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已发表论文
泛免疫炎症值预测冠心病患者心房颤动风险的价值：一项多中心机器学习研究
Received 4 November 2025
Accepted for publication 22 January 2026
Published 29 January 2026 Volume 2026:19 579135
DOI https://doi.org/10.2147/IJGM.S579135
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Redoy Ranjan
Ke He,1 Jinbo Zhao,2 Changjiang Zhang1
1Department of Cardiology, Minda Hospital of Hubei Minzu University, Enshi, Hubei, People’s Republic of China; 2Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi, Hubei, People’s Republic of China
Correspondence: Changjiang Zhang, Email zcj2008@163.com
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.
Keywords: atrial fibrillation, coronary heart disease, pan-immune-inflammation value, XGBoost, deep learning
1Department of Cardiology, Minda Hospital of Hubei Minzu University, Enshi, Hubei, People’s Republic of China; 2Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi, Hubei, People’s Republic of China
Correspondence: Changjiang Zhang, Email zcj2008@163.com
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.
Keywords: atrial fibrillation, coronary heart disease, pan-immune-inflammation value, XGBoost, deep learning