Prediction of the development of contrast‑induced nephropathy following percutaneous coronary artery intervention by machine learning

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.

Keywords:

• Machine learning
• contrast-induced nephropathy
• percutaneous coronary artery intervention
• diagnostic prediction
  
• support vector machine
• model interpretation

Consent form

All the co-authors agreed to the publication of this paper.

Author contributions

Concept and design of the work by Chun Gui, Xiao Ma; Writing of the text by Xiao Ma, Changhua Mo; Statistical analysis by Yujuan Li, Xinyuan Chen; Editing by Chun Gui.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Guangxi Natural Science Foundation (Grant No. 2020GXNSFDA297014) and the Innovative Research Team Project of Guangxi Natural Science Foundation (Grant No. 2018GXNSFGA281006).