Prediction of the development of contrast‑induced nephropathy following percutaneous coronary artery intervention by
Xiao Ma1,2,3, Changhua Mo1,2,3, Yujuan Li1,2,3
1Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China.
Insights
Machine learning accurately predicts contrast-induced nephropathy (CIN) risk after percutaneous coronary intervention (PCI). This model identifies high-risk patients, potentially reducing adverse outcomes in those undergoing PCI.
Area of Science:
- Cardiology
- Nephrology
- Artificial Intelligence
Background:
- Contrast-induced nephropathy (CIN) increases mortality and morbidity in patients with coronary artery disease undergoing percutaneous coronary intervention (PCI).
- Accurate prediction of CIN risk is crucial for patient management and preventative strategies.
Purpose of the Study:
- To develop and evaluate a machine learning-based risk stratification model for predicting CIN after elective PCI.
- To identify key clinical predictors associated with CIN development.
Main Methods:
- Retrospective study of 240 patients undergoing PCI (December 2017 - May 2020).
- CIN defined as serum creatinine increase ≥0.5 mg/dL or ≥25% within 72 hours post-PCI.
- Eight machine learning models were trained and evaluated using clinical variables; Shapley Additive exPlanations (SHAP) were used for model interpretation.
Main Results:
- CIN developed in 16.5% of patients (37 out of 240).
- Eleven significant predictors of CIN were identified: uric acid, peripheral vascular disease, cystatin C, creatine kinase-MB, hemoglobin, N-terminal pro-brain natriuretic peptide, age, diabetes, systemic immune-inflammatory index, total protein, and low-density lipoprotein.
- Support Vector Machine (SVM) model achieved the highest Area Under the Curve (AUC) of 0.784 for CIN prediction.
Conclusions:
- Machine learning models demonstrate significant potential in identifying patients at high risk for CIN following elective PCI.
- The SVM model, utilizing 11 key clinical features, offers a promising tool for risk stratification and personalized preventative strategies.
Abstract:
Contrast-induced nephropathy (CIN) is associated with increased mortality and morbidity in patients with coronary artery disease undergoing elective percutaneous coronary intervention(PCI). We developed a machine learning-based risk stratification model to predict contrast-induced nephropathy after PCI. A study retrospectively enrolling 240 patients eligible for PCI from December 2017 to May 2020 was performed. CIN was defined as a rise in serum creatinine levels ≥0.5 mg/dL or ≥25% from baseline within 72 h after surgery. Eight machine learning methods were performed based on clinical variables. Shapley Additive exPlanation values were also used to interpret the best-performing prediction models. Development of CIN was found in 37 patients(16.5%) after PCI. There were 11 significant predictors of CIN, including uric acid, peripheral vascular disease, cystatin C, creatine kinase-MB, haemoglobin, N-terminal pro-brain natriuretic peptide, age, diabetes, systemic immune-inflammatory index, total protein, and low-density lipoprotein. Regarding the efficacy of the machine learning model that accurately predicted CIN, SVM exhibited the most outstanding AUC value of 0.784. The SHAP and radar plots were used to illustrate the positive and negative effects of the 11 features attributed to the SVM. Machine learning models have the potential to identify the risk of CIN for elective PCI patients.
More Related Videos
07:25Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
Published on: September 22, 2020
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Related Concept Videos
01:28Cardiac Catheterization I: Pre-Procedure Overview
01:30Acute Kidney Injury IV: Diagnostic Studies and Prevention