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. 2021 May 28:14:2127-2136.
doi: 10.2147/IJGM.S312058. eCollection 2021.

Plasma Endogenous Sulfur Dioxide: A Novel Biomarker to Predict Acute Kidney Injury in Critically Ill Patients

Yijia Jiang  1 , Jingyi Wang  1 , Xi Zheng  1 , Jiantong Du  2
Affiliations

Plasma Endogenous Sulfur Dioxide: A Novel Biomarker to Predict Acute Kidney Injury in Critically Ill Patients

Yijia Jiang et al. Int J Gen Med. .

Abstract

Purpose: Sulfur dioxide (SO2) is a novel gaseous signaling molecule that plays an important role in inflammation, which contributes the pathogenesis of acute kidney injury (AKI). The aim of this study was to explore the predictive value of plasma SO2 for AKI in high-risk patients.

Patients and methods: A prospective cohort of 167 patients who underwent major noncardiac surgery was enrolled in the study. Plasma SO2, urine neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinase-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP7) levels were detected immediately after the operation. The primary endpoint was new-onset AKI within 72 h after admission. The ability of biomarkers including SO2 and a clinical risk model to predict AKI was compared by receiver operator characteristic (ROC) curve analysis and decision curve analysis (DCA), additional contributions were evaluated by integrated discrimination improvement (IDI) and net reclassification improvement (NRI) analyses.

Results: A total of 61 (36.5%) patients developed AKI within 72 h of surgery. Compared to NGAL and [TIMP-2]·[IGFBP7], SO2 showed better predictive ability for new-onset AKI with an area under the ROC curve of 0.771 (95% confidence interval: 0.700-0.832, p<0.001). The improvement in predictive value by including SO2 in the clinical risk model was supported by NRI (0.28; P=0.04) and IDI (0.15; P<0.001) analyses. The net benefit of the combination of SO2 and clinical variables was the max in DCA.

Conclusion: Plasma SO2 shows a useful value for predicting new-onset AKI, and improved AKI prediction based on clinical variables, which can guide the implementation of preventive measures for high-risk patients.

Keywords: AKI; gasotransmitter; intensive care unit; predictive modelling.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Discrimination of plasma SO2 between non-AKI and AKI of different severities. ***Comparison between non-AKI and AKI of different severities (p<0.001).
Figure 2
Figure 2
Predictive value of plasma SO2 and urine biomarkers for new-onset AKI.
Figure 3
Figure 3
Decision curve for prediction of new-onset AKI using different prediction models.
See this image and copyright information in PMC

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