Development and Testing a New Online Dynamic Nomogram for Contrast-Induced Acute Kidney Injury in Elderly Patients
Jingkun Jin1, Jiahui Ding1, Xishen Zhang1
1The First School of Clinical Medicine, Xuzhou Medical University, Xuzhou, People's Republic of China.
Insights
A new machine learning model predicts contrast-induced acute kidney injury (CI-AKI) risk in elderly patients undergoing percutaneous coronary intervention for ST-segment elevation myocardial infarction (STEMI). This tool aids personalized prevention strategies for this vulnerable population.
Area of Science:
- Cardiology
- Nephrology
- Artificial Intelligence in Medicine
Background:
- ST-segment elevation myocardial infarction (STEMI) requires prompt percutaneous coronary intervention (PCI).
- Contrast-induced acute kidney injury (CI-AKI) is a significant complication of PCI, particularly in elderly patients.
- Effective risk stratification for CI-AKI is crucial for patient management.
Purpose of the Study:
- To develop and validate a machine learning model for predicting CI-AKI risk in elderly STEMI patients undergoing PCI.
- To identify key clinical predictors of CI-AKI in this patient cohort.
- To create a practical tool for clinical risk assessment.
Main Methods:
- Utilized data from 2120 elderly STEMI patients treated with PCI (Xuzhou Medical University Affiliated Hospital) and an external validation cohort (MIMIC-IV).
- Employed Lasso regression for feature selection and evaluated nine machine learning algorithms.
- Developed a dynamic nomogram based on overlapping top-ranked features from high-performing models (AUC >0.8).
Main Results:
- The final model identified five independent predictors: lymphocyte-to-monocyte ratio, diuretic use, residual cholesterol, serum creatinine, and blood urea nitrogen.
- The nomogram demonstrated robust discrimination with C-statistics of 0.782 (testing) and 0.791 (validation).
- Decision curve analysis confirmed the clinical utility of the nomogram.
Conclusions:
- A novel, user-friendly online dynamic nomogram was developed for CI-AKI risk stratification in elderly STEMI patients.
- This tool can assist clinicians in implementing personalized CI-AKI prevention strategies.
- The model offers a valuable approach to managing PCI-related kidney complications.
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.
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