AUTHOR=Wang Zhaokai , Yang Shuping , Li Cheng , Zhou Chunxue , Wang Chaofan , Jiang Tangxing , Chen Chengcheng , Shao Mengxin , Xu Tongda 

TITLE=Development and validation of an AMR-based predictive model for post-PCI contrast-induced nephropathy in patients with acute ST-segment elevation myocardial infarction

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1552762

DOI=10.3389/fcvm.2025.1552762

ISSN=2297-055X

ABSTRACT=BackgroundThis study aimed to develop and validate an angiography-derived microcirculatory resistance index (AMR)- based nomogram to predict the probability of contrast-induced nephropathy (CIN) following percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI).MethodIn this study, 595 STEMI patients from the Affiliated Hospital of Xuzhou Medical University from January 1, 2022 to December 31, 2023 were included as the training cohort, and 256 patients from the East Hospital of Xuzhou Medical University were included as the validation cohort. Independent risk factors for the development of nomogram were identified using univariate logistic regression, randomized forest regression, multifactorial logistic regression, and LASSO regression analyses. The study evaluated performance by creating calibration curves, analyzing the area under the curve (AUC-ROC) of subjects' work characteristics, examining calibration plots, and conducting decision curve analysis (DCA).ResultMultifactorial logistic regression analysis identified five independent predictors, including eGFR (OR:0.975; 95% CI: 0.970–0.983; P < 0.001), AMR (OR: 2.505; 95% CI: 1.756–3.656; P < 0.001), Serum blood uric acid to high-density lipoprotein cholesterol ratio (UHR) (OR: 1.006; 95% CI: 1.003–1.007; P < 0.001), The triglyceride and glucose index (TyG) (OR: 1.829; 95% CI: 1.346–2.502; P < 0.001), Contrast agent dosage (OR: 1.022; 95% CI: 1.016–1.028; P < 0.001), The nomogram accurately predicted the probability of CIN after PCI. Both the training cohort (AUC: 0.881) and validation cohort (AUC: 0.841) demonstrated good predictive ability of the model. Calibration plots confirmed the agreement between the predictions of the training and validation cohorts. DCA analysis also demonstrated the feasibility of the nomogram in clinical patient management.ConclusionThe nomogram showed good performance in predicting CIN, and it could help clinicians optimize the clinical treatments to improve the prognosis of STEMI patients.