Abstract
OBJECTIVE: To establish a nomogram model to predict the risk of contrast-induced acute kidney injury (CI-AKI) by analyzing the risk factors of CI-AKI and to evaluate its effectiveness. METHODS: Retrospectively analyze the clinical data of non-ST-elevation acute coronary syndrome (NSTE-ACS) patients who underwent percutaneous coronary intervention (PCI) in our cardiology department from September 2018 to June 2021. Of these, patients who underwent PCI in an earlier period formed the training cohort (70%; n = 809) for nomogram development, and those who underwent PCI thereafter formed the validation cohort (30%; n = 347) to confirm the model's performance. The independent risk factors of CI-AKI were determined by LASSO regression and multivariable logistic regression analysis. By using R software from which nomogram models were subsequently generated. The nomogram was developed and evaluated based on discrimination, calibration, and clinical efficacy using the concordance statistic (C-statistic), calibration plot, and decision curve analysis (DCA), respectively. RESULTS: The nomogram consisted of six variables: age >75, left ventricular ejection fraction, diabetes mellitus, fibrinogen-to-albumin ratio, high-sensitive C-reactive protein, and lymphocyte count. The C-index of the nomogram is 0.835 (95% CI: 0.800-0.871) in the training cohort and 0.767 (95% CI: 0.711-0.824) in the validation cohort, respectively. The calibration plots exhibited that the nomogram was in good agreement between prediction and observation in the training and validation cohorts. Decision curve analysis and clinical impact curve suggested that the predictive nomogram had clinical utility. CONCLUSION: The nomogram model established has a good degree of differentiation and accuracy, which is intuitively and individually to screen high-risk groups and has a certain predictive value for the occurrence of CI-AKI in NSTE-ACS patients after PCI.