4.6 Article

A Multibiomarker-Based Model for Estimating the Risk of Septic Acute Kidney Injury

Journal

CRITICAL CARE MEDICINE
Volume 43, Issue 8, Pages 1646-1653

Publisher

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/CCM.0000000000001079

Keywords

biomarkers; decision tree; inflammation; kidney injury; modeling; sepsis

Funding

  1. Institutional Clinical and Translational Science Award, National Institutes of Health (NIH)
  2. National Center for Research Resources [8UL1 TR000077]
  3. NIH [RO1GM064619, RO1GM099773, R01GM108025]
  4. Cincinnati Children's Hospital Medical Center
  5. U.S. Army Medical Research and Material Command [W81XWH-BAA-11-1]
  6. Children's Hospital of Cincinnati
  7. NICHD [K12HD047349]
  8. Hackensack University Medical Center

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Objective: The development of acute kidney injury in patients with sepsis is associated with worse outcomes. Identifying those at risk for septic acute kidney injury could help to inform clinical decision making. We derived and tested a multibiomarker-based model to estimate the risk of septic acute kidney injury in children with septic shock. Design: Candidate serum protein septic acute kidney injury biomarkers were identified from previous transcriptomic studies. Model derivation involved measuring these biomarkers in serum samples from 241 subjects with septic shock obtained during the first 24 hours of admission and then using a Classification and Regression Tree approach to estimate the probability of septic acute kidney injury 3 days after the onset of septic shock, defined as at least two-fold increase from baseline serum creatinine. The model was then tested in a separate cohort of 200 subjects. Setting: Multiple PICUs in the United States. Interventions: None other than standard care. Measurements and Main Results: The decision tree included a first-level decision node based on day 1 septic acute kidney injury status and five subsequent biomarker-based decision nodes. The area under the curve for the tree was 0.95 (CI 95, 0.91-0.99), with a sensitivity of 93% and a specificity of 88%. The tree was superior to day 1 septic acute kidney injury status alone for estimating day 3 septic acute kidney injury risk. In the test cohort, the tree had an area under the curve of 0.83 (0.72-0.95), with a sensitivity of 85% and a specificity of 77% and was also superior to day 1 septic acute kidney injury status alone for estimating day 3 septic acute kidney injury risk. Conclusions: We have derived and tested a model to estimate the risk of septic acute kidney injury on day 3 of septic shock using a novel panel of biomarkers. The model had very good performance in a test cohort and has test characteristics supporting clinical utility and further prospective evaluation.

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