Development and Validation of a Risk Nomogram Model for Predicting Contrast-Induced Acute Kidney Injury in Patients with Non-ST-Elevation Acute Coronary Syndrome Undergoing Primary Percutaneous Coronary Intervention

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.

Prognostic Nutritional Index Combined with Triglyceride-Glucose Index to Contrast a Nomogram for Predicting Contrast-Induced Kidney Injury in Type 2 Diabetes Mellitus Patients with Acute Coronary Syndrome After Percutaneous Coronary Intervention

“…Consequently, several models have been suggested to address this concern. 22 , 23 However, the TyG index combined with PNI to predict contrast nephropathy has not yet been proposed. This retrospective study enrolled 722 T2DM patients with ACS PCI, of whom 69 (9.55%) developed CI-AKI.…”

A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study

“…Other researchers also found that GBDT [ 24 ] and RNN [ 25 ] could perform well in predicting CIAKI. Moreover, Sun et al [ 26 ] exhibited that in patients with ACS, the LASSO + LR-based nomogram model provided a better prediction of CIAKI than the Mehran score (AUC was 0.835 and 0.762, respectively). According to our results, in diabetic patients, ML models (including LASSO + LR, GBDT, XGBT, and SVM) demonstrated better discriminative power than traditional LR and Mehran score in developing predictive models.…”

A Clinical Nomogram Based on the Triglyceride-Glucose Index to Predict Contrast-Induced Acute Kidney Injury after Percutaneous Intervention in Patients with Acute Coronary Syndrome with Diabetes Mellitus

“…By reviewing the literature, we collected some influential factors that may affect the occurrence of CI-AKI after PCI in patients with ACS combined with DM [22][23][24]. Clinical data for all patients were collected from the ASCVD database, including 54 variables such as patient's demographic data (age, gender, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP)), past medical history (hypertension, coronary heart disease, myocardial infarction, chronic kidney disease (CKD), hyperuricemia, etc.…”