Nomogram for predicting acute kidney injury after primary PCI in STEMI patients: development and validation study

Abstract

Objective: To identify the risk factors for acute kidney injury (AKI) after primary percutaneous coronary intervention (PCI) in patients with ST-elevation myocardial infarction (STEMI) and to develop and evaluate a nomogram predictive model. Methods: A retrospective analysis was performed on STEMI patients who underwent primary PCI in our hospital between January 1, 2020, and December 31, 2024. Patients were divided into AKI and non-AKI groups. Univariate and multivariate logistic regression analyses were used to identify the independent risk factors for post-PCI AKI. Based on these factors, a nomogram model was constructed and its predictive accuracy, calibration, and clinical utility were assessed using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Results: A total of 227 STEMI patients were enrolled, of whom 41 (18.1%) developed AKI after PCI. NSAIDs use, female sex, blood glucose, serum creatinine, and serum uric acid emerged as independent risk factors. The ROC analysis showed that the nomogram model (AUC: 0.793) had superior predictive performance compared to the Mehran score (AUC: 0.626). Calibration plots showed strong alignment between predicted and observed outcomes, while DCA indicated a substantial net benefit. Moreover, patients in the high-risk group had significantly longer lengths of stays than those in the low- and moderate-risk groups. Conclusion: The nomogram model exhibits robust predictive accuracy and clinical utility, providing valuable support for the early identification of high-risk STEMI patients for AKI after primary PCI and facilitating the implementation of stratified preventive strategies.