Value of Kidney Disease Improving Global Outcomes Urine Output Criteria in Critically Ill Patients: A Secondary Analysis of a Multicenter Prospective Cohort Study

BACKGROUND: Urine output (UO) is an essential criterion of the Kidney Disease Improving Global Outcomes (KDIGO) definition and classification system for acute kidney injury (AKI), of which the diagnostic value has not been extensively studied. We aimed to determine whether AKI based on KDIGO UO criteria (KDIGO(UO)) could improve the diagnostic and prognostic accuracy, compared with KDIGO serum creatinine criteria (KDIGO(SCr)). METHODS: We conducted a secondary analysis of the database of a previous study conducted by China Critical Care Clinical Trial Group (CCCCTG), which was a 2-month prospective cohort study (July 1, 2009 to August 31, 2009) involving 3063 patients in 22 tertiary Intensive Care Units in Mainland of China. AKI was diagnosed and classified separately based on KDIGO(UO) and KDIGO(SCr). Hospital mortality of patients with more severe AKI classification based on KDIGO(UO) was compared with other patients by univariate and multivariate regression analyses. RESULTS: The prevalence of AKI increased from 52.4% based on KDIGO(SCr) to 55.4% based on KDIGO(SCr) combined with KDIGO(UO). KDIGO(UO) also resulted in an upgrade of AKI classification in 7.3% of patients, representing those with more severe AKI classification based on KDIGO(UO). Compared with non-AKI patients or those with maximum AKI classification by KDIGO(SCr), those with maximum AKI classification by KDIGO(UO) had a significantly higher hospital mortality of 58.4% (odds ratio [OR]: 7.580, 95% confidence interval [CI]: 4.141–13.873, P < 0.001). In a multivariate logistic regression analysis, AKI based on KDIGO(UO) (OR: 2.891, 95% CI: 1.964–4.254, P < 0.001), but not based on KDIGO(SCr) (OR: 1.322, 95% CI: 0.902–1.939, P = 0.152), was an independent risk factor for hospital mortality. CONCLUSION: UO was a criterion with additional value beyond creatinine criterion for AKI diagnosis and classification, which can help identify a group of patients with high risk of death.