ESTONIAN ACADEMY
PUBLISHERS
eesti teaduste
akadeemia kirjastus
PUBLISHED
SINCE 1952
 
Proceeding cover
proceedings
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2020): 1.045

Estimation of dialysis patients’ survival through combined approach of small molecule uremic markers; pp. 315–321

Full article in PDF format | doi: 10.3176/proc.2014.3.04

Authors
Jana Holmar, Ivo Fridolin, Fredrik Uhlin, Anders Fernström, Merike Luman

Abstract

Survival rate of dialysis patients is still alarmingly low and various factors may have in it an important role. The purpose of this study was to observe the relationship between the survival of dialysis patients and the serum level of urea, creatinine, and uric acid (UA). Serum urea and creatinine concentrations may express patient’s nutritional status and muscle mass, and high UA value may refer to higher risk for cardiovascular events. The idea of combining the concentrations and removal of urea and UA into a single model for predicting the patient’s outcome is introduced. The study included 33 hemodialysis patients from Linköping, Sweden and 10 from Tallinn, Estonia. Kaplan–Meier analysis was used for survival analysis. Logistic and Cox regression analysis was applied to create models for predicting patients’ three-year survival. It was observed that higher serum UA is significantly related to poor survival in dialysis patients (p = 0.026). A reverse effect was observed in case of urea (p = 0.095). The level of creatinine was not related to survival (p = 0.905). The best logistic regression model for predicting patients’ outcome included both UA and urea based parameters (Chi Square 21.0, p = 0.0001). Survival of dialysis patients seems to be determined by a set of causal factors and combined models may have a predictive relevance. A possibility for automatic online monitoring of small molecule uremic markers is proposed. Since the number of participating patients was small, larger studies including more patients and testing the models in independent validation cohort is the future goal.


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