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The Predictive Value of the Pan-Immune-Inflammation Value for Atrial Fibrillation Risk in Patients with Coronary
Ke He1, Jinbo Zhao2, Changjiang Zhang1
1Department of Cardiology, Minda Hospital of Hubei Minzu University, Enshi, Hubei, People's Republic of China.
Insights
The pan-immune-inflammation value (PIV) effectively predicts atrial fibrillation (AF) in coronary heart disease (CHD) patients. Machine learning models, particularly XGBoost, show promise for assessing individual AF risk in this population.
Area of Science:
- Cardiology
- Inflammation Research
- Medical Informatics
Background:
- Atrial fibrillation (AF) is a common arrhythmia in patients with coronary heart disease (CHD).
- Inflammatory response is a key factor in AF pathogenesis.
- The predictive value of pan-immune-inflammation value (PIV) for AF in CHD patients requires further investigation.
Purpose of the Study:
- To investigate the predictive value of PIV for AF in patients with CHD.
- To compare the performance of machine learning models (XGBoost and MLP) in predicting AF risk.
- To identify key predictors of AF in this patient cohort.
Main Methods:
- A multicenter retrospective study involving patients diagnosed with CHD.
- Collected clinical characteristics and laboratory data, including PIV.
- Utilized logistic regression for feature selection and developed XGBoost and MLP models for AF prediction.
- Evaluated model performance using AUC and calibration, with SHAP values for interpretability.
Main Results:
- Patients with AF exhibited significantly higher PIV, age, AST, WBC, and TBIL levels compared to the non-AF group.
- PIV, age, and diabetes were identified as independent predictors of AF.
- The XGBoost model demonstrated superior predictive performance (AUC = 0.73 in testing) over the MLP model (AUC = 0.69).
- SHAP analysis highlighted PIV as the most influential predictor of AF risk.
Conclusions:
- PIV is a valuable predictor of AF in patients with CHD.
- The XGBoost model offers a robust tool for individualized AF risk assessment in this population.
- Machine learning approaches can enhance the prediction of AF in CHD patients.
Background:
Atrial fibrillation (AF) is a common arrhythmia among patients with coronary heart disease (CHD), and inflammatory response plays a key role in its pathogenesis. The pan-immune-inflammation value (PIV), a novel composite marker reflecting systemic inflammation, has not been fully investigated for its predictive value in AF among CHD patients.
Methods:
This multicenter retrospective study enrolled patients diagnosed with CHD by coronary angiography from two tertiary hospitals. Participants were categorized into AF and non-AF groups. Clinical characteristics and laboratory data were collected. Feature selection was performed using multivariate logistic regression, and significant predictors were incorporated into two models: extreme gradient boosting (XGBoost) and multilayer perceptron (MLP). Model performance was evaluated by area under the ROC curve (AUC) and calibration analysis. Model interpretability was assessed using SHAP (SHapley Additive exPlanations) values, and partial dependence plots (PDPs) were applied to explore variable interactions.
Results:
Compared with the non-AF group, the AF group had significantly higher levels of PIV, age, AST, WBC, and TBIL. Logistic regression identified PIV, age, and diabetes as independent predictors of AF, while sex, left main coronary artery disease (LM), and AST showed borderline significance. The XGBoost model achieved superior performance (AUC = 0.79 in training and 0.73 in testing) compared to the MLP model (AUC = 0.75 and 0.69, respectively), with better calibration consistency. SHAP analysis indicated that PIV was the most influential feature, with higher values associated with increased AF risk. PDPs further demonstrated synergistic effects between PIV and other key variables.
Conclusion:
PIV is a valuable predictor of AF in CHD patients. The XGBoost model outperformed the deep learning model in this context and may serve as a robust tool for individualized AF risk assessment.
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