早期心房颤动预测模型构建：基于中国人群窦性心律期间心电定量特征

doi:10.12122/j.issn.1673-4254.2025.02.02

南方医科大学学报 ›› 2025, Vol. 45 ›› Issue (2): 223-228.doi: 10.12122/j.issn.1673-4254.2025.02.02

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早期心房颤动预测模型构建：基于中国人群窦性心律期间心电定量特征

朱晓庆¹^,²(), 石亚君³, 沈娟¹, 王清松¹^,²^,⁴, 宋婷婷¹, 修建成⁵, 陈韬¹^,², 郭军¹^,²()

^1.中国人民解放军总医院第六医学中心心血管病医学部，北京 100048
^2.中国人民解放军医学院，北京 100853
^3.中国人民解放军总医院第一医学中心心血管内科，北京 100853
^4.中国人民解放军联勤保障部队第九八八医院心血管内科，河南郑州 450042
^5.南方医科大学南方医院心血管内科，广东广州 510515

收稿日期:2024-10-16 出版日期:2025-02-20 发布日期:2025-03-03
通讯作者: 郭军 E-mail:zxq990715@163.com;guojun301@126.com
作者简介:朱晓庆，在读硕士研究生，E-mail: zxq990715@163.com
基金资助:
国家重点研发计划(2018YFC2001205);全军临床重点专科建设项目;河南省医学科技攻关计划联合共建项目(LHGJ20230706)

An atrial fibrillation prediction model based on quantitative features of electrocardiogram during sinus rhythm in the Chinese population

Xiaoqing ZHU¹^,²(), Yajun SHI³, Juan SHEN¹, Qingsong WANG¹^,²^,⁴, Tingting SONG¹, Jiancheng XIU⁵, Tao CHEN¹^,², Jun GUO¹^,²()

^1.Senior Department of Cardiology, Sixth Medical Center of PLA General Hospital, Beijing 100048, China
^2.Medical School of Chinese PLA, Beijing 100853, China
^3.Department of Cardiology, First Medical Center of PLA General Hospital, Beijing 100853, China
^4.Department of Cardiology, No. 988 Hospital of PLA Joint Logistic Support Force, Zhengzhou 450042, China
^5.Southern Medical University, Guangzhou 510515, China

Received:2024-10-16 Online:2025-02-20 Published:2025-03-03
Contact: Jun GUO E-mail:zxq990715@163.com;guojun301@126.com

摘要/Abstract

摘要：

目的基于中国人群的心电大数据开发早期房颤风险预测模型。方法回顾性纳入2009年~2023年于解放军总医院有多次心电图检查记录的患者30 383例。患者按7∶3的比例随机划分为训练集和内部测试集。使用训练集数据，采用单因素分析、LASSO回归、Boruta算法筛选预测因子。基于Cox比例风险回归建立心电模型以及结合年龄、性别和心电模型评分的复合模型。采用受试者工作特征分析曲线下面积（AUROC）、校准曲线、决策曲线评估模型区分度、校准度及临床净获益。结果纳入患者的中位年龄为51（36，62）岁，男性占比51.1%，房颤的发生率为4.5%（1370/30 383）。在心电模型中，P波相关参数及QRS波相关参数是重要预测变量。在测试集中，心电模型预测5年房颤风险的AUROC为0.77（95% CI：0.74-0.80），加入年龄和性别后的复合模型AUROC提升至0.81（95% CI：0.78-0.83），净重新分类指数为0.123，综合判别改善指数为0.04（P<0.05）。模型校准曲线斜率接近对角线。决策曲线分析显示复合模型的临床净获益在绝大多数风险阈值范围内均高于心电模型。结论基于中国人群窦性心律期间的心电图定量特征及年龄和性别开发的复合模型可有效预测未来房颤风险，为房颤的早期风险评估及预防干预提供了低成本的筛查工具。

关键词: 心电图, 心房颤动, LASSO回归, Boruta算法

Abstract:

