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Background: Uric acid is synthesized from xanthine via xanthine oxidase as an end-product of purine metabolism. Uric acid is a major non-enzymatic antioxidant in the blood, and it exerts a protective action on vitamin C. There are a limited number of ICU studies related to uric acid, which is a valuable prognostic biomarker. This study aimed to evaluate the utility of uric acid as a biomarker in predicting the outcomes of critically ill patients. Methods: This prospective, multi-centered cohort study included 128 patients from two different intensive care units who met the study inclusion criteria between May 2017 and October 2017. Study inclusion criteria were first admission to the ICU, age > 18 years, and ICU stay > 24 hours. In each patient, baseline serum uric acid levels were measured after acute interventions, prior to the initiation of the treatment process. Results: When comparing the last uric acid levels of patients, the median last uric acid levels in the non-survival and survival groups were significantly different (p = 0.001). A last uric acid level > 4.5 mg/dL was associated with a 2.638 times higher risk (relative risk) for mortality. According to ROC analysis, a cutoff value of 1.5 for the ratio between the last two uric acid levels had a sensitivity of 0.21 and a specificity of 0.96 for predicting mortality. A 1.5-fold increase in the uric acid level yielded a positive predictive value of 92.6% and a negative predictive value of 65.2% for predicting mortality. The median uric level in the patient subset with ARDS, was significantly higher than those without ARDS (p = 0.001). Conclusions: Results of this study indicate that a time-dependent increase in uric acid levels can be used as an important biomarker for predicting mortality in critically ill patients; further, uric acid levels should possibly be included in the current mortality risk scoring systems. In addition, elevation of uric acid, a simple, inexpensive, and readily available biomarker, may provide guidance in the diagnostic stage and in predicting clinical outcomes of patients with sepsis or ARDS. [ABSTRACT FROM AUTHOR]