Background: Acute kidney injury (AKI) frequently occurs after aortic
surgery and has a significant impact on patient outcomes. Early detection or
prediction of AKI is crucial for timely interventions. This study aims to develop
and validate a novel model for predicting AKI following aortic surgery.
Methods: We enrolled 156 patients who underwent on-pump aortic surgery
in our hospital from February 2023 to April 2023. Postoperative levels of eight
cytokines related to macrophage polarization analyzed using a multiplex cytokine
assay. All-subset regression was used to select the optimal cytokines to predict
AKI. A logistic regression model incorporating the selected cytokines was used
for internal validation in combination with a bootstrapping technique. The
model’s ability to discriminate between cases of AKI and non-AKI was assessed
using receiver operating characteristic (ROC) curve analysis. Results:
Of the 156 patients, 109 (69.87%) developed postoperative AKI. Interferon-gamma
(IFN-) and interleukin-4 (IL-4) were identified as candidate AKI
predictors. The cytokine-based model including IFN- and IL-4
demonstrated excellent discrimination (C-statistic: 0.90) and good calibration
(Brier score: 0.11). A clinical nomogram was generated, and decision curve
analysis revealed that the cytokine-based model outperformed the clinical
factor-based model in terms of net benefit. Moreover, both IFN- and
IL-4 emerged as independent risk factors for AKI. Patients in the second and
third tertiles of IFN- and IL-4 concentrations had a significantly
higher risk of severe AKI, a higher likelihood of requiring renal replacement
therapy, or experiencing in-hospital death. These patients also had extended
durations of mechanical ventilation and intensive care unit stays, compared with
those in the first tertile (all p for group trend 0.001).
Conclusions: We successfully established a novel and powerful predictive
model for AKI, and demonstrating the significance of IFN- and IL-4 as
valuable clinical markers. These cytokines not only predict the risk of AKI
following aortic surgery but are also linked to adverse in-hospital outcomes.
This model offers a promising avenue for the early identification of high-risk
patients, potentially improving clinical decision-making and patient care.