Hou Ling, Su Ke, Zhao Jinbo, He Ting, Li Yuanhong
Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, Hubei Province, People's Republic of China.
Department of Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Shiyan, Hubei, People's Republic of China.
Risk Manag Healthc Policy. 2024 Nov 25;17:2907-2915. doi: 10.2147/RMHP.S488310. eCollection 2024.
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.
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.
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.
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.
冠心病（CHD）是全球主要的死亡原因之一，心房颤动（AF）是其常见并发症。慢性炎症反应在冠状动脉疾病与心房颤动的关系中起着重要作用。本研究旨在探讨多炎症指标（MII）在预测冠心病患者心房颤动发生中的价值。
对2020年1月1日至2023年12月31日来我院就诊的患者进行回顾性分析，共纳入1392例患者。收集临床资料和实验室检查结果。使用Boruta算法进行特征选择。构建了五个机器学习模型：逻辑回归、决策树、弹性网络、轻量级梯度提升机（LightGBM）和多层感知器。使用五折交叉验证评估模型性能。利用SHAP值分析特征重要性和模型可解释性。
该研究纳入了1302例无房颤患者和90例房颤患者。房颤患者的MII显著高于无房颤患者（10.02对4.79）。使用Boruta算法选择了13个与房颤发生最相关的变量。LightGBM模型表现优于其他模型，在训练集和测试集中均显示出最高的准确性和校准度。在训练集中，LightGBM的AUC为0.958，准确率为0.851，灵敏度为0.943，而在测试集中，其AUC为0.757，准确率为0.821。SHAP分析表明，年龄、心率和MII是房颤发生的主要预测因素。
LightGBM模型表现出足够的灵敏度和准确性。多炎症指标在预测冠心病患者心房颤动中起着关键作用。
