. Furthermore, hierarchical multiple linear regression reported that the final model of symptom clusters explained 24% of the variance in PCS. Psychological symptoms (beta = -0.20, p= .001), fluid volume symptoms (beta = -0.22, p= .001), and sexual symptoms (beta = -0.15, p = .01) were contributed as predictors of PCS. The final model of symptom clusters explained 38% of the variance in mental component summary (MCS). Psychological symptoms (beta = -0.24, p = .001), fluid volume symptoms (beta = -0.44, p= .001), and sexual symptoms (beta = -0.12, p = .02) were significant predictors of MCS.
Conclusion: Lack of energy was consistently the highest symptom burden among patients with hemodialysis. PCS subscale was lowest score of KDQOL-36. This study found 5 symptom clusters as follows: fluid volume symptoms, neuromuscular or pain symptoms, sexual symptoms, sleep symptoms, and psychological symptoms. However, only three symptom clusters were consistent along dimensions involve fluid volume symptoms, sexual symptoms, and psychological symptoms. Age, fluid volume symptoms, psychological symptoms, and sexual symptoms found as the best predictor of PCS. Fluid volume symptoms, psychological symptoms, and sexual symptoms found as the best predictor of MCS. Create appropriate intervention and manage the symptoms experienced by patients with hemodialysis in the cluster level is very important to increase their quality of life.

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