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.

Abstract

Our objective was to develop and validate a nomogram model aiming at predicting the risk of contrast-induced acute kidney injury (CI-AKI) following percutaneous coronary intervention (PCI) in patients suffering fromtype 2 diabetes mellitus (T2DM) and also diagnosed with acute coronary syndrome (ACS). The study gathered data from 722 T2DM patients with ACS who received PCI treatment at the Affiliated Hospital of Xuzhou Medical University between February 2019 and December 2022, serving as the training set. Considering the validation set, the study included 217 patients who received PCI at the East Affiliated Hospital of Xuzhou Medical University. The patients were classified into CI-AKI and non-CI-AKI groups. The study employed univariate and multivariate logistic analysis for identifying independent risk factors for CI-AKI, followed by developing a predictive nomogram model for CI-AKI risk using R software. The predictive performance and clinical utility of the nomogram were assessed through internal and external validation, utilizing the areas under the receiver operating characteristic curve (AUC-ROC), the Hosmer-Lemeshow test and calibration correction curve, and decision curve analysis (DCA). The nomogram comprised four variables: age, estimated glomerular filtration rate (eGFR), triglyceride-glucose (TyG) index, and prognostic nutritional index (PNI). The AUC-ROC were 0.785 (95% confidence interval (CI) 0.729-0.841) and 0.802 (95% CI 0.699-0.905) for the training and validation cohorts, respectively, indicating a high discriminative ability of the nomogram. The calibration assessment and decision curve analysis have substantiated the strong concordance and clinical usefulness of the aforementioned. The nomogram exhibits favorable discrimination and accuracy, enabling it to visually and individually identify pre-procedure high-risk patients, and possesses a predictive capacity regarding CI-AKI incidence after PCI in patients diagnosed with both T2DM and ACS.