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Introduction:Acute Kidney Injury (AKI) describes a swift decrease in kidney function occurring over hours to days, characterized by the kidneys' failure to remove waste products from the bloodstream. This leads to dangerous complications like metabolic acidosis, fluid overload, and electrolyte imbalances, such as hyperkalemia, which can cause life-threatening arrhythmias. AKI is common in both hospital and outpatient settings, often triggered by dehydration, sepsis, or exposure to nephrotoxic...
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Machine Learning-Based Prediction and Feature Attribution Analysis of Contrast-Associated Acute Kidney Injury in

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Machine learning models significantly improve prediction of contrast-associated acute kidney injury (CA-AKI) in acute myocardial infarction patients. These advanced algorithms, particularly ensemble methods, offer superior risk stratification and reliably identify low-risk individuals for preventive strategies.

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Area of Science:

  • Cardiology
  • Nephrology
  • Artificial Intelligence in Medicine
  • Medical Informatics

Background:

  • Contrast-associated acute kidney injury (CA-AKI) is a significant complication following coronary angiography in acute myocardial infarction (AMI) patients.
  • Conventional risk stratification models for CA-AKI often lack accuracy due to linear assumptions and limited variable integration.
  • Accurate and early prediction of CA-AKI is crucial for implementing timely preventive measures.

Purpose of the Study:

  • To evaluate and compare the predictive performance of various machine learning (ML) algorithms against traditional logistic regression and the Mehran risk score for CA-AKI prediction in AMI patients.
  • To identify key determinants of CA-AKI risk using explainable artificial intelligence (XAI) methods.
  • To assess the clinical utility of ML models in identifying low-risk patients for tailored preventive strategies.

Main Methods:

  • A retrospective analysis of 1741 AMI patients undergoing coronary angiography.
  • Development and comparison of multiple ML models (GBM, RF, XGBoost, SVM, elastic net) and logistic regression using clinical and laboratory data.
  • Construction of a weighted ensemble ML model and assessment of model performance using AUC, sensitivity, specificity, PPV, and NPV.
  • Application of SHAP for model interpretability to determine feature importance.

Main Results:

  • CA-AKI occurred in 20.4% of patients.
  • The weighted ensemble ML model achieved the highest discriminative performance (AUC 0.721), outperforming logistic regression (AUC 0.608) and the Mehran risk score.
  • The ensemble model demonstrated a high negative predictive value (NPV 0.942), effectively identifying low-risk patients.
  • Key predictors identified by XAI included inflammatory markers (neutrophil-to-lymphocyte ratio), sodium levels, uric acid, baseline renal function, and contrast volume.

Conclusions:

  • Interpretable ML models, particularly ensemble and gradient boosting methods, offer superior risk stratification for CA-AKI in AMI patients compared to conventional approaches.
  • The high NPV of ML models supports their clinical utility in safely identifying low-risk patients.
  • These findings enable the development of individualized, risk-adapted preventive strategies for CA-AKI.