Application of Multi-Inflammatory Index to Predict Atrial Fibrillation Risk in Patients with Coronary Heart Disease: A Retrospective Machine Learning Study.

IF 2.7 4区医学 Q2 HEALTH CARE SCIENCES & SERVICES

Risk Management and Healthcare Policy Pub Date : 2024-11-25 eCollection Date: 2024-01-01 DOI:10.2147/RMHP.S488310

Ling Hou, Ke Su, Jinbo Zhao, Ting He, Yuanhong Li

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引用次数: 0

Abstract

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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应用多重炎症指标预测冠心病患者房颤风险：一项回顾性机器学习研究

背景：冠心病（CHD）是世界范围内死亡的主要原因，房颤（AF）是一种常见的并发症。慢性炎症反应在冠状动脉疾病与房颤的关系中起着重要作用。本研究旨在探讨多重炎症指数（multi-inflammatory index， MII）对冠心病患者房颤发生的预测价值。方法：回顾性分析2020年1月1日至2023年12月31日在我院就诊的患者，共1392例。收集临床资料和实验室结果。采用Boruta算法进行特征选择。构建了逻辑回归、决策树、弹性网络、光梯度增强机和多层感知机五个机器学习模型。采用五重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。结果：该研究包括1302例无房颤患者和90例房颤患者。房颤患者的MII明显高于无房颤患者（10.02 vs 4.79）。采用Boruta算法选择与AF发生最相关的13个变量。LightGBM模型优于其他模型，在训练集和测试集上都显示出最高的准确性和校准。在训练集中，LightGBM的AUC为0.958，准确率为0.851，灵敏度为0.943，在测试集中，AUC为0.757，准确率为0.821。SHAP分析显示，年龄、心率和MII是房颤发生的主要预测因素。结论：LightGBM模型具有良好的灵敏度和准确性。多重炎症指标在预测冠心病患者房颤中起着至关重要的作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Risk Management and Healthcare Policy Medicine-Public Health, Environmental and Occupational Health

CiteScore

6.20

自引率

2.90%

发文量

242

审稿时长

16 weeks

期刊介绍： Risk Management and Healthcare Policy is an international, peer-reviewed, open access journal focusing on all aspects of public health, policy and preventative measures to promote good health and improve morbidity and mortality in the population. Specific topics covered in the journal include: Public and community health Policy and law Preventative and predictive healthcare Risk and hazard management Epidemiology, detection and screening Lifestyle and diet modification Vaccination and disease transmission/modification programs Health and safety and occupational health Healthcare services provision Health literacy and education Advertising and promotion of health issues Health economic evaluations and resource management Risk Management and Healthcare Policy focuses on human interventional and observational research. The journal welcomes submitted papers covering original research, clinical and epidemiological studies, reviews and evaluations, guidelines, expert opinion and commentary, and extended reports. Case reports will only be considered if they make a valuable and original contribution to the literature. The journal does not accept study protocols, animal-based or cell line-based studies.