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Application of Multi-Inflammatory Index to Predict Atrial Fibrillation Risk in Patients with Coronary Heart Disease:
Ling Hou1,2, Ke Su1, Jinbo Zhao1
1Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, Hubei Province, People's Republic of China.
Insights
The multi-inflammatory index (MII) can predict atrial fibrillation (AF) in coronary heart disease (CHD) patients. A machine learning model using MII showed high accuracy in identifying patients at risk for AF.
Area of Science:
- Cardiology
- Biomarkers
- Machine Learning in Medicine
Background:
- Coronary heart disease (CHD) is a major global cause of death.
- Atrial fibrillation (AF) is a frequent complication of CHD.
- Chronic inflammation is implicated in the link between CHD and AF.
Purpose of the Study:
- To evaluate the predictive value of the multi-inflammatory index (MII) for AF occurrence in CHD patients.
- To develop and assess machine learning models for AF prediction in this population.
Main Methods:
- Retrospective analysis of 1392 patients with CHD.
- Feature selection using the Boruta algorithm.
- Development and validation of five machine learning models (Logistic Regression, Decision Tree, Elastic Net, LightGBM, MLP) using five-fold cross-validation.
- SHAP values for feature importance and model interpretability.
Main Results:
- Patients with AF (n=90) had significantly higher MII than those without AF (n=1302) (10.02 vs 4.79).
- The LightGBM model demonstrated superior performance, achieving high accuracy and AUC in both training and testing sets.
- SHAP analysis identified age, heart rate, and MII as key predictors of AF.
Conclusions:
- The multi-inflammatory index (MII) is a crucial predictor of atrial fibrillation in patients with coronary heart disease.
- The developed LightGBM model shows promising sensitivity and accuracy for AF risk stratification.
Background:
Coronary heart disease (CHD) is a leading cause of mortality worldwide, with atrial fibrillation (AF) being a common complication. Chronic inflammatory responses play a significant role in the relationship between coronary artery disease and AF. This study aims to investigate the value of the multi-inflammatory index (MII) in predicting the occurrence of atrial fibrillation in patients with coronary heart disease.
Methods:
A retrospective analysis was conducted on patients who visited our hospital from January 1, 2020, to December 31, 2023, including a total of 1392 patients. Clinical data and laboratory results were collected. Feature selection was performed using the Boruta algorithm. Five machine learning models were constructed: Logistic Regression, Decision Tree, Elastic Net, Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron. Model performance was evaluated using five-fold cross-validation. SHAP values were utilized to analyze feature importance and model interpretability.
Results:
The study included 1302 patients without AF and 90 patients with AF. Patients with AF had significantly higher MII compared to those without AF (10.02 vs 4.79). Thirteen variables most related to AF occurrence were selected using the Boruta algorithm. The LightGBM model outperformed others, showing the highest accuracy and calibration in both training and test sets. In the training set, LightGBM achieved an AUC of 0.958, accuracy of 0.851, and sensitivity of 0.943, while in the testing set, it achieved an AUC of 0.757 and accuracy of 0.821. SHAP analysis indicated that age, heart rate, and MII were the primary predictors of AF occurrence.
Conclusion:
The LightGBM model demonstrated adequate sensitivity and accuracy. The multi-inflammatory index plays a crucial role in predicting atrial fibrillation in patients with coronary heart disease.
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