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

ABSTRACT: 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.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.Multiple PICUs in the United States.None other than standard care.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 (CI95, 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.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.

SUBMITTER: Wong HR

PROVIDER: S-EPMC4667777 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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

Wong Hector R HR Cvijanovich Natalie Z NZ Anas Nick N Allen Geoffrey L GL Thomas Neal J NJ Bigham Michael T MT Weiss Scott L SL Fitzgerald Julie J Checchia Paul A PA Meyer Keith K Shanley Thomas P TP Quasney Michael M Hall Mark M Gedeit Rainer R Freishtat Robert J RJ Nowak Jeffrey J Raj Shekhar S SS Gertz Shira S Dawson Emily E Howard Kelli K Harmon Kelli K Lahni Patrick P Frank Erin E Hart Kimberly W KW Lindsell Christopher J CJ

Critical care medicine 20150801 8

<h4>Objective</h4>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.<h4>Design</h4>Candidate serum protein septic acute kidney injury biomarkers were identified from previous transcriptomic studies. Model derivation ...[more]

PMID: 25962083

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Project description:BackgroundSeptic acute kidney injury (S-AKI) results from an imbalance in the regulation of systemic inflammatory responses. Glycosylation plays an important role in inflammatory responses. However, the relationship between S-AKI and glycosylation is unclear.MethodsThe datasets of the public platform were analyzed using R language to obtain glycosylation-related differentially expressed genes (GRDEGs) in S-AKI. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed for GRDEGs. Hub genes were obtained using three machine learning algorithms and their diagnostic values were evaluated using receiver operating characteristic (ROC) curves. The relationships between the hub genes, immune cells, and signaling pathways were analyzed, and the upstream miRNAs, transcription factors, and compounds of the hub genes were predicted. Mouse models of AKI with sepsis were constructed and the expression of the hub genes was verified.ResultsWe obtained 45 GRDEGs that were mainly enriched in glycoprotein metabolism and immune inflammatory response, such as "O-glycan biosynthesis", "phagosome", "pathogenic Escherichia coli infection", "glycosyltransferase activity", etc. Seven hub genes that have potential diagnostic value were identified and were associated with the regulation of immune cells. Through gene set enrichment analysis (GSEA) of hub genes, it was found that these genes may be involved in metabolism, signaling transduction, and inflammation-related signaling pathways, such as "metabolism of amino and derivatives", "RHO GTPase cycle", "transport of small molecules", "neutrophil degranulation", "immune system", etc. We then predicted 100 miRNAs, 60 TFs, and 23 compounds of the hub genes using forecasting tools. Finally, animal experiments confirmed the differential expression of ASGR1, UMOD, SPTBN1, and ADAMTS17.ConclusionThis study identified and validated four biomarkers associated with abnormal glycosylation that could be potential targets for AKI in sepsis.

Project description:BackgroundThe relationship between cardiac output and septic acute kidney injury (AKI) remains unclear. The purpose of this study was to assess the association between the cardiac index (CI) and the renal outcomes in patients with septic shock.MethodsA one-year prospective cohort study was performed in the surgical and medical ICU of a teaching hospital in Nanjing, China. Twenty-nine septic shock patients who required early goal-directed fluid resuscitation were consecutively included. Pulse indicator continuous cardiac output (PiCCO) device was used to measure hemodynamic parameters before and after early goal-directed therapy (EGDT). Based on CI changes after EGDT, patients were assign to the CI increased group or the CI constant group, respectively. The incidence of poor renal outcome, which was defined as AKI on admission without recovery in following three days or new onset AKI within 28 days, was recorded. We investigated whether an increased CI was associated with a better renal outcome.ResultsAfter EGDT, there were 16 patients in the CI increased group and 13 patients in the CI constant group. The incidence of poor renal outcome was lower in CI increased group than in the CI constant group (6% vs. 62%; P = 0.003) with a relative risk of 0.10. The logistic regression showed that the CI percent change was associated with renal outcome, with an odd ratio of 0.003 (P = 0.056) after adjustment of possible confounding factors. The CI percent change would predict a good renal outcome (AU ROC 0.739, P = 0.012) with moderate accuracy (sensitivity 75% and specificity 89%) when using a 10% cut-off value from Youden index. The CI percent change was also positively correlated with creatinine clearance (CCr) after EGDT (ρ = 0.548; P = 0.002).ConclusionsThe increased CI after EGDT was a protective factor for kidney in patients with septic shock. A CI increased above 10% could be potentially used to predict development and reversibility of AKI in septic shock patients.Trial registrationClinicaltrials.gov:NCT01862588 (May 13, 2013).

Dataset Information

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

Publications

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

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