The Using Of Chinese Version Of The Functional Assessment Of Chronic Illness Therapy-fatigue In Patients Receiving Maintenance Hemodialysis And Analysis The Influencing Factors Of Fatigue

Objective 1. To evaluate fatigue assessment tool in Chinese patients receiving maintenance hemodialysis(MHD).2. To describe fatigue in MHD patients and determine indicators which influence fatigue problem.Methods 1. A cross-sectional study was conducted in Tianjin First Center Hospital between March 2013 and September 2013, 172 MHD patients were enrolled. Patients’ demographic information and disease characteristics were collected by auditing patients’ medical records and completing a package of several measures. Functional Assessment of Chronic Illness Therapy-Fatigue(FACIT- Fatigue) and Revised Piper Fatigue Scale(RPFS) to measure fatigue, Hospital Anxiety and Depression Scale(HADS) to measure patients’ mood status, and Pittsburgh Sleep Quality Index(PSQI) to measure patients’ sleep status. To determine the psychometric characteristics of FACIT- Fatigue included reliability, validity and the standard error of measurement(SEM). Reliability was established by calculating test-retest reliability(Intraclass Correlation Coefficient, ICC) and Cronbach’s alpha coefficient of this scale. The validity of Chinese version of the FACIT-Fatigue was presented by content validity and construct validity. The content validity Index(CVI) of this scale was evaluated by calculating Item-content validity Index(I-CVI) and Scale-content validity Index(S-CVI). Construct validity was tested by calculating the Spearman correlation coefficient between FACIT-Fatigue and RPFS, HADS, and PSQI. Aslo, SEM should be tested.2. On the basis of a double center cross-sectional survey, this part was conducted in the hemodialysis centers of Tianjin Frist and Third center hospitals from October 2013 to March 2014. Patients’ demographic information and disease characteristics were collected by auditing patients’ medical records and completing a package of several measures. FACIT-Fatigue to measure fatigue, HADS to measure patients’ mood status, PSQI to measure patients’ sleep status, and Family APGAR Index(PAGAR) and the medical outcomes study health status-Social Functioning subscale(SF-36, SF) to measure patients’ family and social functioning. Using univariate analyses to compare the fatigue in different groups and using the continuous variables(anxiety, depression, sleep disturbance, family and social functioning) to test the Spearman correlation coefficient. Exploring the independent factors by applying multiple linear regression with stepwise procedure.Results 1. Psychometric characteristics of FACIT- Fatigue172 MHD patients were recruited. The FACIT-Fatigue had excellent test-retest reliability(ICC =0.98) and internal consistency(Cronbach’s α = 0.92), the validity of this scale was supported by the values of CVI, S-CVI/UA and S-CVI/Ave at 0.67~1, 0.85 and 0.96, respectively. The SEM of the FACIT-Fatigue was 1.2. The significant correlations between the FACIT-Fatigue score and the RPFS(rs =-0.658), HADS(rs =-0.566), and PSQI(rs=-0.489) were supported by the FACIT-Fatigue with good construct validity(all P<0.01).2. MHD patients’ fatigue characteristics, univariate and multiple liner regression analysis345 MHD patients were recruited. The score of FACIT-Fatigue was 39(Interquartile Range, 31-44), with minimum 14 and maximum 52, which indicated that the hemodialysis patients’ fatigue was worse than that of the general population in the U.S. Univariate analysis revealed fatigue was significantly associated with age, patients’ employment status, family economic status, exercise time per day in the demographic information(all P<0.05); fatigue was significantly correlated with primary cause of kidney diseases, comorbidity, hemodialysis vascular access, Scr, Kt/V in the disease charecteristics(all P<0.05). In the bivariate correlation analysis, FACIT-Fatigue were associated with HADS, PSQI, PAGAR and SF(all P<0.001).When multiple linear regression analysis was performed in MHD patients, sleep problem(β= 0.296), social functioning(β=-0.255), anxiety(β= 0.258), age(β= 0.110), comorbidity(β= 0.099), exercise time per day(β=-0.112), Kt/V(β=-0.088) and Scr(β= 0.085) were significant predictors of fatigue which explained 52.8% of the variance.Conclusions The FACIT-Fatigue had acceptable validity and reliability for maintenance dialysis patients and can be used as a valid tool for the measurement of fatigue among these Chinese patients. Fatigue problem in MHD patients should be taken into consideration. Sleep problem, anxiety, social functioning, exercise time per day, age were the main influencing factors of fatigue. Therefore, nurses in clinical practice should be aware of the fatigue problem in hemodialysis patients by considering it as an indispensable part of the routine evaluation, especially in those with risk factors. It is desirable to reduce the complaint of fatigue and enhance the quality of life and survival rate for the MHD patients by further interventions.