IMR Press / RCM / Volume 25 / Issue 2 / DOI: 10.31083/j.rcm2502054
Open Access Original Research
A Novel Predictive Model for Acute Kidney Injury Following Surgery of the Aorta
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1 Department of Surgery, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
2 Department of Cardiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
3 Cardiology Department, Heart Center of Fujian Province, Union Hospital, Fujian Medical University, 350000 Fuzhou, Fujian, China
*Correspondence: (Liqing Wang); (Zhaoyang Chen)
These authors contributed equally.
Rev. Cardiovasc. Med. 2024, 25(2), 54;
Submitted: 30 May 2023 | Revised: 31 August 2023 | Accepted: 18 September 2023 | Published: 4 February 2024
Copyright: © 2024 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

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-γ) and interleukin-4 (IL-4) were identified as candidate AKI predictors. The cytokine-based model including IFN-γ 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-γ and IL-4 emerged as independent risk factors for AKI. Patients in the second and third tertiles of IFN-γ 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-γ 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.

acute kidney injury
macrophage polarization
predictive model
aortic surgery
NSFC8210022135/National Natural Science Foundation of China
2021-1-I2M-016/Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences
Fig. 1.
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