BackgroundSeveral models have been developed for prediction of contrast‐induced nephropathy (CIN); however, they only contain patients receiving intra‐arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure‐related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media.Methods and ResultsA total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5‐fold cross‐validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver‐operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level.ConclusionsThe newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN.
With the rapid development of imaging diagnosis and interventional therapy, contrast media (CM) are widely used in clinics. However, contrast-induced nephropathy (CIN) is the third leading cause of hospital-acquired acute renal failure accounting for 10-12% of all causes of hospital-acquired renal failure. Recent study found that inflammation may participate in the pathogenesis of CIN, but the role of it remains unclear. HK-2 cells were treated with Iohexol, Urografin, and mannitol. Two types of CM increased the release of HMGB1 in cell supernatant accompanied by increased expression of TLR2 and CXCR4. Iohexol and Urografin also caused a significant increase in NF-κB followed by the release of IL-6 and MCP-1. To clarify the role of HMGB1, TLR2, and CXCR4, glycyrrhizin, anti-TLR2-IgG, and AMD3100 were used to inhibit HMGB1, TLR2, and CXCR4, respectively. Significant decrease in the expression of TLR2, CXCR4, nuclear NF-κB, and the release of IL-6 and MCP-1 were observed. These results indicate that TLR2 and CXCR4 signaling are involved in CM-induced HK-2 cell injury model in an HMGB1-dependent pathway, which may provide a new target for the prevention and the treatment of CIN.
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