Development and Testing a New Online Dynamic Nomogram for Contrast-Induced Acute Kidney Injury in Elderly Patients with ST-Segment Elevation Myocardial Infarction.

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.