AUTHOR=Zhang Xinling , Ding Ranran , Yang Guangming , Jiang Yan , Feng Yaping , Qu Feng , Qiao Yuling , Meng Qiang 

TITLE=Development and validation of a triglyceride-glucose integrated nomogram for acute kidney injury prediction in acute myocardial infarction patients: a multicenter database study

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1620664

DOI=10.3389/fcvm.2025.1620664

ISSN=2297-055X

ABSTRACT=BackgroundAcute kidney injury (AKI) is a life-threatening complication in patients with acute myocardial infarction (AMI), leading to increased morbidity and mortality. Early prediction of high-risk patients remains a clinical challenge.MethodsWe developed and validated a predictive model for AKI using data from two large critical care databases: MIMIC-IV (n = 1,227) and eICU (n = 1,954). Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression were applied to identify independent predictors. A nomogram was constructed incorporating the triglyceride-glucose (TyG) index and clinical variables.ResultsSeven predictors were included in the final model: TyG index, blood urea nitrogen (BUN), SOFA score, age, serum sodium, serum albumin and systolic blood pressure (SBP). The model demonstrated excellent discrimination with area under the curve (AUC) values of 0.85 in the training cohort, 0.83 in the internal validation cohort and 0.81 in the external validation cohort. Decision curve analysis showed clinical usefulness across a wide range of risk thresholds (22%–45%). The TyG index was independently associated with increased AKI risk (odds ratio 1.31; 95% CI: 1.07–1.60). The model also showed improved risk reclassification (net reclassification index: 0.22; p < 0.001).ConclusionThe TyG-based nomogram provides a practical and accurate tool for early prediction of AKI in AMI patients. By integrating metabolic, hemodynamic, and organ dysfunction markers, this model enables multidimensional risk stratification and may support timely preventive strategies in the ICU setting.