Serum cystatin C is a potential predictor of short-term mortality and acute kidney injury in acute aortic dissection patients: a retrospective cohort study

Wang, Jun; Yang, Biwen; Liu, Meili; You, Tianyan; Han, Song; Chen, Yihuan; Huang, Haoyue; Li, Shifeng; Wang, Zhiyang; Li, Xinyue; Huang, Fang; Teng, Xiaochun

doi:10.21037/jtd-22-937

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…In this study, the prevalence of postoperative AKI in aortic dissection patients was found to be 20.29%, which is lower than in previous studies 22 , 23 , 24 and may be due to the lower mean age of the participants in this research. Meanwhile, the results of the present study confirm and extend previous findings.…”

Section: Discussioncontrasting

confidence: 82%

Variability of blood pressure and risk of postoperative acute kidney injury in patients undergoing surgery for acute aortic dissection: A 11‐year single‐center study

Zheng

Zhu

et al. 2023

J of Clinical Hypertension

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In patients receiving surgery, increased blood pressure variability may contribute to the onset of acute kidney injury (AKI). However, the extent and significance of blood pressure variability in patients undergoing aortic dissection surgery have yet to be determined. The authors analyzed all patients operated for aortic dissection at Peking University Shenzhen Hospital from January 1, 2011 to December 31, 2021. Preoperative blood pressure variability was expressed as standard deviation, coefficient of variation (CV), and range. The primary outcome was postoperative AKI. After multivariate analysis, systolic blood pressure variability was found to be independently associated with AKI after aortic dissection surgery. The odds ratios and 95% confidence intervals for standard deviation, CV, and systolic blood pressure range were 1.232 (1.011-1.500), 1.451 (1.058-1.991), and 1.138 (0.986-1.288) for postoperative AKI, respectively. However, there was no significant relationship between diastolic blood pressure variability and AKI after aortic dissection surgery. Finally, in patients undergoing surgery for aortic dissection, the variability of systolic blood pressure is actually an important factor in the development of AKI.

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Section: Discussioncontrasting

confidence: 82%

Variability of blood pressure and risk of postoperative acute kidney injury in patients undergoing surgery for acute aortic dissection: A 11‐year single‐center study

Zheng

Zhu

et al. 2023

J of Clinical Hypertension

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“…Serum cystatin C is eliminated solely via glomerular filtration and can be easily measured [ 34 ]. Compared with urea and creatinine, the serum cystatin C concentration increases earlier when the kidney is injured [ 35 ], and it is a useful predictor of short-term mortality and AKI in acute aortic dissection patients [ 36 ]. INR is a standardized measure of the extrinsic coagulation pathway.…”

Section: Discussionmentioning

confidence: 99%

A simple machine learning model for the prediction of acute kidney injury following noncardiac surgery in geriatric patients: a prospective cohort study

Peng,

Zhu,

Chen

et al. 2024

BMC Geriatr

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Background Surgery in geriatric patients often poses risk of major postoperative complications. Acute kidney injury (AKI) is a common complication following noncardiac surgery and is associated with increased mortality. Early identification of geriatric patients at high risk of AKI could facilitate preventive measures and improve patient prognosis. This study used machine learning methods to identify important features and predict AKI following noncardiac surgery in geriatric patients. Methods The data for this study were obtained from a prospective cohort. Patients aged ≥ 65 years who received noncardiac surgery from June 2019 to December 2021 were enrolled. Data were split into training set (from June 2019 to March 2021) and internal validation set (from April 2021 to December 2021) by time. The least absolute shrinkage and selection operator (LASSO) regularization algorithm and the random forest recursive feature elimination algorithm (RF-RFE) were used to screen important predictors. Models were trained through extreme gradient boosting (XGBoost), random forest, and LASSO. The SHapley Additive exPlanations (SHAP) package was used to interpret the machine learning model. Results The training set included 6753 geriatric patients. Of these, 250 (3.70%) patients developed AKI. The XGBoost model with RF-RFE selected features outperformed other models with an area under the precision-recall curve (AUPRC) of 0.505 (95% confidence interval [CI]: 0.369–0.626) and an area under the receiver operating characteristic curve (AUROC) of 0.806 (95%CI: 0.733–0.875). The model incorporated ten predictors, including operation site and hypertension. The internal validation set included 3808 geriatric patients, and 96 (2.52%) patients developed AKI. The model maintained good predictive performance with an AUPRC of 0.431 (95%CI: 0.331–0.524) and an AUROC of 0.845 (95%CI: 0.796–0.888) in the internal validation. Conclusions This study developed a simple machine learning model and a web calculator for predicting AKI following noncardiac surgery in geriatric patients. This model may be a valuable tool for guiding preventive measures and improving patient prognosis. Trial registration The protocol of this study was approved by the Committee of Ethics from West China Hospital of Sichuan University (2019–473) with a waiver of informed consent and registered at www.chictr.org.cn (ChiCTR1900025160, 15/08/2019).

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“…In recent years, several novel biomarkers for AKI have been evaluated in aortic surgery. The most studied biomarkers, derived from urine or plasma samples, are related to cell cycle arrest, such as NGAL, liver-type fatty acid-binding protein (L-FABP), IGFBP-7, TIMP-2, and cystatin C [ 7 , 8 , 9 , 10 , 11 , 29 , 30 , 31 , 32 ]. A few studies have evaluated the predictive ability of blood immune-inflammatory biomarkers such as plasma secretory leukocyte peptidase inhibitor (SLPI), pentraxin 3 (PTX3), IL1-RA, MCP-1, suppressor of tumorigenicity 2 (ST-2), IL-6, and IL-10 [ 19 , 20 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

A Novel Predictive Model for Acute Kidney Injury Following Surgery of the Aorta

Chen,

Zhao,

Chen

et al. 2024

Rev. Cardiovasc. Med.

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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.

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Serum cystatin C is a potential predictor of short-term mortality and acute kidney injury in acute aortic dissection patients: a retrospective cohort study

Cited by 4 publications

References 29 publications

Variability of blood pressure and risk of postoperative acute kidney injury in patients undergoing surgery for acute aortic dissection: A 11‐year single‐center study

Variability of blood pressure and risk of postoperative acute kidney injury in patients undergoing surgery for acute aortic dissection: A 11‐year single‐center study

A simple machine learning model for the prediction of acute kidney injury following noncardiac surgery in geriatric patients: a prospective cohort study

A Novel Predictive Model for Acute Kidney Injury Following Surgery of the Aorta

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