Objective To develop an early atrial fibrillation (AF) risk prediction model based on large-scale electrocardiogram (ECG) data from the Chinese population. Methods The data of multiple ECG records of 30 383 patients admitted in the Chinese PLA General Hospital between 2009 and 2023 were randomly divided into the training set and the internal testing set in a 7:3 ratio. The predictive factors were selected based on the training set using univariate analysis, LASSO regression, and the Boruta algorithm. Cox proportional hazards regression was used to establish the ECG model and the composite model incorporating age, gender, and ECG model score. The discrimination power, calibration, and clinical net benefits of the models were evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curves. Results The cohort included 51.1% male patients with a median age of the patients of 51 (36, 62) years and an AF incidence of 4.5% (1370/30 383). In the ECG model, the parameters related to the P wave and QRS complex were identified as significant predictors. In the testing set, the AUROC of the ECG model for predicting 5-year AF risk was 0.77 (95% CI: 0.74-0.80), which was increased to 0.81 (95% CI: 0.78-0.83) after incorporating age and gender, with a net reclassification improvement of 0.123 and an integrated discrimination improvement of 0.04 (P<0.05). The calibration curve of the model was close to the diagonal line. Decision curve analysis showed that the clinical net benefit of the composite model was higher than that of the ECG model across the majority of threshold probability. Conclusion The composite model incorporating quantitative ECG features during sinus rhythm, along with age and gender, can effectively predict AF risk in the Chinese population, thus providing a low-cost screening tool for early AF risk assessment and management.

Key words: electrocardiogram, atrial fibrillation, LASSO regression, Boruta algorithm

朱晓庆, 石亚君, 沈娟, 王清松, 宋婷婷, 修建成, 陈韬, 郭军. 早期心房颤动预测模型构建：基于中国人群窦性心律期间心电定量特征[J]. 南方医科大学学报, 2025, 45(2): 223-228.

Xiaoqing ZHU, Yajun SHI, Juan SHEN, Qingsong WANG, Tingting SONG, Jiancheng XIU, Tao CHEN, Jun GUO. An atrial fibrillation prediction model based on quantitative features of electrocardiogram during sinus rhythm in the Chinese population[J]. Journal of Southern Medical University, 2025, 45(2): 223-228.

图/表 6

表1 房颤组与非房颤组基线对比

Tab.1 Comparison of baseline characteristics between non-AF group and AF group


Item	Non-AF group (n=29 013)	AF group (n=1370)	P
Age (year)	50.0 (35.0, 61.0)	70.0 (60.0, 78.0)	<0.001
Gender (male)	14648 (50.5)	868 (63.4)	<0.001

表2 训练集与测试集基线对比

Tab.2 Comparison of baseline characteristics between the training set and test set


Item	Training set (n=21268)	Test set (n=9115)	P
Age (years)	51.0 (36.0, 62.0)	51.0 (35.0, 62.0)	0.603
Gender (male)	10895 (51.2)	4621 (50.7)	0.397
Incidence of AF	956 (4.5%)	414 (4.5%)	0.857

图1 心电模型中包含的变量对房颤预测的相对重要性

Fig.1 Relative importance of the variables included in the ECG model for predicting atrial fibrillation measured by chi-squared statistic minus predictor degrees of freedom (df).

图2 心电模型及复合模型预测5年房颤风险的ROC曲线

Fig.2 ROC curves of the ECG model and the composite model for predicting 5-year atrial fibrillation risk. A: ROC curves in the training set. B: ROC curves in the test set.

图3 心电模型及复合模型预测5年房颤风险的校准度

Fig.3 Calibration of the ECG model and the composite model for predicting 5-year atrial fibrillation risk. A: Calibration curves in the training set. B: Calibration curves in the test set.

图4 心电模型及复合模型预测5年房颤风险的决策曲线

Fig.4 Decision curves of the ECG model and the composite model for predicting 5-year atrial fibrillation risk. A: Decision curves in the training set. B: Decision curves in in the test set.

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早期心房颤动预测模型构建：基于中国人群窦性心律期间心电定量特征

An atrial fibrillation prediction model based on quantitative features of electrocardiogram during sinus rhythm in the Chinese population

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