Related Experiment Video
Updated: May 6, 2026
A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
Published on: February 2, 2021
Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease:
Yike Li1, Mingyang Xiao1, Yaqian Li1
1The Second Clinical Medical School, Zhengzhou University, Zhengzhou, China.
Insights
Machine learning accurately predicts acute kidney injury (AKI) in critically ill patients with coronary heart disease (CHD). The XGBoost model identifies key risk factors, enabling early intervention to reduce mortality.
Area of Science:
- Critical Care Medicine
- Nephrology
- Artificial Intelligence in Healthcare
Background:
- Acute kidney injury (AKI) significantly increases mortality and hospitalization in critically ill patients with coronary heart disease (CHD).
- Early prediction of AKI is vital for timely interventions and improved patient outcomes.
Purpose of the Study:
- To develop and validate a machine learning (ML) clinical prediction model for AKI upon admission in critically ill CHD patients.
- To identify key predictors for AKI development in this population.
Main Methods:
- Utilized the MIMIC-IV database (v2.2) for critically ill CHD patients.
- Developed and compared six ML models (LR, DT, NB, RF, XGBoost, SVM) using 13 variables.
- Performed feature selection with LASSO regression and model evaluation via calibration and decision curve analysis, including external validation.
Main Results:
- The XGBoost model demonstrated superior performance in discrimination (AUROC=0.765) and accuracy (0.725).
- External validation confirmed the model's generalizability (AUROC=0.835).
- Key predictors identified include mechanical ventilation, antiplatelet agents, age, NT-proBNP, and APSIII.
Conclusions:
- Machine learning models are reliable for forecasting AKI in critically ill CHD patients.
- The XGBoost model offers high accuracy and can assist clinicians in identifying high-risk patients for early intervention, potentially lowering mortality.
Background:
Acute kidney injury (AKI) frequently occurs in critically ill patients with coronary heart disease (CHD), and its development markedly elevates mortality rates and prolongs hospitalization duration. Early AKI prediction is crucial for timely intervention and amelioration of patient outcomes.
Objective:
This study aimed to develop and verify a clinical prediction model for the occurrence of AKI upon admission in the critically ill population with CHD through machine learning (ML).
Methods:
Data from the MIMIC-IV (Medical Information Mart for Intensive Care IV) version 2.2 database were gathered and included information about critically ill individuals with CHD in the intensive care unit (ICU). The dataset was randomized into a training set (70%) and a testing set (30%). Least absolute shrinkage and selection operator (LASSO) regression was used for feature variable selection. ML models, including logistic regression (LR), decision tree (DT), naive Bayes (NB), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM), were constructed using 13 variables in the training set. The 6 models were compared in the testing set to identify the best-performing model. Subsequently, the model was assessed using calibration curve analysis and decision curve analysis (DCA). External validation was conducted using data from the Second Affiliated Hospital of Zhengzhou University. Ultimately, the predictive model was interpreted via Shapley Additive Explanation (SHAP) values.
Results:
In total, 2711 patients with CHD admitted to the ICU were selected, with 1809 (66.7%) having AKI. XGBoost exhibited the best performance regarding discrimination (area under the receiver operating characteristic curve [AUROC]=0.765, 95% CI 0.731-0.800), accuracy (0.725), and sensitivity (0.759). External validation using a cohort of 226 patients confirmed the strong generalizability of the XGBoost model (AUROC=0.835, 95% CI 0.782-0.887). Feature importance analyses derived from SHAP values, DT, RF, and XGBoost consistently identified 5 key predictors associated with the development of AKI: mechanical ventilation, use of antiplatelet agents, age, N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels, and acute physiology score III (APSIII).
Conclusions:
ML models can serve as reliable tools for forecasting AKI in the critically ill population with CHD. The XGBoost model is highly accurate and may aid doctors in identifying high-risk individuals for early intervention to lower mortality.
Related Concept Videos
01:22Acute Kidney Injury I: Introduction
01:29Acute Kidney Injury II: Pathophysiology
01:29Acute Kidney Injury III: Clinical Manifestations
01:30Acute Kidney Injury IV: Diagnostic Studies and Prevention
01:20Acute Kidney Injury V: Interprofessional Care
01:22Acute Kidney Injury VI: Nursing Management