#3049 Differences on fatigue characterization in two distinct populations of chronic hemodialysis patients
Authors: Santos Carla , Lucas Carlos , Pearce Suzanne , Silva Eliana , Martínez-Vaquera Shaira , Mursalova Zhanar , Macário Fernando 
Published: May 2024

Background and Aims

Chronic fatigue remains, despite all the technological advances, one of the most important symptoms in prevalent hemodialysis (HD) subjects and has a strong impact on mortality and health-related quality of life. The objective of our work was to characterize fatigue in our patients, as a starting point for the development, in the near future, of an oriented and standardized approach.

Method

This study was part of a pilot evaluation, performed in two distinct countries of a large HD provider: Spain (Sp) and Kazakhstan (Kz). We designed a prospective and observational study, involving all eligible patients from one clinic in each country. Fatigue was evaluated in July 2023 by the validated “Standardized Outcomes in Nephrology (SONG) survey”. SONG survey comprises 3 questions (each scored from 0 to 3), complemented by a fourth one “how long do you take to recover after a dialysis session”, with 0 score attributed to “&lt;2 h”, 1 to “2 to six hours”, 2 to “6 to 12 h” and 3 to “&gt;12 h.” According to this, the final score ranged from 0 to 12. Demographic information (age and gender), clinical data (diabetes status, comorbidity index (CI), and time on dialysis) and analytical data (hemoglobin concentration, phosphorus, albumin and eKt/V) were also collected. Patients were grouped according to the final score: Group A, no or mild fatigue (0-4), Group B, moderate fatigue (5-8), and Group C, severe fatigue (9-12) and compared with T-test, z-test, ANOVA, Kruskal-Wallis test and multivariate linear regression. Results were presented as mean ± standard deviation or proportions, as appropriate. A p-value below 0.05 was considered statistically significant.

Results

In this study, 267 HD patients were included, 105 from Kazakhstan and 162 from Spain. According to fatigue severity, 42.7% were in the mild group, 28.1% in moderate and 14.6% in severe; 14.6% had no fatigue. The Sp cohort was older (mean age 66.39 ± 12.93 vs. 50.23 ± 11.16, p = 2.2e-16) and with a lower proportion of females (32.1% vs. 42.5%, p = 7.522e-06). Kz patients had a lower median CI (3 [0-14] vs. 4 [0-13], p &lt; 0.05), but a similar proportion of diabetic patients. Despite this, the mean SONG score was significantly higher in the Kz group (4.491 ± 2.348 vs. 4.469 ± 3.969, p = 2.2e-16). In subgroup analysis, patients reporting more severe fatigue had a higher proportion of diabetes, but no other differences were observed in the remaining demographic and clinical characteristics (Table 1). Interestingly, severity of fatigue score distribution was different between the two groups, with a higher proportion of patients in Sp presenting no fatigue (19.1% vs. 7.6%, p &lt; 0.001) or severe fatigue (21.0% vs. 4.8%, p &lt; 0.001), when compared to Kz patients (Table 2). In the Kz population, but not in the Sp cohort, diabetes was and independent predictor of the presence of fatigue (1.53 [0.01-3.05], p = 0.048).

Conclusion

Fatigue was frequent in the two distinct populations studied, with a global prevalence of 85%. Despite this, fatigue severity distribution was different between the two cohorts, which may be associated to the different characteristics of the populations evaluated. These results highlight the pressing need to specifically characterize fatigue in different populations, before the design and implementation of oriented programs.