Establishment of a nomogram model for predicting the risk of contrast-induced nephropathy after percutaneous coronary intervention in type 2 diabetics

Objective To explore the risk factors of contrast-induced nephropathy(CIN) in patients with type 2 diabetes mellitus(T2DM) undergoing percutaneous coronary intervention(PCI) and develop a visualized evaluation tool for clinical prediction of CIN. Methods A total of 625 T2DM patients fulfilling the criteria were selected and divided into control(n=546) and CIN(n=79) groups. Clinical data related to CIN were collected. Statistically significant variables were imported into a multivariate Logistic regression model for analyzing the risk factors of CIN. R-software was utilized for developing a nomogram model for predicting CIN risk. Receiver operating characteristic(ROC) curve was plotted for testifying the predictive value and conformance testing was performed by plotting calibration curve. Hosmer-Lemeshow test was utilized for judging the model's goodness-of-fit. Results Age, hypertension, hemoglobin<130 g/L, albumin<30 g/L, LEVF<50%, uric acid>400 μmol/L and eGFR<90 mL·min^-1·(1.73m²) ^-1 were independent risk factors for T2DM complicated with post-PCI CIN(all P<0.05). With an inclusion of these factors, a nomogram model was successfully constructed. It had decent prediction performance with an area under the ROC curve of 0.82(95% CI=0.802 to 0.882). Calibration curve indicated an excellent correlation between predicted and actual results(P=0.278). Conclusion The above individualized nomogram model for predicting the risk of CIN patients has an excellent resolution. It has guiding significance for screening high-risk population with CIN among T2DM patients undergoing PCI and formulating intervention strategies.