Machine learning in risk prediction of continuous renal replacement therapy after coronary artery bypass grafting
Qian Zhang1, Peng Zheng1, Zhou Hong2
1Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Insights
Machine learning models predict continuous renal replacement therapy (CRRT) risk after coronary artery bypass grafting (CABG) surgery. The Gaussian Naïve Bayes model identified key predictors, aiding in optimizing patient care.
Area of Science:
- Medical Informatics
- Cardiovascular Surgery
- Nephrology
Background:
- Continuous renal replacement therapy (CRRT) is a critical intervention for intensive care unit (ICU) patients post-coronary artery bypass grafting (CABG).
- Predicting the need for CRRT after CABG is essential for timely intervention and improved patient outcomes.
- Existing risk prediction methods may not fully capture the complexity of CRRT requirement post-CABG.
Purpose of the Study:
- To develop and validate machine learning models for predicting CRRT risk in ICU patients undergoing CABG surgery.
- To identify significant risk factors associated with CRRT requirement post-CABG.
- To enhance perioperative management strategies through accurate risk stratification.
Main Methods:
- Utilized electronic medical record data from CABG patients.
- Employed the Boruta method for feature selection and developed seven machine learning algorithms.
- Validated models using 10-fold cross-validation, assessing discrimination with AUC and calibration plots.
- Applied SHapley Additive exPlanations (SHAP) for feature interpretability.
Main Results:
- 72 (37.89%) patients required CRRT post-CABG, exhibiting higher mortality.
- The Gaussian Naïve Bayes (GNB) model demonstrated the highest predictive performance (AUC).
- Key predictors included cardiac troponin T, creatine kinase isoenzyme, albumin, LDL cholesterol, NYHA class, serum creatinine, and age.
- SHAP analysis provided individualized risk predictions.
Conclusions:
- Machine learning models effectively predict CRRT risk in the post-CABG ICU population.
- Identified crucial risk variables for CRRT, facilitating early intervention.
- The developed models can aid in optimizing perioperative care for high-risk patients.
Objectives:
This study aimed to develop machine learning models for risk prediction of continuous renal replacement therapy (CRRT) following coronary artery bypass grafting (CABG) surgery in intensive care unit (ICU) patients.
Methods:
We extracted CABG patients from the electronic medical record system of the hospital. The endpoint of this study was the requirement for CRRT after CABG surgery. The Boruta method was used for feature selection. Seven machine learning algorithms were developed to train models and validated using 10 fold cross-validation (CV). Model discrimination and calibration were estimated using the area under the receiver operating characteristic curve (AUC) and calibration plot, respectively. We used the SHapley Additive exPlanations (SHAP) method to illustrate the effects of the features attributed to the model and analyze the effects of individual features on the output of the mode.
Results:
In this study, 72 (37.89%) patients underwent CRRT, with a higher mortality compared to those patients without CRRT. The Gaussian Naïve Bayes (GNB) model with the highest AUC were considered as the final predictive model and performed best in predicting postoperative CRRT. The analysis of importance revealed that cardiac troponin T, creatine kinase isoenzyme, albumin, low-density lipoprotein cholesterol, NYHA, serum creatinine, and age were the top seven features of the GNB model. The SHAP force analysis illustrated how created model visualized individualized prediction of CRRT.
Conclusions:
Machine learning models were developed to predict CRRT. This contributes to the identification of risk variables for CRRT following CABG surgery in ICU patients and enables the optimization of perioperative managements for patients.
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