Jingkun Jin,1,* Jiahui Ding,1,* Xishen Zhang,1 Linsheng Wang,1 Xudong Zhang,1 Wenhua Li,1,2 Shanshan Li2 

1The First School of Clinical Medicine, Xuzhou Medical University, Xuzhou, People’s Republic of China; 2Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Wenhua Li; Shanshan Li, Email xzwenhua0202@163.com; 707701357@qq.com

Background: ST-segment elevation myocardial infarction (STEMI), the most severe form of acute coronary syndrome (ACS), requires timely percutaneous coronary intervention (PCI) to restore coronary blood flow. However, contrast-induced acute kidney injury (CI-AKI), the third most common cause of hospital-acquired renal failure, remains a critical complication of PCI.
Objective: To develop a machine learning model predicting CI-AKI risk in elderly patients with STEMI patients using clinical features.
Methods: Data from 2120 elderly patients with STEMI treated with PCI at Xuzhou Medical University Affiliated Hospital (2019– 2023) were used for model development and testing. An external validation cohort, comprising 236 individuals, was derived from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (2008– 2019). Lasso regression selected predictors, and nine Machine Learning (ML) algorithms were evaluated via Receiver Operating Characteristic (ROC) analysis. Overlapping top-ranked features from high-performing models (AUC > 0.8) informed a nomogram. Performance was assessed using AUC and decision curve analysis (DCA).
Results: The final model included five independent predictors: lymphocyte-to-monocyte ratio, diuretic use, residual cholesterol, serum creatinine, and blood urea nitrogen. This model was developed as a simple-to-use online dynamic nomogram. It demonstrated robust discrimination, with C-statistics of 0.782 in the testing dataset and 0.791 in the validation dataset. DCA confirmed its clinical utility across a wide range of risk thresholds.
Conclusion: A new online dynamic nomogram was developed to provide a practical tool for CI-AKI risk stratification in elderly STEMI patients, aiding personalized prevention strategies.

Keywords: contrast-induced acute kidney injury, ST-segment elevation myocardial infarction, remnant cholesterol, lymphocyte to monocyte ratio, elderly patients