Novel Blood Cytokine-Based Model for Predicting Severe Acute Kidney Injury and Poor Outcomes After Cardiac Surgery.

ABSTRACT: Background Alterations in serum creatinine levels delay the identification of severe cardiac surgery-associated acute kidney injury. To provide timely diagnosis, novel predictive tools should be investigated. Methods and Results This prospective observational study consists of a screening cohort (n=204) and a validation cohort (n=198) from 2 centers from our hospital. Thirty-two inflammatory cytokines were measured via a multiplex cytokine assay. Least absolute shrinkage and selection operator regression was conducted to select the cytokine signatures of severe cardiac surgery-associated acute kidney injury. Afterwards, the significant candidates including interferon-?, interleukin-16, and MIP-1? (macrophage inflammatory protein-1 alpha) were integrated into the logistic regression model to construct a predictive model. The predictive accuracy of the model was evaluated in these 2 cohorts. The cytokine-based model yielded decent performance in both the screening (C-statistic: 0.87, Brier 0.10) and validation cohorts (C-statistic: 0.86, Brier 0.11). Decision curve analysis revealed that the cytokine-based model had a superior net benefit over both the clinical factor-based model and the established plasma biomarker-based model for predicting severe acute kidney injury. In addition, elevated concentrations of each cytokine were associated with longer mechanical ventilation times, intensive care unit stays, and hospital stays. They strongly predicted the risk of composite events (defined as treatment with renal replacement therapy and/or in-hospital death) (OR of the fourth versus the first quartile [95% CI]: interferon-?, 27.78 [3.61-213.84], interleukin-16, 38.07 [4.98-291.07], and MIP-1?, 9.13 [2.84-29.33]). Conclusions Our study developed and validated a promising blood cytokine-based model for predicting severe acute kidney injury after cardiac surgery and identified prognostic biomarkers for assisting in outcome risk stratification.

SUBMITTER: Chen Z 

PROVIDER: S-EPMC7763725 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature