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Kidney Week

Abstract: SA-PO112

Metabolomics Analysis and Classic Biomarkers to Predict Mortality in Patients with AKI and Kidney Replacement Therapy

Session Information

Category: Acute Kidney Injury

  • 102 AKI: Clinical, Outcomes, and Trials

Authors

  • Del Toro-Cisneros, Noemi, Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Ciudad de Mexico, Ciudad de México, Mexico
  • Martinez-Rojas, Miguel Angel, Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Ciudad de Mexico, Ciudad de México, Mexico
  • González Soria, Isaac, Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Ciudad de Mexico, Ciudad de México, Mexico
  • Ortega-Trejo, Juan Antonio, Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Ciudad de Mexico, Ciudad de México, Mexico
  • Bobadilla, Norma, Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Ciudad de Mexico, Ciudad de México, Mexico
  • Vega, Olynka, Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Ciudad de Mexico, Ciudad de México, Mexico
Background

Acute kidney injury (AKI) requiring kidney replacement therapy (KRT) is associated with mortality in critically ill patients. Serum metabolic biomarkers and markers of tubular damage might differentiate patients with a high risk of mortality.

Methods

Prospective cohort study of patients with critical COVID-19 in intensive care unit (ICU) with invasive mechanical ventilation (IMV) and who required KRT, admitted to Mar 2020 - Feb 2022. Patients with CKD stages 4 or 5 and kidney transplant were excluded. Urine SerpinA3, KIM-1, nGAL, HSP-72, and metabolomics analysis were measured on day 0 (start of KRT). Serum IL-6, IL-10, and TNF-alpha were also measured on day 0.

Results

Sixty patients were included, 52% died before discharge. The parameters measured at the beginning of the KRT were not different between living and dead patients (Fig 1). Of the urinary biomarkers studied, KIM-1 was the best mortality predictor (Fig.2a). The rest of the biomarkers had AUC minors (0.5-0.6) to predict this outcome. In the discriminant analysis of differential metabolites between the living (HD-A) and the dead (HD-D), p-cresol glucuronide was present in higher amounts in HD-D (Fig.2b).

Conclusion

In this study it was observed that KIM-1 was the best predictor of mortality. In the metabolomics analysis, p-cresol glucuronide was the metabolite present in highest amounts among the deceased patients.