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预测冠心病患者心房颤动风险的列线图的开发。

Development of a Nomogram That Predicts the Risk of Atrial Fibrillation in Patients with Coronary Heart Disease.

作者信息

Cao Xinfu, Sun Yi, Chen Yuqiao, Tang Chao, Yu Hongwen, Li Xiaolong, Gu Zhenhua

机构信息

Department of Cardiology, Changzhou Affiliated Hospital of Nanjing University of Chinese Medicine, Changzhou, Jiangsu Province, People's Republic of China.

Department of Cardiology, The Second Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, People's Republic of China.

出版信息

Risk Manag Healthc Policy. 2024 Jul 9;17:1815-1826. doi: 10.2147/RMHP.S466205. eCollection 2024.

Abstract

OBJECTIVE

To explore the risk factors of atrial fibrillation (AF) in patients with coronary heart disease (CHD), and to construct a risk prediction model.

METHODS

The participants in this case-control study were from the cardiovascular Department of Changzhou Affiliated Hospital of Nanjing University of Chinese Medicine from June 2016 to June 2023, and they were divided into AF group and non-AF group according to whether AF occurred during hospitalization. The clinical data of the two groups were compared by retrospective analysis. Multivariate Logistic regression analysis was used to investigate the risk factors of AF occurrence in CHD patients. The nomogram model was constructed with R 4.2.6 language "rms" package, and the model's differentiation, calibration and effectiveness were evaluated by drawing ROC curve, calibration curve and decision curve.

RESULTS

A total of 1258 patients with CHD were included, and they were divided into AF group (n=92) and non-AF group (n=1166) according to whether AF was complicated. Logistic regression analysis showed that age, coronary multiple branch lesion, history of heart failure, history of drinking, pulmonary hypertension, left atrial diameter, left ventricular end-diastolic diameter and diabetes mellitus were independent risk factors for the occurrence of AF in CHD patients (P < 0.05). The ROC curve showed that the AUC of this model was 0.956 (95% CI (0.916, 0.995)) and the consistency index was 0.966. The calibration curve of the model is close to the ideal curve. The analysis of decision curve shows that the prediction value of the model is better when the probability threshold of the model is 0.042~0.963.

CONCLUSION

The nomogram model established in this study for predicting the risk of AF in patients with CHD has better predictive performance and has certain reference value for clinical identification of high-risk groups prone to AF in patients with CHD.

摘要

目的

探讨冠心病(CHD)患者发生心房颤动(AF)的危险因素,并构建风险预测模型。

方法

本病例对照研究的参与者来自2016年6月至2023年6月南京中医药大学附属常州医院心血管科,根据住院期间是否发生AF分为AF组和非AF组。通过回顾性分析比较两组的临床资料。采用多因素Logistic回归分析探讨CHD患者发生AF的危险因素。使用R 4.2.6语言的“rms”包构建列线图模型,并通过绘制ROC曲线、校准曲线和决策曲线评估模型的区分度、校准度和有效性。

结果

共纳入1258例CHD患者,根据是否合并AF分为AF组(n = 92)和非AF组(n = 1166)。Logistic回归分析显示,年龄、冠状动脉多支病变、心力衰竭史、饮酒史、肺动脉高压、左心房直径、左心室舒张末期直径和糖尿病是CHD患者发生AF的独立危险因素(P < 0.05)。ROC曲线显示,该模型的AUC为0.956(95%CI(0.916,0.995)),一致性指数为0.966。模型的校准曲线接近理想曲线。决策曲线分析显示,当模型概率阈值为0.042~0.963时,模型的预测价值较好。

结论

本研究建立的用于预测CHD患者AF风险的列线图模型具有较好的预测性能,对临床识别CHD患者中易发生AF的高危人群具有一定的参考价值。

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