Prediction of Acute Kidney Injury by Cystatin C and [TIMP-2]*[IGFBP7] after Thoracic Aortic Surgery with Moderate Hypothermic Circulatory Arrest

Biomarker-guided detection of acute kidney injury in abdominal aortic surgery: the new and the old

Introduction

Acute kidney injury (AKI) is a common complication in patients undergoing major vascular surgery. Despite significant research efforts in this area, the incidence of AKI remains high, posing a significant challenge to healthcare systems, especially in situations where resources are limited. Early prediction of AKI severity and individualized postoperative care is therefore essential.

Methods

The primary objective of this exploratory study was to assess the diagnostic value of urine cell-cycle arrest biomarkers [(TIMP-2) × (IGFBP7)] and soluble urokinase plasminogen activator receptor (suPAR) for predicting moderate or severe AKI within 24 h after open aortic surgery, and compared to routine kidney biomarkers. Seventy-five patients undergoing elective aortic surgery were included. Clinical parameters, urine and blood samples were collected preoperatively, immediately postoperatively, and 24 h later. AKI was defined using KDIGO criteria. Individual and combined diagnostic performance of biomarkers were evaluated.

Results

Of the 75 patients, 61% developed AKI, of which 28% developed moderate or severe AKI within 24 h of surgery. Baseline demographics, comorbidities and kidney parameters did not differ between patients with moderate or severe AKI (AKI II/III) and none or mild AKI (AKI 0/I), except for higher preoperative suPAR levels in later AKI II/III patients. Urine osmolality, Cystatin C and serum creatinine had the highest predictive power for AKI II/III with AUCs of 0.75-0.72. (TIMP-2) × (IGFBP7), and neither (TIMP-2) × (IGFBP7) nor suPAR individually showed superior diagnostic value. Combining CysC or SCr with urine osmolality and 6 h urine output gave the best performance with AUCs of 0.86 (95% CI, 0.74-0.96) and 0.85 (95% CI, 0.75-0.95) respectively.

Conclusion

Our study suggests that routine parameters like urine osmolality, CysC, SCr and 6 h urine output perform best in predicting postoperative AKI after aortic surgery compared to the new biomarkers (TIMP-2) × (IGFBP7) and suPAR. Combining biomarkers, particularly CysC or SCr with urine output, urine osmolality, may enhance diagnostic accuracy. Further validation in larger cohorts and clinical settings is warranted to establish their clinical utility.

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

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.

Comparing diagnostic accuracy of biomarkers for acute kidney injury after major surgery: A PRISMA systematic review and network meta-analysis

BACKGROUND

The timely identification of patients at risk of acute kidney injury (AKI), along with early prevention, real-time monitoring, and prompt intervention, plays a crucial role in enhancing patient prognosis after major surgery.

METHODS

We conducted a comprehensive search across multiple databases, including Web of Science, EMBASE, MEDLINE, China National Knowledge Infrastructure, and Cochrane Library. Each study's risk of bias was independently evaluated as low, moderate, or high, utilizing criteria adapted from Quality Assessment of Diagnostic Accuracy Studies 2. The analysis was performed using STATA V.17.0 and R software V.3.4.1. Diagnostic tests were ranked based on the dominance index. We performed meta-analyses to calculate odds ratios (ORs) and 95% confidence intervals (CIs) individually. We then carried out a network meta-analysis to compare the performances of these biomarkers.

RESULTS

Fifteen studies were included in this analysis. The meta-analysis findings revealed that among all the biomarkers assessed, serum cystatin C (s-CysC) (hierarchical summary receiver operating characteristic curve [HSROC] 82%, 95% CI 0.78-0.85) exhibited the highest HSROC value. The network meta-analysis demonstrated that urinary kidney injury molecule-1 (u-KIM-1) and s-CysC displayed relatively higher sensitivity and specificity, respectively. In subgroup analyses, u-KIM-1 in the urine output (OU) group (OR 303.75, 95% CI 3.39-1844.88), s-CysC in the non-OU group (OR 10.31, 95% CI 3.09-26.2), interleukin-18 in the noncardiac surgery group (OR 46.20, 95% CI 0.48-307.68), s-CysC in the cardiac group (OR 12.42, 95% CI 2.9-35.86), u-KIM-1 in the retrospective group (OR 243.00, 95% CI 1.73-1582.11), and s-CysC in the prospective group (OR 8.35, 95% CI 2.34-21.15) had the best diagnostic accuracy. However, it is important to note that existing publication bias may reduce the reliability of the above-mentioned results.

CONCLUSION

The biomarker of s-CysC has the highest HSROC value to predicting acute kidney injury after major surgery in meta-analysis and relatively higher specificity in network meta-analyses. u-KIM-1 exhibited relatively higher sensitivity, with best diagnostic accuracy in the OU and retrospective group in the subgroup analysis.

Comparative accuracy of biomarkers for the prediction of hospital-acquired acute kidney injury: a systematic review and meta-analysis

Background

Several biomarkers have been proposed to predict the occurrence of acute kidney injury (AKI); however, their efficacy varies between different trials. The aim of this study was to compare the predictive performance of different candidate biomarkers for AKI.

Methods

In this systematic review, we searched PubMed, Medline, Embase, and the Cochrane Library for papers published up to August 15, 2022. We selected all studies of adults (> 18 years) that reported the predictive performance of damage biomarkers (neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (L-FABP)), inflammatory biomarker (interleukin-18 (IL-18)), and stress biomarker (tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein-7 (TIMP-2 × IGFBP-7)) for the occurrence of AKI. We performed pairwise meta-analyses to calculate odds ratios (ORs) and 95% confidence intervals (CIs) individually. Hierarchical summary receiver operating characteristic curves (HSROCs) were used to summarize the pooled test performance, and the Grading of Recommendations, Assessment, Development and Evaluations criteria were used to appraise the quality of evidence.

Results

We identified 242 published relevant studies from 1,803 screened abstracts, of which 110 studies with 38,725 patients were included in this meta-analysis. Urinary NGAL/creatinine (diagnostic odds ratio [DOR] 16.2, 95% CI 10.1–25.9), urinary NGAL (DOR 13.8, 95% CI 10.2–18.8), and serum NGAL (DOR 12.6, 95% CI 9.3–17.3) had the best diagnostic accuracy for the risk of AKI. In subgroup analyses, urinary NGAL, urinary NGAL/creatinine, and serum NGAL had better diagnostic accuracy for AKI than urinary IL-18 in non-critically ill patients. However, all of the biomarkers had similar diagnostic accuracy in critically ill patients. In the setting of medical and non-sepsis patients, urinary NGAL had better predictive performance than urinary IL-18, urinary L-FABP, and urinary TIMP-2 × IGFBP-7: 0.3. In the surgical patients, urinary NGAL/creatinine and urinary KIM-1 had the best diagnostic accuracy. The HSROC values of urinary NGAL/creatinine, urinary NGAL, and serum NGAL were 91.4%, 85.2%, and 84.7%, respectively.

Conclusions

Biomarkers containing NGAL had the best predictive accuracy for the occurrence of AKI, regardless of whether or not the values were adjusted by urinary creatinine, and especially in medically treated patients. However, the predictive performance of urinary NGAL was limited in surgical patients, and urinary NGAL/creatinine seemed to be the most accurate biomarkers in these patients. All of the biomarkers had similar predictive performance in critically ill patients.

Trial registrationCRD42020207883, October 06, 2020.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-04223-6.